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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Random forests
Leo Breiman · 2001
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Analysis of representations for domain adaptation
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Imagenet: A large-scale hierarchical image database
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Unsupervised supervised learning i: Estimating classification and regression errors without labels
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An analysis of single-layer networks in unsupervised feature learning
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Learning feature representations with k-means
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
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Emmanouil Antonios Platanios, Avrim Blum, and Tom Mitchell · 2014
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Estimating the accuracies of multiple classifiers without labeled data
Ariel Jaffe, Boaz Nadler, and Yuval Kluger · 2015
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Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
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Very deep convolutional networks for large-scale image recognition
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Unsupervised risk estimation using only conditional independence structure
Jacob Steinhardt and Percy Liang · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Dual path networks
Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, and Jiashi Feng · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Exploring generalization in deep learning
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Emmanouil Platanios, Hoifung Poon, Tom M Mitchell, and Eric J Horvitz · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, et al · 2018
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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A survey on domain adaptation theory: learning bounds and theoretical guarantees
Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, and Younès Bennani · 2020
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Learning to validate the predictions of black box classifiers on unseen data
Sebastian Schelter, Tammo Rukat, and Felix Biessmann · 2020
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On uniform convergence and low-norm interpolation learning
Lijia Zhou, Danica J. Sutherland, and Nati Srebro · 2020
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Beit: Bert pre-training of image transformers
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Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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Squeeze-and-excitation networks
Li Shen Jie Hu and Gang Sun · 2018
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Adversarial attacks and defences competition
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan L. Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, and Motoki Abe · 2018
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Progressive neural architecture search
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Shufflenet v2: practical guidelines for efficient cnn architecture design
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Mobilenetv2: Inverted residuals and linear bottlenecks
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
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High-performance large-scale image recognition without normalization
Andy Brock, Soham De, Samuel L Smith, and Karen Simonyan · 2021
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Convit: Improving vision transformers with soft convolutional inductive biases
Stéphane D’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun · 2021
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Are labels always necessary for classifier accuracy evaluation?
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
Weijian Deng, Stephen Gould, and Liang Zheng · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Ratt: Leveraging unlabeled data to guarantee generalization
S. Garg, Sivaraman Balakrishnan, J. Zico Kolter, and Zachary Chase Lipton · 2021
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Partial success in closing the gap between human and machine vision
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2021
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Predicting with confidence on unseen distributions
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Wilds: A benchmark of in-the-wild distribution shifts
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Accuracy on the line: On the strong correlation between out-of-distribution and in-distribution generalization
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt§ · 2021
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Distributional generalization: A new kind of generalization, 2021
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Bottleneck transformers for visual recognition
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Going deeper with image transformers
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Scaling local self-attention for parameter efficient visual backbones
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Co-scale conv-attentional image transformers
Weijian Xu, Yifan Xu, Tyler Chang, and Zhuowen Tu · 2021
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Assessing generalization of SGD via disagreement
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Deconstructing distributions: A pointwise framework of learning
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On the nonlinear correlation of ml performance across data subpopulations
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A convnet for the 2020s
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Id and ood performance are sometimes inversely correlated on real-world datasets
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Predicting out-of-distribution error with the projection norm, 2022
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