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Multi-Exit models (MEMs) use an early-exit strategy to improve the accuracy and efficiency of deep neural networks (DNNs) by allowing samples to exit the network before the last layer.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Low-rank approximations for conditional feedforward computation in deep neural networks
Andrew Davis and Itamar Arel · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2016
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Deciding how to decide: Dynamic routing in artificial neural networks
Mason McGill and Pietro Perona · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, Riccardo Volpi, and John Duchi · 2017
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Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
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Convolutional networks with adaptive inference graphs
Andreas Veit and Serge Belongie · 2018
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Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
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Interpreting deep visual representations via network dissection
Bolei Zhou, David Bau, Aude Oliva, and Antonio Torralba · 2018
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How can we be so dense? the benefits of using highly sparse representations
Subutai Ahmad and Luiz Scheinkman · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Cited alongside, same era.
Shallow-deep networks: Understanding and mitigating network overthinking
Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitras · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Revisiting batch normalization for improving corruption robustness
Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon · 2021
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A winning hand: Compressing deep networks can improve out-of-distribution robustness
James Diffenderfer, Brian Bartoldson, Shreya Chaganti, Jize Zhang, and Bhavya Kailkhura · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
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Improving anytime prediction with parallel cascaded networks and a temporal-difference loss
Michael Iuzzolino, Michael C Mozer, and Samy Bengio · 2021
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Adaptive inference through early-exit networks: Design, challenges and directions
Stefanos Laskaridis, Alexandros Kouris, and Nicholas D Lane · 2021
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Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
Cited alongside, same era.
Object detection with deep learning: A review
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, and Xindong Wu · 2019
Cited alongside, same era.
Anomalous example detection in deep learning: A survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K Varshney, and Dawn Song · 2020
Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference
Sanghyun Hong, Yiğitcan Kaya, Ionuţ-Vlad Modoranu, and Tudor Dumitraş · 2020
Cited alongside, same era.
Fastbert: a self-distilling bert with adaptive inference time
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Haotang Deng, and Qi Ju · 2020
Cited alongside, same era.
Mood: Multi-level out-of-distribution detection
Ziqian Lin, Sreya Dutta Roy, and Yixuan Li · 2021
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Ttt++: When does self-supervised test-time training fail or thrive?
Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi · 2021
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Anytime dense prediction with confidence adaptivity
Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang, Trevor Darrell, and Evan Shelhamer · 2021
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How robust are randomized smoothing based defenses to data poisoning?
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2021
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Understanding the limits of unsupervised domain adaptation via data poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2021
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Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, and Z Morley Mao · 2021
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Early exiting with ensemble internal classifiers
Tianxiang Sun, Yunhua Zhou, Xiangyang Liu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, and Xipeng Qiu · 2021
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Zero time waste: Recycling predictions in early exit neural networks
Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor T Podolak, Jacek Tabor, Marek Śmieja, and Tomasz Trzcinski · 2021
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Neural mean discrepancy for efficient out-of-distribution detection
Xin Dong, Junfeng Guo, Ang Li, Wei-Te Ting, Cong Liu, and HT Kung · 2022
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Head2toe: Utilizing intermediate representations for better transfer learning
Utku Evci, Vincent Dumoulin, Hugo Larochelle, and Michael C Mozer · 2022
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Hubert-ee: Early exiting hubert for efficient speech recognition
Ji Won Yoon, Beom Jun Woo, and Nam Soo Kim · 2022
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