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In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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A simple and effective method for removal of hidden units and weights
Masafumi Hagiwara · 1994
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Automated flower classification over a large number of classes
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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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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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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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
K. Simonyan and Andrew Zisserman · 2014
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 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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Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
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Less is more: Towards compact cnns
Hao Zhou, José Manuel Álvarez, and Fatih Murat Porikli · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Adversarial reprogramming of neural networks
Gamaleldin F Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2018
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Impact of low-bitwidth quantization on the adversarial robustness for embedded neural networks
Rémi Bernhard, Pierre-Alain Moellic, and Jean-Max Dutertre · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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What do compressed deep neural networks forget?
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome · 2019
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Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
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Sparsity winning twice: Better robust generalization from more efficient training
Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, and Zhangyang Wang · 2022
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Llm.int8(): 8-bit matrix multiplication for transformers at scale, 2022
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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A review of sparse expert models in deep learning
William Fedus, Jeff Dean, and Barret Zoph · 2022
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X-risk analysis for ai research
Dan Hendrycks and Mantas Mazeika · 2022
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How well do sparse imagenet models transfer?
Eugenia Iofinova, Alexandra Peste, Mark Kurtz, and Dan Alistarh · 2022
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James Gilles · 2020
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Reliability evaluation of compressed deep learning models
Brunno F Goldstein, Sudarshan Srinivasan, Dipankar Das, Kunal Banerjee, Leandro Santiago, Victor C Ferreira, Alexandre S Nery, Sandip Kundu, and Felipe MG França · 2020
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Characterising bias in compressed models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton · 2020
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Training data-efficient image transformers & distillation through attention. arxiv 2020
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources
Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho · 2020
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Calibrate and prune: Improving reliability of lottery tickets through prediction calibration
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Caltech 101, 2022
Fei-Fei Li, Marco Andreeto, Marc’Aurelio Ranzato, and Pietro Perona · 2022
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Calibration Analysis of Structured Pruning Methods and Cascade Classifier Design Based on Pruned Networks
Ruofeng Li · 2022
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Cross-modal adversarial reprogramming
Paarth Neekhara, Shehzeen Hussain, Jinglong Du, Shlomo Dubnov, Farinaz Koushanfar, and Julian McAuley · 2022
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Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu · 2022
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Zeroquant: Efficient and affordable post-training quantization for large-scale transformers, 2022
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, and Yuxiong He · 2022
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Fairness reprogramming
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Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
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