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Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively.
A coefficient of agreement for nominal scales
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A Probabilistic Theory of Pattern Recognition
L. Devroye, L. Györfi, and G. Lugosi · 1997
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Vernon Mountcastle · 1997
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Greedy function approximation: A gradient boosting machine
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Random forests
Leo Breiman · 2001
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Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
Rich Caruana, Steve Lawrence, and C. Giles · 2001
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Flexible control of mutual inhibition: a neural model of two-interval discrimination
Christian K Machens, Ranulfo Romo, and Carlos D Brody · 2005
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An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil · 2006
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An empirical evaluation of supervised learning in high dimensions
Rich Caruana, Nikos Karampatziakis, and Ainur Yessenalina · 2008
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Consistency of random forests and other averaging classifiers
Gérard Biau, Luc Devroye, and Gábor Lugosi · 2008
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim · 2014
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On the number of linear regions of deep neural networks
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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A probabilistic theory of deep learning
Ankit B. Patel, Tan Nguyen, and Richard G. Baraniuk · 2015
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End-to-end learning of deterministic decision trees
Thomas M Hehn and Fred A Hamprecht · 2019
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Gradient boosting with piece-wise linear regression trees
Yu Shi, Jian Li, and Zhize Li · 2019
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Complexity of linear regions in deep networks
Boris Hanin and David Rolnick · 2019
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Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, and Joaquin Vanschoren · 2019
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Openml-python: an extensible python api for openml
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Mueller, Joaquin Vanschoren, and Frank Hutter · 2019
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Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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From whole-brain data to functional circuit models: The zebrafish optomotor response
Eva A Naumann, James E Fitzgerald, Timothy W Dunn, Jason Rihel, Haim Sompolinsky, and Florian Engert · 2016
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Roflmao: Robust oblique forests with linear matrix operations
Tyler M Tomita, Mauro Maggioni, and Joshua T Vogelstein · 2017
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
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Notes on the number of linear regions of deep neural networks
Guido Montúfar · 2017
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Connectal coding: discovering the structures linking cognitive phenotypes to individual histories
Joshua T Vogelstein, Eric W Bridgeford, Benjamin D Pedigo, Jaewon Chung, Keith Levin, Brett Mensh, and Carey E Priebe · 2019
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Pushing the limits of semi-supervised learning for automatic speech recognition
Yu Zhang, James Qin, Daniel S Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V Le, and Yonghui Wu · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Modern machine learning: Partition & vote
Carey E. Priebe, Joshua T. Vogelstein, Florian Engert, and Christopher M. White · 2020
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Sparse projection oblique randomer forests
Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal Burns, Mauro Maggioni, and Joshua T. Vogelstein · 2020
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Reverse-engineering deep relu networks
David Rolnick and Konrad P Kording · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2020
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Jakobovski/free-spoken-digit-dataset: v1.0.10, August 2020
Zohar Jackson, César Souza, Jason Flaks, Yuxin Pan, Hereman Nicolas, and Adhish Thite · 2020
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Deep differentiable random forests for age estimation
Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo Wang, and Alan Yuille · 2021
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Accounting for variance in machine learning benchmarks
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Text-independent speaker recognition using deep neural networks
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A neural architecture search based framework for liquid state machine design
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