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Transformer architecture has shown impressive performance in multiple research domains and has become the backbone of many neural network models.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Globally optimal gradient descent for a convnet with gaussian inputs
Alon Brutzkus and Amir Globerson · 2017
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An analytical formula of population gradient for two-layered relu network and its applications in convergence and critical point analysis
Yuandong Tian · 2017
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Learning relus via gradient descent
Mahdi Soltanolkotabi · 2017
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When is a convolutional filter easy to learn?
Simon S Du, Jason D Lee, and Yuandong Tian · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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A convergence analysis of gradient descent for deep linear neural networks
Sanjeev Arora, Nadav Cohen, Noah Golowich, and Wei Hu · 2018
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Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Peter Bartlett, Dave Helmbold, and Philip Long · 2018
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Learning one convolutional layer with overlapping patches
Surbhi Goel, Adam Klivans, and Raghu Meka · 2018
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Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima
Simon Du, Jason Lee, Yuandong Tian, Aarti Singh, and Barnabas Poczos · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Gradient descent provably optimizes over-parameterized neural networks, 2018
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
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On the global convergence of gradient descent for over-parameterized models using optimal transport
Lenaic Chizat and Francis Bach · 2018
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A mean field view of the landscape of two-layer neural networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M Dai, Matthew D Hoffman, Monica Dinculescu, and Douglas Eck · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Transformer-transducer: End-to-end speech recognition with self-attention
Ching-Feng Yeh, Jay Mahadeokar, Kaustubh Kalgaonkar, Yongqiang Wang, Duc Le, Mahaveer Jain, Kjell Schubert, Christian Fuegen, and Michael L Seltzer · 2019
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Are transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2019
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Toward understanding the importance of noise in training neural networks
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, and Tuo Zhao · 2019
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Towards understanding the importance of shortcut connections in residual networks
Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S Du, Enlu Zhou, and Tuo Zhao · 2019
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On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
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Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 2022
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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, et al · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Ul2: Unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, et al · 2022
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Statistically meaningful approximation: a case study on approximating turing machines with transformers
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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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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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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
Cited alongside, same era.
On the ability and limitations of transformers to recognize formal languages
Satwik Bhattamishra, Kabir Ahuja, and Navin Goyal · 2020
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On the computational power of transformers and its implications in sequence modeling
Satwik Bhattamishra, Arkil Patel, and Navin Goyal · 2020
Cited alongside, same era.
Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks
Samet Oymak and Mahdi Soltanolkotabi · 2020
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Colin Wei, Yining Chen, and Tengyu Ma · 2022
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Vision transformers provably learn spatial structure
Samy Jelassi, Michael Sander, and Yuanzhi Li · 2022
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Inductive biases and variable creation in self-attention mechanisms
Benjamin L Edelman, Surbhi Goel, Sham Kakade, and Cyril Zhang · 2022
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
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Hidden progress in deep learning: Sgd learns parities near the computational limit
Boaz Barak, Benjamin Edelman, Surbhi Goel, Sham Kakade, Eran Malach, and Cyril Zhang · 2022
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Tensor programs v: Tuning large neural networks via zero-shot hyperparameter transfer
Greg Yang, Edward J Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, and Jianfeng Gao · 2022
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Understanding the role of nonlinearity in training dynamics of contrastive learning
Yuandong Tian · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Gpt-4 technical report, 2023
OpenAI · 2023
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Do transformers parse while predicting the masked word?
Haoyu Zhao, Abhishek Panigrahi, Rong Ge, and Sanjeev Arora · 2023
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How do transformers learn topic structure: Towards a mechanistic understanding
Yuchen Li, Yuanzhi Li, and Andrej Risteski · 2023
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A theoretical understanding of shallow vision transformers: Learning, generalization, and sample complexity
Hongkang Li, Meng Wang, Sijia Liu, and Pin-Yu Chen · 2023
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On the role of attention in prompt-tuning
Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, and Christos Thrampoulidis · 2023
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Max-margin token selection in attention mechanism
Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, and Samet Oymak · 2023
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Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
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The closeness of in-context learning and weight shifting for softmax regression
Shuai Li, Zhao Song, Yu Xia, Tong Yu, and Tianyi Zhou · 2023
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Over-parameterization exponentially slows down gradient descent for learning a single neuron
Weihang Xu and Simon S Du · 2023
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Understanding the role of nonlinearity in training dynamics of contrastive learning
Yuandong Tian · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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The impact of positional encoding on length generalization in transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, and Siva Reddy · 2023
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