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Ensembling multiple Deep Neural Networks (DNNs) is a simple and effective way to improve top-line metrics and to outperform a larger single model.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E. Schapire · 1995
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Popular ensemble methods: An empirical study
D. Opitz and R. Maclin · 1999
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Random forests
Leo Breiman · 2001
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2003
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Diverse ensembles improve calibration
Asa Cooper Stickland and Iain Murray · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Anti-distillation: Improving reproducibility of deep networks
Gil I. Shamir and Lorenzo Coviello · 2010
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
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Multiple networks are more efficient than one: Fast and accurate models via ensembles and cascades
Xiaofang Wang, Dan Kondratyuk, Kris M. Kitani, Yair Movshovitz-Attias, and Elad Eban · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Why M heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David J. Crandall, and Dhruv Batra · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Fairness constraints: Mechanisms for fair classification, 2015
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Launch and iterate: Reducing prediction churn
Mahdi Milani Fard, Quentin Cormier, Kevin Canini, and Maya Gupta · 2016
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A brief survey of deep reinforcement learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou · 2017
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On fairness, diversity and randomness in algorithmic decision making, 2017
Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, and Adrian Weller · 2017
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Regularization for deep learning: A taxonomy, 2017
Jan Kukačka, Vladimir Golkov, and Daniel Cremers · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D Sculley · 2017
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Satrajit Chatterjee · 2020
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Beyond point estimate: Inferring ensemble prediction variation from neuron activation strength in recommender systems, 2020
Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, and Claire Cui · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2018
Cited alongside, same era.
Vitaly Feldman and Chiyuan Zhang · 2020
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Maximizing overall diversity for improved uncertainty estimates in deep ensembles
Siddhartha Jain, Ge Liu, Jonas Mueller, and David Gifford · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Impact of model ensemble on the fairness of classifiers in machine learning
Patrik Joslin Kenfack, Adil Mehmood Khan, SM Ahsan Kazmi, Rasheed Hussain, Alma Oracevic, and Asad Masood Khattak · 2021
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Are my deep learning systems fair? an empirical study of fixed-seed training
Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, and Sameena Shah · 2021
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Manipulating sgd with data ordering attacks, 2021
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, and Ross Anderson · 2021
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Synthesizing irreproducibility in deep networks
Robert R. Snapp and Gil I. Shamir · 2021
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Nondeterminism and instability in neural network optimization
Cecilia Summers and Michael J Dinneen · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Neural ensemble search for uncertainty estimation and dataset shift
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C Holmes, Frank Hutter, and Yee Teh · 2021
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The effects of regularization and data augmentation are class dependent
Randall Balestriero, Leon Bottou, and Yann LeCun · 2022
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Maat: a novel ensemble approach to addressing fairness and performance bugs for machine learning software
Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman · 2022
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Ensembling over classifiers: a bias-variance perspective
Neha Gupta, Jamie Smith, Ben Adlam, and Zelda Mariet · 2022
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Intriguing properties of compression on multilingual models, 2022
Kelechi Ogueji, Orevaoghene Ahia, Gbemileke Onilude, Sebastian Gehrmann, Sara Hooker, and Julia Kreutzer · 2022
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Diverse weight averaging for out-of-distribution generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord · 2022
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Fairness via in-processing in the over-parameterized regime: A cautionary tale
Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, and Siddharth Garg · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Randomness in neural network training: Characterizing the impact of tooling
Donglin Zhuang, Xingyao Zhang, Shuaiwen Song, and Sara Hooker · 2022
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Arbitrariness and prediction: The confounding role of variance in fair classification, 2023
A. Feder Cooper, Katherine Lee, Madiha Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang · 2023
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Towards understanding fairness and its composition in ensemble machine learning
Usman Gohar, Sumon Biswas, and Hridesh Rajan · 2023
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