Fetching the paper…
Reading the bibliography…
We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
Earlier work this paper cites.
Introduction to online convex optimization
Elad Hazan · 1909
Earlier work this paper cites.
Inadmissibility of the usual estimator for the mean of a multivariate normal distribution
Charles Stein · 1956
Earlier work this paper cites.
On-line learning and stochastic approximations
Léon Bottou · 1998
Earlier work this paper cites.
A statistical study on on-line learning
Noboru Murata · 1998
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Felix Gers, Jürgen Schmidhuber, and Fred Cummins · 2000
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn Treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Stochastic gradient tricks
Léon Bottou · 2012
Earlier work this paper cites.
Lecture 6.5 - RMSProp, COURSERA: Neural networks for machine learning
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Stochastic first- and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Cited alongside, same era.
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Later among the works it cites.
Mogrifier lstm
Gábor Melis, Tomáš Kočiský, and Phil Blunsom · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Later among the works it cites.
Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
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.
Scaling SGD batch size to 32k for imagenet training
Yang You, Igor Gitman, and Boris Ginsburg · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Shampoo: Preconditioned stochastic tensor optimization
Vineet Gupta, Tomer Koren, and Yoram Singer · 2018
Cited alongside, same era.
The power of interpolation: Understanding the effectiveness of SGD in modern over-parametrized learning
Siyuan Ma, Raef Bassily, and Mikhail Belkin · 2018
Cited alongside, same era.
On the convergence of adam and beyond
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, and et al · 2021
Later among the works it cites.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu · 2021
Later among the works it cites.
Ran Tian, Joshua Maynez, and Ankur P. Parikh · 2021
Later among the works it cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
Closest in time.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
Closest in time.
Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Zou · 2022
Closest in time.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2074
Closest in time.