[38;5;12m                                                                                                           [39m[38;2;255;187;0m[1m[4mAwesome Random Forest[0m

[38;5;12mRandom Forest - a curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.[39m

[38;2;255;187;0m[4mContributing[0m
[38;5;12mPlease feel free to [39m[38;5;14m[1mpull requests[0m[38;5;12m (https://github.com/kjw0612/awesome-random-forest/pulls).[39m

[38;5;12mThe project is not actively maintained.[39m

[38;5;14m[1m![0m[38;5;12mJoin the chat at https://gitter.im/kjw0612/awesome-random-forest[39m[38;5;14m[1m (https://badges.gitter.im/Join%20Chat.svg)[0m[38;5;12m (https://gitter.im/kjw0612/awesome-random-forest?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)[39m

[38;5;12m![39m[38;5;14m[1mrandomforest[0m[38;5;12m (https://31.media.tumblr.com/79670eabe93cdd448c15f5bcb198d0fb/tumblr_inline_n8e398YbKv1s04rc3.png)[39m

[38;2;255;187;0m[4mTable of Contents[0m

[38;5;12m - [39m[38;5;14m[1mCodes[0m[38;5;12m  (#codes)[39m
[38;5;12m - [39m[38;5;14m[1mTheory[0m[38;5;12m (#theory)[39m
[38;5;12m   - [39m[38;5;14m[1mLectures[0m[38;5;12m (#lectures)[39m
[38;5;12m   - [39m[38;5;14m[1mBooks[0m[38;5;12m (#books)[39m
[38;5;12m   - [39m[38;5;14m[1mPapers[0m[38;5;12m  (#papers)[39m
[48;5;235m[38;5;249m - **Analysis / Understanding**  (#analysis--understanding)[49m[39m
[48;5;235m[38;5;249m - **Model variants**  (#model-variants)[49m[39m[48;5;235m[38;5;249m                   [49m[39m
[38;5;12m   - [39m[38;5;14m[1mThesis[0m[38;5;12m  (#thesis)[39m
[38;5;12m - [39m[38;5;14m[1mApplications[0m[38;5;12m  (#applications)[39m
[38;5;12m   - [39m[38;5;14m[1mImage Classification[0m[38;5;12m  (#image-classification)[39m
[38;5;12m   - [39m[38;5;14m[1mObject Detection[0m[38;5;12m  (#object-detection)[39m
[38;5;12m   - [39m[38;5;14m[1mObject Tracking[0m[38;5;12m  (#object-tracking)[39m
[38;5;12m   - [39m[38;5;14m[1mEdge Detection[0m[38;5;12m  (#edge-detection)[39m
[38;5;12m   - [39m[38;5;14m[1mSemantic Segmentation[0m[38;5;12m  (#semantic-segmentation)[39m
[38;5;12m   - [39m[38;5;14m[1mHuman / Hand Pose Estimation[0m[38;5;12m  (#human--hand-pose-estimation)[39m
[38;5;12m   - [39m[38;5;14m[1m3D Localization[0m[38;5;12m  (#3d-localization)[39m
[38;5;12m   - [39m[38;5;14m[1mLow-Level Vision[0m[38;5;12m  (#low-level-vision)[39m
[38;5;12m   - [39m[38;5;14m[1mFacial Expression Recognition[0m[38;5;12m  (#facial-expression-recognition)[39m
[38;5;12m   - [39m[38;5;14m[1mInterpretability, regularization, compression pruning and feature selection[0m[38;5;12m (#Interpretability, regularization, compression pruning and feature selection)[39m
[38;5;12m   [39m

[38;2;255;187;0m[4mCodes[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMatlab[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPiotr Dollar's toolbox[0m[38;5;12m  (http://vision.ucsd.edu/~pdollar/toolbox/doc/)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAndrej Karpathy's toolbox[0m[38;5;12m  (https://github.com/karpathy/Random-Forest-Matlab)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mM5PrimeLab by Gints Jekabsons[0m[38;5;12m  (http://www.cs.rtu.lv/jekabsons/regression.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mR[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBreiman and Cutler's random forests[0m[38;5;12m  (http://cran.r-project.org/web/packages/randomForest/)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHothorn et al.'s party package with [0m[48;5;235m[38;5;249m[1mcforest[0m[38;5;14m[1m function[0m[38;5;12m (http://cran.r-project.org/web/packages/party/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mC/C++[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSherwood library[0m[38;5;12m  (http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRegression tree package by Pierre Geurts[0m[38;5;12m  (http://www.montefiore.ulg.ac.be/~geurts/Software.html)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mranger: A Fast Implementation of Random Forests[0m[38;5;12m  (https://github.com/imbs-hl/ranger)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mPython[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mScikit-learn[0m[38;5;12m  (http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJavaScript[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mForestjs[0m[38;5;12m  (https://github.com/karpathy/forestjs)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mGo (golang)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCloudForest[0m[38;5;12m  (https://github.com/ryanbressler/CloudForest)[39m
[38;5;12m   [39m
[38;2;255;187;0m[4mTheory[0m
[38;2;255;187;0m[4mLectures[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICCV 2013 Tutorial : Decision Forests and Fields for Computer Vision[0m[38;5;12m  (http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/) by Jamie Shotton and Sebastian Nowozin[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 1[0m
[38;5;12m  (http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/) : Randomized Decision Forests and their Applications in Computer Vision I (Decision Forest, Classification Forest, [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 2[0m[38;5;12m  (http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/) : Randomized Decision Forests and their Applications in Computer Vision II (Regression Forest, Decision Jungle)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 3[0m[38;5;12m  (http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/) : Entropy estimation and streaming data[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 4[0m[38;5;12m  (http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/) : Decision and Regression Tree Fields[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUBC Machine Learning[0m[38;5;12m  (http://www.cs.ubc.ca/~nando/540-2013/lectures.html) by Nando de Freitas[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 8 slide[0m[38;5;12m  (http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf) , [39m[38;5;14m[1mLecture 8 video[0m[38;5;12m  (https://www.youtube.com/watch?v=-dCtJjlEEgM&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=11) : Decision trees[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 9 slide[0m[38;5;12m  (http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf) , [39m[38;5;14m[1mLecture 9 video[0m[38;5;12m  (https://www.youtube.com/watch?v=3kYujfDgmNk&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=12) : Random forests[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLecture 10 video[0m[38;5;12m  (https://www.youtube.com/watch?v=zFGPjRPwyFw&index=13&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6) : Random forest applications[39m
[38;5;12m  [39m
[38;2;255;187;0m[4mBooks[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mAntonio Criminisi, Jamie Shotton (2013)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDecision Forests for Computer Vision and Medical Image Analysis[0m[38;5;12m  (http://link.springer.com/book/10.1007%2F978-1-4471-4929-3)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mTrevor Hastie, Robert Tibshirani, Jerome Friedman (2008)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Elements of Statistical Learning, (Chapter 10, 15, and 16)[0m[38;5;12m  (http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mLuc Devroye, Laszlo Gyorfi, Gabor Lugosi (1996) [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA Probabilistic Theory of Pattern Recognition (Chapter 20, 21)[0m[38;5;12m (http://www.szit.bme.hu/~gyorfi/pbook.pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4mPapers[0m
[38;2;255;187;0m[4mAnalysis / Understanding[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mConsistency of random forests [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.normalesup.org/~scornet/paper/article.pdf) [39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mScornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, in press. [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mOn the asymptotics of random forests [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://arxiv.org/abs/1409.2090)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mScornet, E. (2015). On the asymptotics of random forests, Journal of Multivariate Analysis, in press.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mRandom Forests In Theory and In Practice [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://jmlr.org/proceedings/papers/v32/denil14.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMisha Denil, David Matheson, Nando de Freitas, Narrowing the Gap: Random Forests In Theory and In Practice, ICML 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mExplaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/1504.07676)[0m[38;5;12m [39m

[38;5;12m  [39m
[38;2;255;187;0m[4mModel variants[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDeep Neural Decision Forests [39m[38;5;12mPaper[39m[38;5;14m[1m (https://www.microsoft.com/en-us/research/publication/deep-neural-decision-forests/)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mPeter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulo, Deep Neural Decision Forests, ICCV 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mCanonical Correlation Forests [39m[38;5;12mPaper[39m[38;5;14m[1m (http://arxiv.org/pdf/1507.05444.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mTom Rainforth, and Frank Wood, Canonical Correlation Forests, arxiv 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mRelating Cascaded Random Forests to Deep Convolutional Neural Networks [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://arxiv.org/pdf/1507.07583.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDavid L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W Myers, and Carsten Rother, Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation, arxiv 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mBayesian Forests [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://jmlr.org/proceedings/papers/v37/matthew15.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mTaddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle, Bayesian and Empirical Bayesian Forests, ICML 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMondrian[39m[38;5;12m [39m[38;5;12mForests:[39m[38;5;12m [39m[38;5;12mEfficient[39m[38;5;12m [39m[38;5;12mOnline[39m[38;5;12m [39m[38;5;12mRandom[39m[38;5;12m [39m[38;5;12mForests[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_nips14.pdf)[39m[38;5;12m [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(http://www.gatsby.ucl.ac.uk/~balaji/mondrianforest/)[39m[38;5;12m [39m[38;5;12mSlides[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_slides.pdf)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mBalaji Lakshminarayanan, Daniel M. Roy and Yee Whye Teh, Mondrian Forests: Efficient Online Random Forests, NIPS 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mExtremely[39m[38;5;12m [39m[38;5;12mrandomized[39m[38;5;12m [39m[38;5;12mtrees[39m[38;5;12m [39m[38;5;12mP[39m[38;5;12m [39m[38;5;12mGeurts,[39m[38;5;12m [39m[38;5;12mD[39m[38;5;12m [39m[38;5;12mErnst,[39m[38;5;12m [39m[38;5;12mL[39m[38;5;12m [39m[38;5;12mWehenkel[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mMachine[39m[38;5;12m [39m[38;5;12mlearning,[39m[38;5;12m [39m[38;5;12m2006[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;14m[1m(http://orbi.ulg.be/bitstream/2268/9357/1/geurts-mlj-advance.pdf)[0m[38;5;12m  [39m[38;5;12mCode[39m[38;5;14m[1m  [0m
[38;5;14m[1m(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)[0m[38;5;12m [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDecision Jungles [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models for Classification, NIPS 2013[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mLaptev,[39m[38;5;12m [39m[38;5;12mDmitry,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mJoachim[39m[38;5;12m [39m[38;5;12mM.[39m[38;5;12m [39m[38;5;12mBuhmann.[39m[38;5;12m [39m[38;5;12mTransformation-invariant[39m[38;5;12m [39m[38;5;12mconvolutional[39m[38;5;12m [39m[38;5;12mjungles.[39m[38;5;12m [39m[38;5;12mCVPR[39m[38;5;12m [39m[38;5;12m2015.[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m
[38;5;14m[1m(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Laptev_Transformation-Invariant_Convolutional_Jungles_2015_CVPR_paper.pdf)[0m[38;5;12m [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSemi-supervised Node Splitting for Random Forest Construction [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mXiao Liu, Mingli Song, Dacheng Tao, Zicheng Liu, Luming Zhang, Chun Chen and Jiajun Bu, Semi-supervised Node Splitting for Random Forest Construction, CVPR 2013[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mImproved Information Gain Estimates for Decision Tree Induction [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSebastian Nowozin, Improved Information Gain Estimates for Decision Tree Induction, ICML 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMIForests: Multiple-Instance Learning with Randomized Trees [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://lrs.icg.tugraz.at/pubs/leistner_eccv_10.pdf)[0m[38;5;12m  [39m[38;5;12mCode[39m[38;5;14m[1m  (http://www.ymer.org/amir/software/milforests/)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mChristian Leistner, Amir Saffari, and Horst Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSamuel[39m[38;5;12m [39m[38;5;12mSchulter,[39m[38;5;12m [39m[38;5;12mPaul[39m[38;5;12m [39m[38;5;12mWohlhart,[39m[38;5;12m [39m[38;5;12mChristian[39m[38;5;12m [39m[38;5;12mLeistner,[39m[38;5;12m [39m[38;5;12mAmir[39m[38;5;12m [39m[38;5;12mSaffari,[39m[38;5;12m [39m[38;5;12mPeter[39m[38;5;12m [39m[38;5;12mM.[39m[38;5;12m [39m[38;5;12mRoth,[39m[38;5;12m [39m[38;5;12mHorst[39m[38;5;12m [39m[38;5;12mBischof:[39m[38;5;12m [39m[38;5;12mAlternating[39m[38;5;12m [39m[38;5;12mDecision[39m[38;5;12m [39m[38;5;12mForests.[39m[38;5;12m [39m[38;5;12mCVPR[39m[38;5;12m [39m[38;5;12m2013[39m[38;5;12m [39m[38;5;14m[1mPaper[0m[38;5;12m [39m
[38;5;12m(http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Schulter_Alternating_Decision_Forests_2013_CVPR_paper.pdf)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDecision Forests, Convolutional Networks and the Models in-Between [39m[38;5;12mPaper[39m[38;5;14m[1m (https://arxiv.org/abs/1603.01250)[0m[38;5;12m [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mRandom Uniform Forests Saïp Ciss [39m[38;5;12mPaper[39m[38;5;14m[1m (https://hal.archives-ouvertes.fr/hal-01104340/)[0m[38;5;12m  [39m[38;5;12mCode R[39m[38;5;14m[1m (https://cran.r-project.org/web/packages/randomUniformForest/index.html)[0m[38;5;12m [39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mAutoencoder Trees, Ozan İrsoy, Ethem Alpaydın 2015 [39m[38;5;12mPaper[39m[38;5;14m[1m (http://www.jmlr.org/proceedings/papers/v45/Irsoy15.pdf)[0m[38;5;12m  [39m
[38;5;12m [39m

[38;2;255;187;0m[4mThesis[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mUnderstanding Random Forests[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mPhD dissertation, Gilles Louppe, July 2014. Defended on October 9, 2014. [39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mRepository[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/glouppe/phd-thesis) with thesis and related codes[39m

[38;5;12m [39m
[38;2;255;187;0m[4mApplications[0m

[38;2;255;187;0m[4mImage classification[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mETH Zurich [39m[38;5;12mPaper-CVPR15[39m[38;5;14m[1m  (http://www.iai.uni-bonn.de/~gall/download/jgall_coarse2fine_cvpr15.pdf)[0m[38;5;12m [39m
[48;5;235m[38;5;249m		 ****Paper-CVPR14**  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ristin_Incremental_Learning_of_2014_CVPR_paper.pdf)** [49m[39m
[48;5;235m[38;5;249m		 ****Paper-ECCV**  (http://www.vision.ee.ethz.ch/~lbossard/bossard_eccv14_food-101.pdf)** [49m[39m[48;5;235m[38;5;249m                                                   [49m[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMarko Ristin, Juergen Gall, Matthieu Guillaumin, and Luc Van Gool, From Categories to Subcategories: Large-scale Image Classification with Partial Class Label Refinement, CVPR 2015[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMarko Ristin, Matthieu Guillaumin, Juergen Gall, and Luc Van Gool, Incremental Learning of NCM Forests for Large-Scale Image Classification, CVPR 2014[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mLukas Bossard, Matthieu Guillaumin, and Luc Van Gool, Food-101 – Mining Discriminative Components with Random Forests, ECCV 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mUniversity of Girona & University of Oxford [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.cs.huji.ac.il/~daphna/course/CoursePapers/bosch07a.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mAnna Bosch, Andrew Zisserman, and Xavier Munoz, Image Classification using Random Forests and Ferns, ICCV 2007[39m

[38;2;255;187;0m[4mObject Detection[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mGraz University of Technology [39m[38;5;12mPaper-CVPR[39m[38;5;14m[1m  (http://lrs.icg.tugraz.at/pubs/schulter_cvpr_14.pdf)[0m[38;5;12m  [39m[38;5;12mPaper-ICCV[39m[38;5;14m[1m  (http://lrs.icg.tugraz.at/pubs/schulter_iccv_13.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSamuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth, and Horst Bischof, Accurate Object Detection with Joint Classification-Regression Random Forests, CVPR 2014[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSamuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth, and Horst Bischof, Alternating Regression Forests for Object Detection and Pose Estimation, ICCV 2013[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mETH Zurich + Microsoft Research Cambridge [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.iai.uni-bonn.de/~gall/download/jgall_houghforest_cvpr09.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJuergen Gall, and Victor Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR 2009[39m

[38;2;255;187;0m[4mObject Tracking[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mTechnische Universitat Munchen [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://campar.in.tum.de/pub/tanda2014cvpr/tanda2014cvpr.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDavid Joseph Tan, and Slobodan Ilic, Multi-Forest Tracker: A Chameleon in Tracking, CVPR 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mETH Zurich + Leibniz University Hannover + Stanford University [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.igp.ethz.ch/photogrammetry/publications/pdf_folder/LeaFenKuzRosSavCVPR14.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mLaura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn, and Silvio Savarese, Learning an image-based motion context for multiple people tracking, CVPR 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mGraz University of Technology [39m[38;5;12mPaper[39m[38;5;14m[1m  (https://lrs.icg.tugraz.at/pubs/godec_iccv_11.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMartin Godec, Peter M. Roth, and Horst Bischof, Hough-based Tracking of Non-Rigid Objects, ICCV 2011[39m

[38;2;255;187;0m[4mEdge Detection[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mUniversity of California, Irvine [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf)[0m[38;5;12m  [39m[38;5;12mCode[39m[38;5;14m[1m  (https://github.com/samhallman/oef)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSam Hallman, and Charless C. Fowlkes, Oriented Edge Forests for Boundary Detection, CVPR 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf)[0m[38;5;12m  [39m[38;5;12mCode[39m[38;5;14m[1m  (https://github.com/pdollar/edges)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mPiotr Dollar, and C. Lawrence Zitnick, Structured Forests for Fast Edge Detection, ICCV 2013[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMassachusetts Inst. of Technology + Microsoft Research [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf)[0m[38;5;12m  [39m[38;5;12mCode[39m[38;5;14m[1m  (https://github.com/joelimlimit/SketchTokens)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJoseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar, Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection, CVPR 2013[39m

[38;2;255;187;0m[4mSemantic Segmentation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mFondazione Bruno Kessler, Microsoft Research Cambridge [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.dsi.unive.it/~srotabul/files/publications/CVPR2014a.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mSamuel Rota Bulo, and Peter Kontschieder, Neural Decision Forests for Semantic Image Labelling, CVPR 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mINRIA +  Microsoft Research Cambridge [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mHerve Lombaert, Darko Zikic, Antonio Criminisi, and Nicholas Ayache, Laplacian Forests:Semantic Image Segmentation by Guided Bagging, MICCAI 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research Cambridge +  GE Global Research Center + University of California +  Rutgers Univeristy [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mAlbert Montillo1, Jamie Shotton, John Winn, Juan Eugenio Iglesias, Dimitri Metaxas, and Antonio Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, IPMI 2011[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mUniversity of Cambridge + Toshiba Corporate R&D Center [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJamie Shotton, Matthew Johnson, and Roberto Cipolla, Semantic Texton Forests for Image Categorization and Segmentation, CVPR 2008[39m
[38;5;12m  [39m
[38;2;255;187;0m[4mHuman / Hand Pose Estimation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research Cambridge [39m[38;5;12mPaper-CHI[39m[38;5;14m[1m  (http://research.microsoft.com/pubs/238453/pn362-sharp.pdf)[0m[38;5;12m [39m[38;5;12mVideo-CHI[39m[38;5;14m[1m  (http://research.microsoft.com/pubs/238453/pn362-sharp-video.mp4)[0m[38;5;12m [39m
[48;5;235m[38;5;249m                           ****Paper-CVPR**  (http://research.microsoft.com/pubs/162510/vm.pdf)** [49m[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mToby[39m[38;5;12m [39m[38;5;12mSharp,[39m[38;5;12m [39m[38;5;12mCem[39m[38;5;12m [39m[38;5;12mKeskin,[39m[38;5;12m [39m[38;5;12mDuncan[39m[38;5;12m [39m[38;5;12mRobertson,[39m[38;5;12m [39m[38;5;12mJonathan[39m[38;5;12m [39m[38;5;12mTaylor,[39m[38;5;12m [39m[38;5;12mJamie[39m[38;5;12m [39m[38;5;12mShotton,[39m[38;5;12m [39m[38;5;12mDavid[39m[38;5;12m [39m[38;5;12mKim,[39m[38;5;12m [39m[38;5;12mChristoph[39m[38;5;12m [39m[38;5;12mRhemann,[39m[38;5;12m [39m[38;5;12mIdo[39m[38;5;12m [39m[38;5;12mLeichter,[39m[38;5;12m [39m[38;5;12mAlon[39m[38;5;12m [39m[38;5;12mVinnikov,[39m[38;5;12m [39m[38;5;12mYichen[39m[38;5;12m [39m[38;5;12mWei,[39m[38;5;12m [39m[38;5;12mDaniel[39m[38;5;12m [39m[38;5;12mFreedman,[39m[38;5;12m [39m[38;5;12mPushmeet[39m[38;5;12m [39m[38;5;12mKohli,[39m[38;5;12m [39m[38;5;12mEyal[39m[38;5;12m [39m[38;5;12mKrupka,[39m[38;5;12m [39m[38;5;12mAndrew[39m[38;5;12m [39m[38;5;12mFitzgibbon,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mShahram[39m[38;5;12m [39m[38;5;12mIzadi,[39m[38;5;12m [39m
[38;5;12mAccurate,[39m[38;5;12m [39m[38;5;12mRobust,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mFlexible[39m[38;5;12m [39m[38;5;12mReal-time[39m[38;5;12m [39m[38;5;12mHand[39m[38;5;12m [39m[38;5;12mTracking,[39m[38;5;12m [39m[38;5;12mCHI[39m[38;5;12m [39m[38;5;12m2015[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mJonathan Taylor, Jamie Shotton, Toby Sharp, and Andrew Fitzgibbon, The Vitruvian Manifold:Inferring Dense Correspondences for One-Shot Human Pose Estimation, CVPR 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research Haifa [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mEyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, and Daniel Freedman, Discriminative Ferns Ensemble for Hand Pose Recognition, CVPR 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research Asia [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://research.microsoft.com/en-us/people/yichenw/cvpr14_facealignment.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mShaoqing Ren, Xudong Cao, Yichen Wei, and Jian Sun, Face Alignment at 3000 FPS via Regressing Local Binary Features, CVPR 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mImperial College London [39m[38;5;12mPaper-CVPR-Face[39m[38;5;14m[1m  (http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_xiaowei.pdf)[0m[38;5;12m [39m
[48;5;235m[38;5;249m                      ****Paper-CVPR-Hand**  (http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_danny.pdf)** [49m[39m
[48;5;235m[38;5;249m					  ****Paper-ICCV**  (http://www.iis.ee.ic.ac.uk/icvl/doc/ICCV13_danny.pdf)** [49m[39m[48;5;235m[38;5;249m                         [49m[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mXiaowei Zhao, Tae-Kyun Kim, and Wenhan Luo, Unified Face Analysis by Iterative Multi-Output Random Forests, CVPR 2014[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDanhang Tang, Hyung Jin Chang, Alykhan Tejani, and Tae-Kyun Kim, Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture, CVPR 2014[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDanhang Tang, Tsz-Ho Yu, and Tae-Kyun Kim, Real-time Articulated Hand Pose Estimation using Semi-supervised Transductive Regression Forests, ICCV 2013[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mETH Zurich + Microsoft [39m[38;5;12mPaper[39m[38;5;14m[1m  (https://lirias.kuleuven.be/bitstream/123456789/398648/2/3601_open+access.pdf)[0m[38;5;12m [39m
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[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mMicrosoft Research Cambridge + University of Illinois + Imperial College London [39m[38;5;12mPaper[39m[38;5;14m[1m  (http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)[0m[38;5;12m [39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mAbner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, and Shahram Izadi, Multi-Output Learning for Camera Relocalization, CVPR 2014[39m
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[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mCui, Zhicheng, et al. "Optimal action extraction for random forests and boosted trees." ACM SIGKDD 2015. [39m[38;5;12mPaper[39m[38;5;14m[1m (http://www.cse.wustl.edu/~ychen/public/OAE.pdf)[0m[38;5;12m [39m
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[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mBegon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning and pruning of decision forests. (2016). [39m[38;5;12mPaper[39m[38;5;14m[1m (http://orbi.ulg.ac.be/bitstream/2268/202344/1/Begon_jlpdf_abstract.pdf)[0m[38;5;12m 	[39m



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