Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications

29 October 2019

Papers citing "Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications"

26 / 26 papers shown

Title
Uncovering Memorization Effect in the Presence of Spurious Correlations Chenyu You Haocheng Dai Yifei Min Jasjeet Sekhon S. Joshi James S. Duncan 139 3 0 01 Jan 2025
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 225 470 0 09 Aug 2020
Data Dropout: Optimizing Training Data for Convolutional Neural Networks Tianyang Wang Jun Huan Bo Li OOD 52 57 0 01 Sep 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 152 1,431 0 24 Mar 2018
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks Yongshuai Liu Jiyu Chen Hao Chen AAML 69 14 0 09 Jan 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels Lu Jiang Zhengyuan Zhou Thomas Leung Li Li Li Fei-Fei NoLa 131 1,456 0 14 Dec 2017
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases Pierre Stock Moustapha Cissé FaML 80 46 0 30 Nov 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 193 593 0 13 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,928 0 25 Aug 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 163 2,160 0 21 Aug 2017
Biased Importance Sampling for Deep Neural Network Training Angelos Katharopoulos François Fleuret 63 68 0 31 May 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang Erik Learned-Miller Andrew McCallum 92 354 0 24 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 83 1,526 0 11 Apr 2017
Practical Coreset Constructions for Machine Learning Olivier Bachem Mario Lucic Andreas Krause 67 186 0 19 Mar 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 219 2,910 0 14 Mar 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 94 1,021 0 18 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,912 0 08 Jul 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 218 6,172 0 01 Jul 2016
Coresets for Scalable Bayesian Logistic Regression Jonathan H. Huggins Trevor Campbell Tamara Broderick 68 219 0 20 May 2016
Training Region-based Object Detectors with Online Hard Example Mining Abhinav Shrivastava Abhinav Gupta Ross B. Girshick ObjD 157 2,422 0 12 Apr 2016
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim Cynthia Rudin J. Shah 81 321 0 03 Mar 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,615 0 01 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 289 14,968 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 317 7,321 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 603 15,907 0 12 Nov 2013