[38;5;12m                                                                                                 [39m[38;2;255;187;0m[1m[4mAwesome - Most Cited Deep Learning Papers[0m

[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome)[39m

[38;5;14m[1mNotice[0m[38;5;12m  This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017.[39m

[38;5;12mA curated list of the most cited deep learning papers (2012-2016)[39m

[38;5;12mWe[39m[38;5;12m [39m[38;5;12mbelieve[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mthere[39m[38;5;12m [39m[38;5;12mexist[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3mclassic[0m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwhich[39m[38;5;12m [39m[38;5;12mare[39m[38;5;12m [39m[38;5;12mworth[39m[38;5;12m [39m[38;5;12mreading[39m[38;5;12m [39m[38;5;12mregardless[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mapplication[39m[38;5;12m [39m[38;5;12mdomain.[39m[38;5;12m [39m[38;5;12mRather[39m[38;5;12m [39m[38;5;12mthan[39m[38;5;12m [39m[38;5;12mproviding[39m[38;5;12m [39m[38;5;12moverwhelming[39m[38;5;12m [39m[38;5;12mamount[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mpapers,[39m[38;5;12m [39m[38;5;12mWe[39m[38;5;12m [39m[38;5;12mwould[39m[38;5;12m [39m[38;5;12mlike[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mprovide[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3mcurated[0m[48;2;30;30;40m[38;5;13m[3m [0m[48;2;30;30;40m[38;5;13m[3mlist[0m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mawesome[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m
[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwhich[39m[38;5;12m [39m[38;5;12mare[39m[38;5;12m [39m[38;5;12mconsidered[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3mmust-reads[0m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mcertain[39m[38;5;12m [39m[38;5;12mresearch[39m[38;5;12m [39m[38;5;12mdomains.[39m

[38;2;255;187;0m[4mBackground[0m

[38;5;12mBefore[39m[38;5;12m [39m[38;5;12mthis[39m[38;5;12m [39m[38;5;12mlist,[39m[38;5;12m [39m[38;5;12mthere[39m[38;5;12m [39m[38;5;12mexist[39m[38;5;12m [39m[38;5;12mother[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3mawesome[0m[48;2;30;30;40m[38;5;13m[3m [0m[48;2;30;30;40m[38;5;13m[3mdeep[0m[48;2;30;30;40m[38;5;13m[3m [0m[48;2;30;30;40m[38;5;13m[3mlearning[0m[48;2;30;30;40m[38;5;13m[3m [0m[48;2;30;30;40m[38;5;13m[3mlists[0m[38;5;12m,[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mexample,[39m[38;5;12m [39m[38;5;14m[1mDeep[0m[38;5;14m[1m [0m[38;5;14m[1mVision[0m[38;5;12m [39m[38;5;12m(https://github.com/kjw0612/awesome-deep-vision)[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;14m[1mAwesome[0m[38;5;14m[1m [0m[38;5;14m[1mRecurrent[0m[38;5;14m[1m [0m[38;5;14m[1mNeural[0m[38;5;14m[1m [0m[38;5;14m[1mNetworks[0m[38;5;12m [39m[38;5;12m(https://github.com/kjw0612/awesome-rnn).[39m[38;5;12m [39m[38;5;12mAlso,[39m[38;5;12m [39m[38;5;12mafter[39m[38;5;12m [39m[38;5;12mthis[39m[38;5;12m [39m
[38;5;12mlist[39m[38;5;12m [39m[38;5;12mcomes[39m[38;5;12m [39m[38;5;12mout,[39m[38;5;12m [39m[38;5;12manother[39m[38;5;12m [39m[38;5;12mawesome[39m[38;5;12m [39m[38;5;12mlist[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mbeginners,[39m[38;5;12m [39m[38;5;12mcalled[39m[38;5;12m [39m[38;5;14m[1mDeep[0m[38;5;14m[1m [0m[38;5;14m[1mLearning[0m[38;5;14m[1m [0m[38;5;14m[1mPapers[0m[38;5;14m[1m [0m[38;5;14m[1mReading[0m[38;5;14m[1m [0m[38;5;14m[1mRoadmap[0m[38;5;12m [39m[38;5;12m(https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap),[39m[38;5;12m [39m[38;5;12mhas[39m[38;5;12m [39m[38;5;12mbeen[39m[38;5;12m [39m[38;5;12mcreated[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mloved[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12mmany[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m
[38;5;12mresearchers.[39m

[38;5;12mAlthough[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3mRoadmap[0m[48;2;30;30;40m[38;5;13m[3m [0m[48;2;30;30;40m[38;5;13m[3mList[0m[38;5;12m [39m[38;5;12mincludes[39m[38;5;12m [39m[38;5;12mlots[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mimportant[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpapers,[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12mfeels[39m[38;5;12m [39m[38;5;12moverwhelming[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mme[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mread[39m[38;5;12m [39m[38;5;12mthem[39m[38;5;12m [39m[38;5;12mall.[39m[38;5;12m [39m[38;5;12mAs[39m[38;5;12m [39m[38;5;12mI[39m[38;5;12m [39m[38;5;12mmentioned[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mintroduction,[39m[38;5;12m [39m[38;5;12mI[39m[38;5;12m [39m[38;5;12mbelieve[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mseminal[39m[38;5;12m [39m[38;5;12mworks[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mgive[39m[38;5;12m [39m[38;5;12mus[39m[38;5;12m [39m[38;5;12mlessons[39m[38;5;12m [39m[38;5;12mregardless[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m
[38;5;12mapplication[39m[38;5;12m [39m[38;5;12mdomain.[39m[38;5;12m [39m[38;5;12mThus,[39m[38;5;12m [39m[38;5;12mI[39m[38;5;12m [39m[38;5;12mwould[39m[38;5;12m [39m[38;5;12mlike[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mintroduce[39m[38;5;12m [39m[38;5;14m[1mtop[0m[38;5;14m[1m [0m[38;5;14m[1m100[0m[38;5;14m[1m [0m[38;5;14m[1mdeep[0m[38;5;14m[1m [0m[38;5;14m[1mlearning[0m[38;5;14m[1m [0m[38;5;14m[1mpapers[0m[38;5;12m [39m[38;5;12mhere[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mgood[39m[38;5;12m [39m[38;5;12mstarting[39m[38;5;12m [39m[38;5;12mpoint[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12moverviewing[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mresearches.[39m

[38;5;12mTo get the news for newly released papers everyday, follow my [39m[38;5;14m[1mtwitter[0m[38;5;12m (https://twitter.com/TerryUm_ML) or [39m[38;5;14m[1mfacebook page[0m[38;5;12m (https://www.facebook.com/terryum.io/)! [39m

[38;2;255;187;0m[4mAwesome list criteria[0m

[38;5;12m1. A list of [39m[38;5;14m[1mtop 100 deep learning papers[0m[38;5;12m published from 2012 to 2016 is suggested.[39m
[38;5;12m2. If a paper is added to the list, another paper (usually from [39m[48;2;30;30;40m[38;5;13m[3mMore Papers from 2016" section) should be removed to keep top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)[0m
[38;5;12m3. Papers that are important, but failed to be included in the list, will be listed in [39m[48;2;30;30;40m[38;5;13m[3mMore than Top 100[0m[38;5;12m section.[39m
[38;5;12m4. Please refer to [39m[48;2;30;30;40m[38;5;13m[3mNew Papers[0m[38;5;12m and [39m[48;2;30;30;40m[38;5;13m[3mOld Papers[0m[38;5;12m sections for the papers published in recent 6 months or before 2012.[39m

[48;2;30;30;40m[38;5;13m[3m(Citation criteria)[0m
[38;5;12m- [39m[38;5;14m[1m< 6 months[0m[38;5;12m : [39m[48;2;30;30;40m[38;5;13m[3mNew Papers[0m[38;5;12m (by discussion)[39m
[38;5;12m- [39m[38;5;14m[1m2016[0m[38;5;12m :  +60 citations or "More Papers from 2016"[39m
[38;5;12m- [39m[38;5;14m[1m2015[0m[38;5;12m :  +200 citations[39m
[38;5;12m- [39m[38;5;14m[1m2014[0m[38;5;12m :  +400 citations[39m
[38;5;12m- [39m[38;5;14m[1m2013[0m[38;5;12m :  +600 citations[39m
[38;5;12m- [39m[38;5;14m[1m2012[0m[38;5;12m :  +800 citations[39m
[38;5;12m- [39m[38;5;14m[1m~2012[0m[38;5;12m : [39m[48;2;30;30;40m[38;5;13m[3mOld Papers[0m[38;5;12m (by discussion)[39m

[38;5;12mPlease[39m[38;5;12m [39m[38;5;12mnote[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mwe[39m[38;5;12m [39m[38;5;12mprefer[39m[38;5;12m [39m[38;5;12mseminal[39m[38;5;12m [39m[38;5;12mdeep[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mbe[39m[38;5;12m [39m[38;5;12mapplied[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mvarious[39m[38;5;12m [39m[38;5;12mresearches[39m[38;5;12m [39m[38;5;12mrather[39m[38;5;12m [39m[38;5;12mthan[39m[38;5;12m [39m[38;5;12mapplication[39m[38;5;12m [39m[38;5;12mpapers.[39m[38;5;12m [39m[38;5;12mFor[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mreason,[39m[38;5;12m [39m[38;5;12msome[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mmeet[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mcriteria[39m[38;5;12m [39m[38;5;12mmay[39m[38;5;12m [39m[38;5;12mnot[39m[38;5;12m [39m[38;5;12mbe[39m[38;5;12m [39m[38;5;12maccepted[39m[38;5;12m [39m[38;5;12mwhile[39m[38;5;12m [39m[38;5;12mothers[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mbe.[39m[38;5;12m [39m[38;5;12mIt[39m[38;5;12m [39m[38;5;12mdepends[39m[38;5;12m [39m[38;5;12mon[39m
[38;5;12mthe[39m[38;5;12m [39m[38;5;12mimpact[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mpaper,[39m[38;5;12m [39m[38;5;12mapplicability[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mother[39m[38;5;12m [39m[38;5;12mresearches[39m[38;5;12m [39m[38;5;12mscarcity[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mresearch[39m[38;5;12m [39m[38;5;12mdomain,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mso[39m[38;5;12m [39m[38;5;12mon.[39m

[38;5;14m[1mWe need your contributions![0m

[38;5;12mIf you have any suggestions (missing papers, new papers, key researchers or typos), please feel free to edit and pull a request.[39m
[38;5;12m(Please read the [39m[38;5;14m[1mcontributing guide[0m[38;5;12m (https://github.com/terryum/awesome-deep-learning-papers/blob/master/Contributing.md) for further instructions, though just letting me know the title of papers can also be a big contribution to us.)[39m

[38;5;12m(Update)[39m[38;5;12m [39m[38;5;12mYou[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mdownload[39m[38;5;12m [39m[38;5;12mall[39m[38;5;12m [39m[38;5;12mtop-100[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;14m[1mthis[0m[38;5;12m [39m[38;5;12m(https://github.com/terryum/awesome-deep-learning-papers/blob/master/fetch_papers.py)[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mcollect[39m[38;5;12m [39m[38;5;12mall[39m[38;5;12m [39m[38;5;12mauthors'[39m[38;5;12m [39m[38;5;12mnames[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;14m[1mthis[0m[38;5;12m [39m
[38;5;12m(https://github.com/terryum/awesome-deep-learning-papers/blob/master/get_authors.py).[39m[38;5;12m [39m[38;5;12mAlso,[39m[38;5;12m [39m[38;5;14m[1mbib[0m[38;5;14m[1m [0m[38;5;14m[1mfile[0m[38;5;12m [39m[38;5;12m(https://github.com/terryum/awesome-deep-learning-papers/blob/master/top100papers.bib)[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mall[39m[38;5;12m [39m[38;5;12mtop-100[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mare[39m[38;5;12m [39m[38;5;12mavailable.[39m[38;5;12m [39m[38;5;12mThanks,[39m[38;5;12m [39m
[38;5;12mdoodhwala,[39m[38;5;12m [39m[38;5;14m[1mSven[0m[38;5;12m [39m[38;5;12m(https://github.com/sunshinemyson)[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;14m[1mgrepinsight[0m[38;5;12m [39m[38;5;12m(https://github.com/grepinsight)![39m

[38;5;12m+ Can anyone contribute the code for obtaining the statistics of the authors of Top-100 papers?[39m


[38;2;255;187;0m[4mContents[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUnderstanding / Generalization / Transfer[0m[38;5;12m (#understanding--generalization--transfer)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptimization / Training Techniques[0m[38;5;12m (#optimization--training-techniques)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUnsupervised / Generative Models[0m[38;5;12m (#unsupervised--generative-models)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvolutional Network Models[0m[38;5;12m (#convolutional-neural-network-models)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImage Segmentation / Object Detection[0m[38;5;12m (#image-segmentation--object-detection)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImage / Video / Etc[0m[38;5;12m (#image--video--etc)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNatural Language Processing / RNNs[0m[38;5;12m (#natural-language-processing--rnns)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpeech / Other Domain[0m[38;5;12m (#speech--other-domain)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReinforcement Learning / Robotics[0m[38;5;12m (#reinforcement-learning--robotics)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMore Papers from 2016[0m[38;5;12m (#more-papers-from-2016)[39m

[48;2;30;30;40m[38;5;13m[3m(More than Top 100)[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNew Papers[0m[38;5;12m (#new-papers) : Less than 6 months[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOld Papers[0m[38;5;12m (#old-papers) : Before 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHW / SW / Dataset[0m[38;5;12m (#hw--sw--dataset) : Technical reports[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBook / Survey / Review[0m[38;5;12m (#book--survey--review)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVideo Lectures / Tutorials / Blogs[0m[38;5;12m (#video-lectures--tutorials--blogs)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAppendix: More than Top 100[0m[38;5;12m (#appendix-more-than-top-100) : More papers not in the list[39m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3m [0m

[38;2;255;187;0m[4mUnderstanding / Generalization / Transfer[0m
[38;5;12m- [39m[38;5;14m[1mDistilling the knowledge in a neural network[0m[38;5;12m (2015), G. Hinton et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1503.02531)[39m
[38;5;12m- [39m[38;5;14m[1mDeep neural networks are easily fooled: High confidence predictions for unrecognizable images[0m[38;5;12m (2015), A. Nguyen et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1412.1897)[39m
[38;5;12m- [39m[38;5;14m[1mHow transferable are features in deep neural networks?[0m[38;5;12m (2014), J. Yosinski et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf)[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mCNN[0m[38;5;14m[1m [0m[38;5;14m[1mfeatures[0m[38;5;14m[1m [0m[38;5;14m[1moff-the-Shelf:[0m[38;5;14m[1m [0m[38;5;14m[1mAn[0m[38;5;14m[1m [0m[38;5;14m[1mastounding[0m[38;5;14m[1m [0m[38;5;14m[1mbaseline[0m[38;5;14m[1m [0m[38;5;14m[1mfor[0m[38;5;14m[1m [0m[38;5;14m[1mrecognition[0m[38;5;12m [39m[38;5;12m(2014),[39m[38;5;12m [39m[38;5;12mA.[39m[38;5;12m [39m[38;5;12mRazavian[39m[38;5;12m [39m[38;5;12met[39m[38;5;12m [39m[38;5;12mal.[39m[38;5;12m [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://www.cv-foundation.org//openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf)[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mLearning[0m[38;5;14m[1m [0m[38;5;14m[1mand[0m[38;5;14m[1m [0m[38;5;14m[1mtransferring[0m[38;5;14m[1m [0m[38;5;14m[1mmid-Level[0m[38;5;14m[1m [0m[38;5;14m[1mimage[0m[38;5;14m[1m [0m[38;5;14m[1mrepresentations[0m[38;5;14m[1m [0m[38;5;14m[1musing[0m[38;5;14m[1m [0m[38;5;14m[1mconvolutional[0m[38;5;14m[1m [0m[38;5;14m[1mneural[0m[38;5;14m[1m [0m[38;5;14m[1mnetworks[0m[38;5;12m [39m[38;5;12m(2014),[39m[38;5;12m [39m[38;5;12mM.[39m[38;5;12m [39m[38;5;12mOquab[39m[38;5;12m [39m[38;5;12met[39m[38;5;12m [39m[38;5;12mal.[39m[38;5;12m [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mVisualizing and understanding convolutional networks[0m[38;5;12m (2014), M. Zeiler and R. Fergus [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1311.2901)[39m
[38;5;12m- [39m[38;5;14m[1mDecaf: A deep convolutional activation feature for generic visual recognition[0m[38;5;12m (2014), J. Donahue et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1310.1531)[39m



[38;2;255;187;0m[4mOptimization / Training Techniques[0m
[38;5;12m- [39m[38;5;14m[1mTraining very deep networks[0m[38;5;12m (2015), R. Srivastava et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mBatch normalization: Accelerating deep network training by reducing internal covariate shift[0m[38;5;12m (2015), S. Loffe and C. Szegedy [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1502.03167)[39m
[38;5;12m- [39m[38;5;14m[1mDelving deep into rectifiers: Surpassing human-level performance on imagenet classification[0m[38;5;12m (2015), K. He et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDropout: A simple way to prevent neural networks from overfitting[0m[38;5;12m (2014), N. Srivastava et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mAdam: A method for stochastic optimization[0m[38;5;12m (2014), D. Kingma and J. Ba [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1412.6980)[39m
[38;5;12m- [39m[38;5;14m[1mImproving neural networks by preventing co-adaptation of feature detectors[0m[38;5;12m (2012), G. Hinton et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1207.0580.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mRandom search for hyper-parameter optimization[0m[38;5;12m (2012) J. Bergstra and Y. Bengio [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a)[39m



[38;2;255;187;0m[4mUnsupervised / Generative Models[0m
[38;5;12m- [39m[38;5;14m[1mPixel recurrent neural networks[0m[38;5;12m (2016), A. Oord et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1601.06759v2.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mImproved techniques for training GANs[0m[38;5;12m (2016), T. Salimans et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mUnsupervised representation learning with deep convolutional generative adversarial networks[0m[38;5;12m (2015), A. Radford et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1511.06434v2)[39m
[38;5;12m- [39m[38;5;14m[1mDRAW: A recurrent neural network for image generation[0m[38;5;12m (2015), K. Gregor et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1502.04623)[39m
[38;5;12m- [39m[38;5;14m[1mGenerative adversarial nets[0m[38;5;12m (2014), I. Goodfellow et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mAuto-encoding variational Bayes[0m[38;5;12m (2013), D. Kingma and M. Welling [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1312.6114)[39m
[38;5;12m- [39m[38;5;14m[1mBuilding high-level features using large scale unsupervised learning[0m[38;5;12m (2013), Q. Le et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1112.6209)[39m


[38;2;255;187;0m[4mConvolutional Neural Network Models[0m
[38;5;12m- [39m[38;5;14m[1mRethinking the inception architecture for computer vision[0m[38;5;12m (2016), C. Szegedy et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mInception-v4, inception-resnet and the impact of residual connections on learning[0m[38;5;12m (2016), C. Szegedy et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1602.07261)[39m
[38;5;12m- [39m[38;5;14m[1mIdentity Mappings in Deep Residual Networks[0m[38;5;12m (2016), K. He et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.05027v2.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDeep residual learning for image recognition[0m[38;5;12m (2016), K. He et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1512.03385)[39m
[38;5;12m- [39m[38;5;14m[1mSpatial transformer network[0m[38;5;12m (2015), M. Jaderberg et al., [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mGoing deeper with convolutions[0m[38;5;12m (2015), C. Szegedy et al.  [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mVery deep convolutional networks for large-scale image recognition[0m[38;5;12m (2014), K. Simonyan and A. Zisserman [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1409.1556)[39m
[38;5;12m- [39m[38;5;14m[1mReturn of the devil in the details: delving deep into convolutional nets[0m[38;5;12m (2014), K. Chatfield et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1405.3531)[39m
[38;5;12m- [39m[38;5;14m[1mOverFeat: Integrated recognition, localization and detection using convolutional networks[0m[38;5;12m (2013), P. Sermanet et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1312.6229)[39m
[38;5;12m- [39m[38;5;14m[1mMaxout networks[0m[38;5;12m (2013), I. Goodfellow et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1302.4389v4)[39m
[38;5;12m- [39m[38;5;14m[1mNetwork in network[0m[38;5;12m (2013), M. Lin et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1312.4400)[39m
[38;5;12m- [39m[38;5;14m[1mImageNet classification with deep convolutional neural networks[0m[38;5;12m (2012), A. Krizhevsky et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)[39m



[38;2;255;187;0m[4mImage: Segmentation / Object Detection[0m
[38;5;12m- [39m[38;5;14m[1mYou only look once: Unified, real-time object detection[0m[38;5;12m (2016), J. Redmon et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mFully convolutional networks for semantic segmentation[0m[38;5;12m (2015), J. Long et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[0m[38;5;12m (2015), S. Ren et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mFast R-CNN[0m[38;5;12m (2015), R. Girshick [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mRich feature hierarchies for accurate object detection and semantic segmentation[0m[38;5;12m (2014), R. Girshick et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mSpatial pyramid pooling in deep convolutional networks for visual recognition[0m[38;5;12m (2014), K. He et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1406.4729)[39m
[38;5;12m- [39m[38;5;14m[1mSemantic image segmentation with deep convolutional nets and fully connected CRFs[0m[38;5;12m, L. Chen et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1412.7062)[39m
[38;5;12m- [39m[38;5;14m[1mLearning hierarchical features for scene labeling[0m[38;5;12m (2013), C. Farabet et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://hal-enpc.archives-ouvertes.fr/docs/00/74/20/77/PDF/farabet-pami-13.pdf)[39m



[38;2;255;187;0m[4mImage / Video / Etc[0m
[38;5;12m- [39m[38;5;14m[1mImage Super-Resolution Using Deep Convolutional Networks[0m[38;5;12m (2016), C. Dong et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1501.00092v3.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mA neural algorithm of artistic style[0m[38;5;12m (2015), L. Gatys et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1508.06576)[39m
[38;5;12m- [39m[38;5;14m[1mDeep visual-semantic alignments for generating image descriptions[0m[38;5;12m (2015), A. Karpathy and L. Fei-Fei [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mShow, attend and tell: Neural image caption generation with visual attention[0m[38;5;12m (2015), K. Xu et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1502.03044)[39m
[38;5;12m- [39m[38;5;14m[1mShow and tell: A neural image caption generator[0m[38;5;12m (2015), O. Vinyals et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf)[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mLong-term[0m[38;5;14m[1m [0m[38;5;14m[1mrecurrent[0m[38;5;14m[1m [0m[38;5;14m[1mconvolutional[0m[38;5;14m[1m [0m[38;5;14m[1mnetworks[0m[38;5;14m[1m [0m[38;5;14m[1mfor[0m[38;5;14m[1m [0m[38;5;14m[1mvisual[0m[38;5;14m[1m [0m[38;5;14m[1mrecognition[0m[38;5;14m[1m [0m[38;5;14m[1mand[0m[38;5;14m[1m [0m[38;5;14m[1mdescription[0m[38;5;12m [39m[38;5;12m(2015),[39m[38;5;12m [39m[38;5;12mJ.[39m[38;5;12m [39m[38;5;12mDonahue[39m[38;5;12m [39m[38;5;12met[39m[38;5;12m [39m[38;5;12mal.[39m[38;5;12m [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mVQA: Visual question answering[0m[38;5;12m (2015), S. Antol et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Antol_VQA_Visual_Question_ICCV_2015_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDeepFace: Closing the gap to human-level performance in face verification[0m[38;5;12m (2014), Y. Taigman et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf):[39m
[38;5;12m- [39m[38;5;14m[1mLarge-scale video classification with convolutional neural networks[0m[38;5;12m (2014), A. Karpathy et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://vision.stanford.edu/pdf/karpathy14.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mTwo-stream convolutional networks for action recognition in videos[0m[38;5;12m (2014), K. Simonyan et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m3D convolutional neural networks for human action recognition[0m[38;5;12m (2013), S. Ji et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf)[39m





[38;2;255;187;0m[4mNatural Language Processing / RNNs[0m
[38;5;12m- [39m[38;5;14m[1mNeural Architectures for Named Entity Recognition[0m[38;5;12m (2016), G. Lample et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://aclweb.org/anthology/N/N16/N16-1030.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mExploring the limits of language modeling[0m[38;5;12m (2016), R. Jozefowicz et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1602.02410)[39m
[38;5;12m- [39m[38;5;14m[1mTeaching machines to read and comprehend[0m[38;5;12m (2015), K. Hermann et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mEffective approaches to attention-based neural machine translation[0m[38;5;12m (2015), M. Luong et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1508.04025)[39m
[38;5;12m- [39m[38;5;14m[1mConditional random fields as recurrent neural networks[0m[38;5;12m (2015), S. Zheng and S. Jayasumana. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Conditional_Random_Fields_ICCV_2015_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mMemory networks[0m[38;5;12m (2014), J. Weston et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1410.3916)[39m
[38;5;12m- [39m[38;5;14m[1mNeural turing machines[0m[38;5;12m (2014), A. Graves et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1410.5401)[39m
[38;5;12m- [39m[38;5;14m[1mNeural machine translation by jointly learning to align and translate[0m[38;5;12m (2014), D. Bahdanau et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1409.0473)[39m
[38;5;12m- [39m[38;5;14m[1mSequence to sequence learning with neural networks[0m[38;5;12m (2014), I. Sutskever et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mLearning phrase representations using RNN encoder-decoder for statistical machine translation[0m[38;5;12m (2014), K. Cho et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1406.1078)[39m
[38;5;12m- [39m[38;5;14m[1mA convolutional neural network for modeling sentences[0m[38;5;12m (2014), N. Kalchbrenner et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1404.2188v1)[39m
[38;5;12m- [39m[38;5;14m[1mConvolutional neural networks for sentence classification[0m[38;5;12m (2014), Y. Kim [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1408.5882)[39m
[38;5;12m- [39m[38;5;14m[1mGlove: Global vectors for word representation[0m[38;5;12m (2014), J. Pennington et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://anthology.aclweb.org/D/D14/D14-1162.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDistributed representations of sentences and documents[0m[38;5;12m (2014), Q. Le and T. Mikolov [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1405.4053)[39m
[38;5;12m- [39m[38;5;14m[1mDistributed representations of words and phrases and their compositionality[0m[38;5;12m (2013), T. Mikolov et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mEfficient estimation of word representations in vector space[0m[38;5;12m (2013), T. Mikolov et al.  [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1301.3781)[39m
[38;5;12m- [39m[38;5;14m[1mRecursive deep models for semantic compositionality over a sentiment treebank[0m[38;5;12m (2013), R. Socher et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.1327&rep=rep1&type=pdf)[39m
[38;5;12m- [39m[38;5;14m[1mGenerating sequences with recurrent neural networks[0m[38;5;12m (2013), A. Graves. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1308.0850)[39m



[38;2;255;187;0m[4mSpeech / Other Domain[0m
[38;5;12m- [39m[38;5;14m[1mEnd-to-end attention-based large vocabulary speech recognition[0m[38;5;12m (2016), D. Bahdanau et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1508.04395)[39m
[38;5;12m- [39m[38;5;14m[1mDeep speech 2: End-to-end speech recognition in English and Mandarin[0m[38;5;12m (2015), D. Amodei et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1512.02595)[39m
[38;5;12m- [39m[38;5;14m[1mSpeech recognition with deep recurrent neural networks[0m[38;5;12m (2013), A. Graves [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1303.5778.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDeep neural networks for acoustic modeling in speech recognition: The shared views of four research groups[0m[38;5;12m (2012), G. Hinton et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.toronto.edu/~asamir/papers/SPM_DNN_12.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mContext-dependent pre-trained deep neural networks for large-vocabulary speech recognition[0m[38;5;12m (2012) G. Dahl et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.337.7548&rep=rep1&type=pdf)[39m
[38;5;12m- [39m[38;5;14m[1mAcoustic modeling using deep belief networks[0m[38;5;12m (2012), A. Mohamed et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.toronto.edu/~asamir/papers/speechDBN_jrnl.pdf)[39m



[38;2;255;187;0m[4mReinforcement Learning / Robotics[0m
[38;5;12m- [39m[38;5;14m[1mEnd-to-end training of deep visuomotor policies[0m[38;5;12m (2016), S. Levine et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume17/15-522/source/15-522.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mLearning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection[0m[38;5;12m (2016), S. Levine et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.02199)[39m
[38;5;12m- [39m[38;5;14m[1mAsynchronous methods for deep reinforcement learning[0m[38;5;12m (2016), V. Mnih et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v48/mniha16.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDeep Reinforcement Learning with Double Q-Learning[0m[38;5;12m (2016), H. Hasselt et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1509.06461.pdf )[39m
[38;5;12m- [39m[38;5;14m[1mMastering the game of Go with deep neural networks and tree search[0m[38;5;12m (2016), D. Silver et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)[39m
[38;5;12m- [39m[38;5;14m[1mContinuous control with deep reinforcement learning[0m[38;5;12m (2015), T. Lillicrap et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1509.02971)[39m
[38;5;12m- [39m[38;5;14m[1mHuman-level control through deep reinforcement learning[0m[38;5;12m (2015), V. Mnih et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.davidqiu.com:8888/research/nature14236.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mDeep learning for detecting robotic grasps[0m[38;5;12m (2015), I. Lenz et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cornell.edu/~asaxena/papers/lenz_lee_saxena_deep_learning_grasping_ijrr2014.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mPlaying atari with deep reinforcement learning[0m[38;5;12m (2013), V. Mnih et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1312.5602.pdf))[39m



[38;2;255;187;0m[4mMore Papers from 2016[0m
[38;5;12m- [39m[38;5;14m[1mLayer Normalization[0m[38;5;12m (2016), J. Ba et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1607.06450v1.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mLearning to learn by gradient descent by gradient descent[0m[38;5;12m (2016), M. Andrychowicz et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1606.04474v1)[39m
[38;5;12m- [39m[38;5;14m[1mDomain-adversarial training of neural networks[0m[38;5;12m (2016), Y. Ganin et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume17/15-239/source/15-239.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mWaveNet: A Generative Model for Raw Audio[0m[38;5;12m (2016), A. Oord et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1609.03499v2) [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://deepmind.com/blog/wavenet-generative-model-raw-audio/)[39m
[38;5;12m- [39m[38;5;14m[1mColorful image colorization[0m[38;5;12m (2016), R. Zhang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.08511)[39m
[38;5;12m- [39m[38;5;14m[1mGenerative visual manipulation on the natural image manifold[0m[38;5;12m (2016), J. Zhu et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1609.03552)[39m
[38;5;12m- [39m[38;5;14m[1mTexture networks: Feed-forward synthesis of textures and stylized images[0m[38;5;12m (2016), D Ulyanov et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v48/ulyanov16.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mSSD: Single shot multibox detector[0m[38;5;12m (2016), W. Liu et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1512.02325)[39m
[38;5;12m- [39m[38;5;14m[1mSqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size[0m[38;5;12m (2016), F. Iandola et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1602.07360)[39m
[38;5;12m- [39m[38;5;14m[1mEie: Efficient inference engine on compressed deep neural network[0m[38;5;12m (2016), S. Han et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1602.01528)[39m
[38;5;12m- [39m[38;5;14m[1mBinarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1[0m[38;5;12m (2016), M. Courbariaux et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1602.02830)[39m
[38;5;12m- [39m[38;5;14m[1mDynamic memory networks for visual and textual question answering[0m[38;5;12m (2016), C. Xiong et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v48/xiong16.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mStacked attention networks for image question answering[0m[38;5;12m (2016), Z. Yang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_Stacked_Attention_Networks_CVPR_2016_paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mHybrid computing using a neural network with dynamic external memory[0m[38;5;12m (2016), A. Graves et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://www.gwern.net/docs/2016-graves.pdf)[39m
[38;5;12m- [39m[38;5;14m[1mGoogle's neural machine translation system: Bridging the gap between human and machine translation[0m[38;5;12m (2016), Y. Wu et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1609.08144)[39m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[48;2;30;30;40m[38;5;13m[3m [0m


[38;2;255;187;0m[4mNew papers[0m
[48;2;30;30;40m[38;5;13m[3mNewly published papers (< 6 months) which are worth reading[0m
[38;5;12m- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1704.04861.pdf)[39m
[38;5;12m- Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1705.03122)[39m
[38;5;12m- A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1702.01932)[39m
[38;5;12m- Accurate, Large Minibatch SGD:Training ImageNet in 1 Hour (2017), Priya Goyal et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://research.fb.com/wp-content/uploads/2017/06/imagenet1kin1h3.pdf)[39m
[38;5;12m- TACOTRON: Towards end-to-end speech synthesis (2017), Y. Wang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1703.10135.pdf)[39m
[38;5;12m- Deep Photo Style Transfer (2017), F. Luan et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1703.07511v1.pdf)[39m
[38;5;12m- Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1703.03864v1.pdf)[39m
[38;5;12m- Deformable Convolutional Networks (2017), J. Dai et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1703.06211v2.pdf)[39m
[38;5;12m- Mask R-CNN (2017), K. He et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://128.84.21.199/pdf/1703.06870)[39m
[38;5;12m- Learning to discover cross-domain relations with generative adversarial networks (2017), T. Kim et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1703.05192v1.pdf) [39m
[38;5;12m- Deep voice: Real-time neural text-to-speech (2017), S. Arik et al., [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1702.07825v2.pdf)[39m
[38;5;12m- PixelNet: Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1702.06506v1.pdf)[39m
[38;5;12m- Batch renormalization: Towards reducing minibatch dependence in batch-normalized models (2017), S. Ioffe. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1702.03275)[39m
[38;5;12m- Wasserstein GAN (2017), M. Arjovsky et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1701.07875v1)[39m
[38;5;12m- Understanding deep learning requires rethinking generalization (2017), C. Zhang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1611.03530)[39m
[38;5;12m- Least squares generative adversarial networks (2016), X. Mao et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1611.04076v2)[39m


[38;2;255;187;0m[4mOld Papers[0m
[48;2;30;30;40m[38;5;13m[3mClassic papers published before 2012[0m
[38;5;12m- An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_CoatesNL11.pdf)[39m
[38;5;12m- Deep sparse rectifier neural networks (2011), X. Glorot et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf)[39m
[38;5;12m- Natural language processing (almost) from scratch (2011), R. Collobert et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1103.0398)[39m
[38;5;12m- Recurrent neural network based language model (2010), T. Mikolov et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.fit.vutbr.cz/research/groups/speech/servite/2010/rnnlm_mikolov.pdf)[39m
[38;5;12m- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)[39m
[38;5;12m- Learning mid-level features for recognition (2010), Y. Boureau [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://ece.duke.edu/~lcarin/boureau-cvpr-10.pdf)[39m
[38;5;12m- A practical guide to training restricted boltzmann machines (2010), G. Hinton [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.csri.utoronto.ca/~hinton/absps/guideTR.pdf)[39m
[38;5;12m- Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf)[39m
[38;5;12m- Why does unsupervised pre-training help deep learning (2010), D. Erhan et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf)[39m
[38;5;12m- Learning deep architectures for AI (2009), Y. Bengio. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://sanghv.com/download/soft/machine%20learning,%20artificial%20intelligence,%20mathematics%20ebooks/ML/learning%20deep%20architectures%20for%20AI%20(2009).pdf)[39m
[38;5;12m- Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009), H. Lee et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.149.802&rep=rep1&type=pdf)[39m
[38;5;12m- Greedy layer-wise training of deep networks (2007), Y. Bengio et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf)[39m
[38;5;12m- Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://homes.mpimf-heidelberg.mpg.de/~mhelmsta/pdf/2006%20Hinton%20Salakhudtkinov%20Science.pdf)[39m
[38;5;12m- A fast learning algorithm for deep belief nets (2006), G. Hinton et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://nuyoo.utm.mx/~jjf/rna/A8%20A%20fast%20learning%20algorithm%20for%20deep%20belief%20nets.pdf)[39m
[38;5;12m- Gradient-based learning applied to document recognition (1998), Y. LeCun et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)[39m
[38;5;12m- Long short-term memory (1997), S. Hochreiter and J. Schmidhuber. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.mitpressjournals.org/doi/pdfplus/10.1162/neco.1997.9.8.1735)[39m


[38;2;255;187;0m[4mHW / SW / Dataset[0m
[38;5;12m-  SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016), Rajpurkar et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.05250.pdf)[39m
[38;5;12m- OpenAI gym (2016), G. Brockman et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.01540)[39m
[38;5;12m- TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1603.04467)[39m
[38;5;12m- Theano: A Python framework for fast computation of mathematical expressions, R. Al-Rfou et al.[39m
[38;5;12m- Torch7: A matlab-like environment for machine learning, R. Collobert et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf)[39m
[38;5;12m- MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1412.4564)[39m
[38;5;12m- Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1409.0575)[39m
[38;5;12m- Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1408.5093)[39m


[38;2;255;187;0m[4mBook / Survey / Review[0m
[38;5;12m- On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1702.07800)[39m
[38;5;12m- Deep Reinforcement Learning: An Overview (2017), Y. Li, [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1701.07274v2.pdf)[39m
[38;5;12m- Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1703.01619v1.pdf)[39m
[38;5;12m- Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen. [39m[38;5;12mhtml[39m[38;5;14m[1m [0m[38;5;12m (http://neuralnetworksanddeeplearning.com/index.html)[39m
[38;5;12m- Deep learning (Book, 2016), Goodfellow et al. [39m[38;5;12mhtml[39m[38;5;14m[1m [0m[38;5;12m (http://www.deeplearningbook.org/)[39m
[38;5;12m- LSTM: A search space odyssey (2016), K. Greff et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1503.04069.pdf?utm_content=buffereddc5&utm_medium=social&utm_source=plus.google.com&utm_campaign=buffer)[39m
[38;5;12m- Tutorial on Variational Autoencoders (2016), C. Doersch. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.05908)[39m
[38;5;12m- Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf)[39m
[38;5;12m- Deep learning in neural networks: An overview (2015), J. Schmidhuber [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1404.7828)[39m
[38;5;12m- Representation learning: A review and new perspectives (2013), Y. Bengio et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1206.5538)[39m

[38;2;255;187;0m[4mVideo Lectures / Tutorials / Blogs[0m

[48;2;30;30;40m[38;5;13m[3m(Lectures)[0m
[38;5;12m- CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://cs231n.stanford.edu/)[39m
[38;5;12m- CS224d, Deep Learning for Natural Language Processing, Stanford University [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://cs224d.stanford.edu/)[39m
[38;5;12m- Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/oxford-cs-deepnlp-2017/lectures)[39m

[48;2;30;30;40m[38;5;13m[3m(Tutorials)[0m
[38;5;12m- NIPS 2016 Tutorials, Long Beach [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://nips.cc/Conferences/2016/Schedule?type=Tutorial)[39m
[38;5;12m- ICML 2016 Tutorials, New York City [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://techtalks.tv/icml/2016/tutorials/)[39m
[38;5;12m- ICLR 2016 Videos, San Juan [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://videolectures.net/iclr2016_san_juan/)[39m
[38;5;12m- Deep Learning Summer School 2016, Montreal [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://videolectures.net/deeplearning2016_montreal/)[39m
[38;5;12m- Bay Area Deep Learning School 2016, Stanford [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://www.bayareadlschool.org/)[39m

[48;2;30;30;40m[38;5;13m[3m(Blogs)[0m
[38;5;12m- OpenAI [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://www.openai.com/)[39m
[38;5;12m- Distill [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://distill.pub/)[39m
[38;5;12m- Andrej Karpathy Blog [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://karpathy.github.io/)[39m
[38;5;12m- Colah's Blog [39m[38;5;12mWeb[39m[38;5;14m[1m [0m[38;5;12m (http://colah.github.io/)[39m
[38;5;12m- WildML [39m[38;5;12mWeb[39m[38;5;14m[1m [0m[38;5;12m (http://www.wildml.com/)[39m
[38;5;12m- FastML [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (http://www.fastml.com/)[39m
[38;5;12m- TheMorningPaper [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://blog.acolyer.org)[39m

[38;2;255;187;0m[4mAppendix: More than Top 100[0m
[48;2;30;30;40m[38;5;13m[3m(2016)[0m
[38;5;12m- A character-level decoder without explicit segmentation for neural machine translation (2016), J. Chung et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.06147)[39m
[38;5;12m- Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al. [39m[38;5;12mhtml[39m[38;5;14m[1m [0m[38;5;12m (http://www.nature.com/nature/journal/v542/n7639/full/nature21056.html)[39m
[38;5;12m- Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1503.00949)[39m
[38;5;12m- Brain tumor segmentation with deep neural networks (2017), M. Havaei et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1505.03540)[39m
[38;5;12m- Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1610.09038)[39m
[38;5;12m- Adversarially learned inference (2016), V. Dumoulin et al. [39m[38;5;12mweb[39m[38;5;14m[1m [0m[38;5;12m (https://ishmaelbelghazi.github.io/ALI/)[39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.00704v1)[39m
[38;5;12m- Understanding convolutional neural networks (2016), J. Koushik [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1605.09081v1)[39m
[38;5;12m- Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)[39m
[38;5;12m- Adaptive computation time for recurrent neural networks (2016), A. Graves [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1603.08983)[39m
[38;5;12m- Densely connected convolutional networks (2016), G. Huang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1608.06993v1)[39m
[38;5;12m- Region-based convolutional networks for accurate object detection and segmentation (2016), R. Girshick et al. [39m
[38;5;12m- Continuous deep q-learning with model-based acceleration (2016), S. Gu et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v48/gu16.pdf)[39m
[38;5;12m- A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.02858)[39m
[38;5;12m- Achieving open vocabulary neural machine translation with hybrid word-character models, M. Luong and C. Manning. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1604.00788)[39m
[38;5;12m- Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1606.01781)[39m
[38;5;12m- Bag of tricks for efficient text classification (2016), A. Joulin et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1607.01759)[39m
[38;5;12m- Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Efficient_Piecewise_Training_CVPR_2016_paper.pdf)[39m
[38;5;12m- Learning to compose neural networks for question answering (2016), J. Andreas et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1601.01705)[39m
[38;5;12m- Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.08155)[39m
[38;5;12m- Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1412.1842)[39m
[38;5;12m- What makes for effective detection proposals? (2016), J. Hosang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1502.05082)[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;12mInside-outside[39m[38;5;12m [39m[38;5;12mnet:[39m[38;5;12m [39m[38;5;12mDetecting[39m[38;5;12m [39m[38;5;12mobjects[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mcontext[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mskip[39m[38;5;12m [39m[38;5;12mpooling[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mrecurrent[39m[38;5;12m [39m[38;5;12mneural[39m[38;5;12m [39m[38;5;12mnetworks[39m[38;5;12m [39m[38;5;12m(2016),[39m[38;5;12m [39m[38;5;12mS.[39m[38;5;12m [39m[38;5;12mBell[39m[38;5;12m [39m[38;5;12met[39m[38;5;12m [39m[38;5;12mal.[39m[38;5;12m [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Bell_Inside-Outside_Net_Detecting_CVPR_2016_paper.pdf).[39m
[38;5;12m- Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.pdf)[39m
[38;5;12m- Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/6527-tree-structured-reinforcement-learning-for-sequential-object-localization.pdf)[39m
[38;5;12m- Deep networks with stochastic depth (2016), G. Huang et al., [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1603.09382)[39m
[38;5;12m- Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume17/teh16a/teh16a.pdf)[39m

[48;2;30;30;40m[38;5;13m[3m(2015)[0m
[38;5;12m- Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.pdf)[39m
[38;5;12m- Exploring models and data for image question answering (2015), M. Ren et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5640-stochastic-variational-inference-for-hidden-markov-models.pdf)[39m
[38;5;12m- Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5641-are-you-talking-to-a-machine-dataset-and-methods-for-multilingual-image-question.pdf)[39m
[38;5;12m- Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Chen_Minds_Eye_A_2015_CVPR_paper.pdf)[39m
[38;5;12m- From captions to visual concepts and back (2015), H. Fang et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Fang_From_Captions_to_2015_CVPR_paper.pdf).[39m
[38;5;12m- Towards AI-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1502.05698)[39m
[38;5;12m- Ask me anything: Dynamic memory networks for natural language processing (2015), A. Kumar et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/pdf/1506.07285)[39m
[38;5;12m- Unsupervised learning of video representations using LSTMs (2015), N. Srivastava et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v37/srivastava15.pdf)[39m
[38;5;12m- Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding (2015), S. Han et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1510.00149)[39m
[38;5;12m- Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1503.00075)[39m
[38;5;12m- Character-aware neural language models (2015), Y. Kim et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1508.06615)[39m
[38;5;12m- Grammar as a foreign language (2015), O. Vinyals et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf)[39m
[38;5;12m- Trust Region Policy Optimization (2015), J. Schulman et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf)[39m
[38;5;12m- Beyond short snippents: Deep networks for video classification (2015) [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ng_Beyond_Short_Snippets_2015_CVPR_paper.pdf)[39m
[38;5;12m- Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1505.04366v1)[39m
[38;5;12m- Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf)[39m
[38;5;12m- Understanding neural networks through deep visualization (2015), J. Yosinski et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1506.06579)[39m
[38;5;12m- An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al.  [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)[39m
[38;5;12m- Deep generative image models using a￼ laplacian pyramid of adversarial networks (2015), E.Denton et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf)[39m
[38;5;12m- Gated Feedback Recurrent Neural Networks (2015), J. Chung et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/proceedings/papers/v37/chung15.pdf)[39m
[38;5;12m- Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1511.07289.pdf%5Cnhttp://arxiv.org/abs/1511.07289%5Cnhttp://arxiv.org/abs/1511.07289)[39m
[38;5;12m- Pointer networks (2015), O. Vinyals et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (http://papers.nips.cc/paper/5866-pointer-networks.pdf)[39m
[38;5;12m- Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al. [39m[38;5;12mpdf[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1506.02078)[39m
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[38;2;255;187;0m[4mAcknowledgement[0m

[38;5;12mThank you for all your contributions. Please make sure to read the [39m[38;5;14m[1mcontributing guide[0m[38;5;12m (https://github.com/terryum/awesome-deep-learning-papers/blob/master/Contributing.md) before you make a pull request.[39m

[38;2;255;187;0m[4mLicense[0m
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[38;5;12mTo the extent possible under law, [39m[38;5;14m[1mTerry T. Um[0m[38;5;12m (https://www.facebook.com/terryum.io/) has waived all copyright and related or neighboring rights to this work.[39m
