awesome-random-forest
by kjw0612
Random Forest - a curated list of resources regarding random forest
AI summary
Forest toolkit
A curated collection of resources and tools for the Random Forest algorithm
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What's in the list
78 links in 30 sections, with live GitHub stats.activeno commit in 2y
Codes / Matlab
- http://vision.ucsd.edu/~pdollar/toolbox/doc/
[Piotr Dollar's toolbox] ( )
https://github.com/karpathy/Random-Forest-Matlab
[Andrej Karpathy's toolbox] ( )
- http://www.cs.rtu.lv/jekabsons/regression.html
[M5PrimeLab by Gints Jekabsons] ( )
Codes / R
- http://cran.r-project.org/web/packages/randomForest/
[Breiman and Cutler's random forests] ( )
Codes / C/C++
- http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/
[Sherwood library] ( )
- http://www.montefiore.ulg.ac.be/~geurts/Software.html
[Regression tree package by Pierre Geurts] ( )
https://github.com/imbs-hl/ranger
[ranger: A Fast Implementation of Random Forests] ( )
Codes / Python
Codes / JavaScript
[Forestjs] ( )
Codes / Go (golang)
https://github.com/ryanbressler/CloudForest
[CloudForest] ( )
Theory / Lectures
- http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/
[ICCV 2013 Tutorial : Decision Forests and Fields for Computer Vision] ( ) by Jamie Shotton and Sebastian Nowozin
Theory / Lectures / http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/
- http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/
[Lecture 1] ( ) : Randomized Decision Forests and their Applications in Computer Vision I (Decision Forest, Classification Forest,
- http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/
[Lecture 2] ( ) : Randomized Decision Forests and their Applications in Computer Vision II (Regression Forest, Decision Jungle)
- http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/
[Lecture 3] ( ) : Entropy estimation and streaming data
- http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/
[Lecture 4] ( ) : Decision and Regression Tree Fields
Theory / Lectures
- http://www.cs.ubc.ca/~nando/540-2013/lectures.html
[UBC Machine Learning] ( ) by Nando de Freitas
Theory / Lectures / http://www.cs.ubc.ca/~nando/540-2013/lectures.html
- http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf
[Lecture 8 slide] ( ) , [Lecture 8 video] ( ) : Decision trees
- http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf
[Lecture 9 slide] ( ) , [Lecture 9 video] ( ) : Random forests
- https://www.youtube.com/watch?v=zFGPjRPwyFw&index=13&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6
[Lecture 10 video] ( ) : Random forest applications
Theory / Books / Antonio Criminisi, Jamie Shotton (2013)
- http://link.springer.com/book/10.1007%2F978-1-4471-4929-3
[Decision Forests for Computer Vision and Medical Image Analysis] ( )
Theory / Books / Trevor Hastie, Robert Tibshirani, Jerome Friedman (2008)
- http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf
[The Elements of Statistical Learning, (Chapter 10, 15, and 16)] ( )
Theory / Books / Luc Devroye, Laszlo Gyorfi, Gabor Lugosi (1996)
Theory / Papers
- [Paper]
Consistency of random forests
- [Paper]
On the asymptotics of random forests
- http://jmlr.org/proceedings/papers/v32/denil14.pdf)]
Random Forests In Theory and In Practice [[Paper] (
- Paper
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease [ ]
- Paper
Deep Neural Decision Forests [ ]
- Paper
Canonical Correlation Forests [ ]
- http://arxiv.org/pdf/1507.07583.pdf)]
Relating Cascaded Random Forests to Deep Convolutional Neural Networks [[Paper] (
- http://jmlr.org/proceedings/papers/v37/matthew15.pdf)]
Bayesian Forests [[Paper] (
- [Paper]
Mondrian Forests: Efficient Online Random Forests
- Paper
Extremely randomized trees P Geurts, D Ernst, L Wehenkel - Machine learning, 2006 [ ] [[Code] (
- http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)]
Decision Jungles [[Paper] (
Theory / Papers / http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)]
- Paper
Laptev, Dmitry, and Joachim M. Buhmann. Transformation-invariant convolutional jungles. CVPR 2015. [ ]
Theory / Papers
- http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf)]
Semi-supervised Node Splitting for Random Forest Construction [[Paper] (
- http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf)]
Improved Information Gain Estimates for Decision Tree Induction [[Paper] (
- http://lrs.icg.tugraz.at/pubs/leistner_eccv_10.pdf)]
MIForests: Multiple-Instance Learning with Randomized Trees [[Paper] ( [[Code] (
- Paper
Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof: Alternating Decision Forests. CVPR 2013
- Paper
Decision Forests, Convolutional Networks and the Models in-Between [ ]
- Paper
Random Uniform Forests Saïp Ciss [ ] [ ]
- Paper
Autoencoder Trees, Ozan İrsoy, Ethem Alpaydın 2015 [ ]
Thesis
[Repository]
with thesis and related codes
Applications / Image classification
- http://www.iai.uni-bonn.de/~gall/download/jgall_coarse2fine_cvpr15.pdf)]
ETH Zurich [[Paper-CVPR15] ( [[Paper-CVPR14] ( [[Paper-ECCV] (
- http://www.cs.huji.ac.il/~daphna/course/CoursePapers/bosch07a.pdf)]
University of Girona & University of Oxford [[Paper] (
Applications / Object Detection
- http://lrs.icg.tugraz.at/pubs/schulter_cvpr_14.pdf)]
Graz University of Technology [[Paper-CVPR] ( [[Paper-ICCV] (
- http://www.iai.uni-bonn.de/~gall/download/jgall_houghforest_cvpr09.pdf)]
ETH Zurich + Microsoft Research Cambridge [[Paper] (
Applications / Object Tracking
- http://campar.in.tum.de/pub/tanda2014cvpr/tanda2014cvpr.pdf)]
Technische Universitat Munchen [[Paper] (
- http://www.igp.ethz.ch/photogrammetry/publications/pdf_folder/LeaFenKuzRosSavCVPR14.pdf)]
ETH Zurich + Leibniz University Hannover + Stanford University [[Paper] (
- https://lrs.icg.tugraz.at/pubs/godec_iccv_11.pdf)]
Graz University of Technology [[Paper] (
Applications / Edge Detection
- http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf)]
University of California, Irvine [[Paper] ( [[Code] (
- http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf)]
Microsoft Research [[Paper] ( [[Code] (
- http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf)]
Massachusetts Inst. of Technology + Microsoft Research [[Paper] ( [[Code] (
Applications / Semantic Segmentation
- http://www.dsi.unive.it/~srotabul/files/publications/CVPR2014a.pdf)]
Fondazione Bruno Kessler, Microsoft Research Cambridge [[Paper] (
- http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf)]
INRIA + Microsoft Research Cambridge [[Paper] (
- http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf)]
Microsoft Research Cambridge + GE Global Research Center + University of California + Rutgers Univeristy [[Paper] (
- http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf)]
University of Cambridge + Toshiba Corporate R&D Center [[Paper] (
Applications / Human / Hand Pose Estimation
- http://research.microsoft.com/pubs/238453/pn362-sharp.pdf)][[Video-CHI]
Microsoft Research Cambridge [[Paper-CHI] ( ( [[Paper-CVPR] (
- http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf)]
Microsoft Research Haifa [[Paper] (
- http://research.microsoft.com/en-us/people/yichenw/cvpr14_facealignment.pdf)]
Microsoft Research Asia [[Paper] (
- http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_xiaowei.pdf)]
Imperial College London [[Paper-CVPR-Face] ( [[Paper-CVPR-Hand] ( [[Paper-ICCV] (
- https://lirias.kuleuven.be/bitstream/123456789/398648/2/3601_open+access.pdf)]
ETH Zurich + Microsoft [[Paper] (
Applications / 3D localization
- http://www.iis.ee.ic.ac.uk/icvl/doc/ECCV2014_aly.pdf)]
Imperial College London [[Paper] (
- http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)]
Microsoft Research Cambridge + University of Illinois + Imperial College London [[Paper] (
- http://research.microsoft.com/pubs/184826/relocforests.pdf)]
Microsoft Research Cambridge [[Paper] (
Applications / Low-Level vision / Super-Resolution
- Paper
Technicolor R&I Hannover [ ]
- http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schulter_Fast_and_Accurate_2015_CVPR_paper.pdf)]
Graz University of Technology [[Paper] (
Applications / Low-Level vision / Denoising
- http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf)]
Microsoft Research + iCub Facility - Istituto Italiano di Tecnologia [[Paper] (
Applications / Facial expression recognition
- Paper
Sorbonne Universites [ ]
Applications / Interpretability, regularization, compression pruning and feature selection
- http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf)]
Global Refinement of Random Forest [[Paper] (
- Paper
L1-based compression of random forest models Arnaud Joly, Fran¸cois Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 [ ]
- http://jmlr.org/proceedings/papers/v37/nan15.pdf)]
Feature-Budgeted Random Forest [[Paper] ( [ ]
Applications / Interpretability, regularization, compression pruning and feature selection / http://jmlr.org/proceedings/papers/v37/nan15.pdf)]
- Paper
Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama NIPS 2016 [ ]
Applications / Interpretability, regularization, compression pruning and feature selection
- Paper
Meinshausen, Nicolai. "Node harvest." The Annals of Applied Statistics 4.4 (2010): 2049-2072. [ ] [ ] [ ]
- Paper
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach S. Hara, K. Hayashi, [ ] [ ]
- Paper
Cui, Zhicheng, et al. "Optimal action extraction for random forests and boosted trees." ACM SIGKDD 2015. [ ]
- Paper
DART: Dropouts meet Multiple Additive Regression Trees K. V. Rashmi, Ran Gilad-Bachrach [ ]
- Paper
Begon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning and pruning of decision forests. (2016). [ ]
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