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2020 – today
- 2023
[j5]Colin Raffel:
Building Machine Learning Models Like Open Source Software. Commun. ACM 66(2): 38-40 (2023)- [c56]Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel:
Petals: Collaborative Inference and Fine-tuning of Large Models. ACL (demo) 2023: 558-568 - [c55]Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Leshem Choshen:
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning. ACL (1) 2023: 788-806 - [c54]Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, Colin Raffel:
Evaluating the Factual Consistency of Large Language Models Through News Summarization. ACL (Findings) 2023: 5220-5255 - [c53]Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel:
Crosslingual Generalization through Multitask Finetuning. ACL (1) 2023: 15991-16111 - [c52]Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch:
Bidirectional Language Models Are Also Few-shot Learners. ICLR 2023 - [c51]Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel:
Large Language Models Struggle to Learn Long-Tail Knowledge. ICML 2023: 15696-15707 - [c50]Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans, Vishal Baskaran, Tenghao Huang, Haokun Liu, Colin Raffel:
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. ICML 2023: 15708-15719 - [i64]Alon Albalak, Colin Raffel, William Yang Wang:
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data. CoRR abs/2302.00674 (2023) - [i63]Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen:
Knowledge is a Region in Weight Space for Fine-tuned Language Models. CoRR abs/2302.04863 (2023) - [i62]Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel:
Scaling Data-Constrained Language Models. CoRR abs/2305.16264 (2023) - [i61]Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, Mohit Bansal:
Resolving Interference When Merging Models. CoRR abs/2306.01708 (2023) - [i60]Mohammed Muqeeth, Haokun Liu, Colin Raffel:
Soft Merging of Experts with Adaptive Routing. CoRR abs/2306.03745 (2023) - [i59]Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans, Vishal Baskaran, Tenghao Huang, Haokun Liu, Colin Raffel:
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. CoRR abs/2306.04529 (2023) - [i58]Michael Matena, Colin Raffel:
NPEFF: Non-Negative Per-Example Fisher Factorization. CoRR abs/2310.04649 (2023) - [i57]Haikang Deng, Colin Raffel:
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model. CoRR abs/2310.09520 (2023) - [i56]Prateek Yadav, Leshem Choshen, Colin Raffel, Mohit Bansal:
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization. CoRR abs/2311.13171 (2023) - 2022
- [j4]Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel:
ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models. Trans. Assoc. Comput. Linguistics 10: 291-306 (2022) - [j3]Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus:
Emergent Abilities of Large Language Models. Trans. Mach. Learn. Res. 2022 (2022) - [c49]Diyi Yang, Ankur P. Parikh, Colin Raffel:
Learning with Limited Text Data. ACL (tutorial) 2022: 28-31 - [c48]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir R. Radev, Mike Tian-Jian Jiang, Alexander M. Rush:
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. ACL (demo) 2022: 93-104 - [c47]Teven Le Scao, Thomas Wang, Daniel Hesslow, Stas Bekman, M. Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy:
What Language Model to Train if You Have One Million GPU Hours? EMNLP (Findings) 2022: 765-782 - [c46]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush:
Multitask Prompted Training Enables Zero-Shot Task Generalization. ICLR 2022 - [c45]Nikhil Kandpal, Eric Wallace, Colin Raffel:
Deduplicating Training Data Mitigates Privacy Risks in Language Models. ICML 2022: 10697-10707 - [c44]Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel:
What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization? ICML 2022: 22964-22984 - [c43]Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel:
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. NeurIPS 2022 - [c42]Michael Matena, Colin Raffel:
Merging Models with Fisher-Weighted Averaging. NeurIPS 2022 - [c41]Michael Matena, Colin Raffel:
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks. NeurIPS 2022 - [c40]Zhenlin Xu, Marc Niethammer, Colin Raffel:
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language. NeurIPS 2022 - [e1]Alon Albalak, Chunting Zhou, Colin Raffel, Deepak Ramachandran, Sebastian Ruder, Xuezhe Ma:
Transfer Learning for Natural Language Processing Workshop, 03 December 2022, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research 203, PMLR 2022 [contents] - [i55]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Mike Tian-Jian Jiang, Alexander M. Rush:
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. CoRR abs/2202.01279 (2022) - [i54]Nikhil Kandpal, Eric Wallace, Colin Raffel:
Deduplicating Training Data Mitigates Privacy Risks in Language Models. CoRR abs/2202.06539 (2022) - [i53]Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo:
Scaling Up Models and Data with t5x and seqio. CoRR abs/2203.17189 (2022) - [i52]Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel:
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? CoRR abs/2204.05832 (2022) - [i51]Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel:
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. CoRR abs/2205.05638 (2022) - [i50]Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus:
Emergent Abilities of Large Language Models. CoRR abs/2206.07682 (2022) - [i49]Marcos V. Treviso, Tianchu Ji, Ji-Ung Lee, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Pedro Henrique Martins, André F. T. Martins, Peter A. Milder, Colin Raffel, Edwin Simpson, Noam Slonim, Niranjan Balasubramanian, Leon Derczynski, Roy Schwartz:
Efficient Methods for Natural Language Processing: A Survey. CoRR abs/2209.00099 (2022) - [i48]Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel:
Petals: Collaborative Inference and Fine-tuning of Large Models. CoRR abs/2209.01188 (2022) - [i47]Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch:
Bidirectional Language Models Are Also Few-shot Learners. CoRR abs/2209.14500 (2022) - [i46]Michael Matena, Colin Raffel:
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks. CoRR abs/2210.00176 (2022) - [i45]Zhenlin Xu, Marc Niethammer, Colin Raffel:
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language. CoRR abs/2210.00482 (2022) - [i44]Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, M. Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy:
What Language Model to Train if You Have One Million GPU Hours? CoRR abs/2210.15424 (2022) - [i43]Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel:
Crosslingual Generalization through Multitask Finetuning. CoRR abs/2211.01786 (2022) - [i42]Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilic, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. Rush, Stella Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Ortiz Suarez, Victor Sanh, Hugo Laurençon, Yacine Jernite, Julien Launay, Margaret Mitchell, Colin Raffel, Aaron Gokaslan, Adi Simhi, Aitor Soroa, Alham Fikri Aji, Amit Alfassy, Anna Rogers, Ariel Kreisberg Nitzav, Canwen Xu, Chenghao Mou, Chris Emezue, Christopher Klamm, Colin Leong, Daniel van Strien
, David Ifeoluwa Adelani, et al.:
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. CoRR abs/2211.05100 (2022) - [i41]Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel:
Large Language Models Struggle to Learn Long-Tail Knowledge. CoRR abs/2211.08411 (2022) - [i40]Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, Colin Raffel:
Evaluating the Factual Consistency of Large Language Models Through Summarization. CoRR abs/2211.08412 (2022) - [i39]Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen:
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning. CoRR abs/2212.01378 (2022) - 2021
- [c39]Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, Colin Raffel:
Improving and Simplifying Pattern Exploiting Training. EMNLP (1) 2021: 4980-4991 - [c38]Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Févry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel:
Do Transformer Modifications Transfer Across Implementations and Applications? EMNLP (1) 2021: 5758-5773 - [c37]Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, Marc Niethammer:
Robust and Generalizable Visual Representation Learning via Random Convolutions. ICLR 2021 - [c36]Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chi-wen Kan:
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition. KDD 2021: 2534-2542 - [c35]Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel:
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. NAACL-HLT 2021: 483-498 - [c34]Yi-Lin Sung, Varun Nair, Colin Raffel:
Training Neural Networks with Fixed Sparse Masks. NeurIPS 2021: 24193-24205 - [c33]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. USENIX Security Symposium 2021: 2633-2650 - [i38]Sewon Min, Jordan L. Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick S. H. Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Sejr Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih:
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. CoRR abs/2101.00133 (2021) - [i37]Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Févry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel:
Do Transformer Modifications Transfer Across Implementations and Applications? CoRR abs/2102.11972 (2021) - [i36]Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, Colin Raffel:
Improving and Simplifying Pattern Exploiting Training. CoRR abs/2103.11955 (2021) - [i35]Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel:
ByT5: Towards a token-free future with pre-trained byte-to-byte models. CoRR abs/2105.13626 (2021) - [i34]Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chih-wen Kan:
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition. CoRR abs/2106.03062 (2021) - [i33]Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal, Diyi Yang:
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP. CoRR abs/2106.07499 (2021) - [i32]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush:
Multitask Prompted Training Enables Zero-Shot Task Generalization. CoRR abs/2110.08207 (2021) - [i31]Michael Matena, Colin Raffel:
Merging Models with Fisher-Weighted Averaging. CoRR abs/2111.09832 (2021) - [i30]Yi-Lin Sung, Varun Nair, Colin Raffel:
Training Neural Networks with Fixed Sparse Masks. CoRR abs/2111.09839 (2021) - [i29]Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, Colin Raffel, Manan Dey, Matthias Gallé, Arun Raja, Chenglei Si, Wilson Y. Lee, Benoît Sagot, Samson Tan:
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP. CoRR abs/2112.10508 (2021) - 2020
- [j2]Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu:
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21: 140:1-140:67 (2020) - [c32]Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, Colin Raffel:
Learning-based Memory Allocation for C++ Server Workloads. ASPLOS 2020: 541-556 - [c31]Adam Roberts, Colin Raffel, Noam Shazeer:
How Much Knowledge Can You Pack Into the Parameters of a Language Model? EMNLP (1) 2020: 5418-5426 - [c30]David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel:
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. ICLR 2020 - [c29]Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. ICLR 2020 - [c28]Sewon Min, Jordan L. Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick S. H. Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Sejr Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih:
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. NeurIPS (Competition and Demos) 2020: 86-111 - [c27]Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena:
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples. NeurIPS 2020 - [c26]Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. NeurIPS 2020 - [i28]Ishaan Gulrajani, Colin Raffel, Luke Metz:
Towards GAN Benchmarks Which Require Generalization. CoRR abs/2001.03653 (2020) - [i27]Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. CoRR abs/2001.07685 (2020) - [i26]Samarth Sinha, Anirudh Goyal, Colin Raffel, Augustus Odena:
Top-K Training of GANs: Improving Generators by Making Critics Less Critical. CoRR abs/2002.06224 (2020) - [i25]Yao Qin, Nicholas Frosst, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Deflecting Adversarial Attacks. CoRR abs/2002.07405 (2020) - [i24]Adam Roberts, Colin Raffel, Noam Shazeer:
How Much Knowledge Can You Pack Into the Parameters of a Language Model? CoRR abs/2002.08910 (2020) - [i23]Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, Karishma Malkan:
WT5?! Training Text-to-Text Models to Explain their Predictions. CoRR abs/2004.14546 (2020) - [i22]Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel:
mT5: A massively multilingual pre-trained text-to-text transformer. CoRR abs/2010.11934 (2020) - [i21]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. CoRR abs/2012.07805 (2020)
2010 – 2019
- 2019
- [c25]Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel:
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation. ACL (1) 2019: 1313-1323 - [c24]David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow:
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. ICLR (Poster) 2019 - [c23]Ishaan Gulrajani, Colin Raffel, Luke Metz:
Towards GAN Benchmarks Which Require Generalization. ICLR (Poster) 2019 - [c22]Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. ICML 2019: 5231-5240 - [c21]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS 2019: 5050-5060 - [i20]Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia Xu Chen, Ye Jia, Anjuli Kannan, Tara N. Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George F. Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel Bacchiani, Thomas B. Jablin, Robert Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon:
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. CoRR abs/1902.08295 (2019) - [i19]Yao Qin, Nicholas Carlini, Ian J. Goodfellow, Garrison W. Cottrell, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. CoRR abs/1903.10346 (2019) - [i18]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR abs/1905.02249 (2019) - [i17]Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel:
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation. CoRR abs/1906.05218 (2019) - [i16]Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. CoRR abs/1907.02957 (2019) - [i15]Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu:
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. CoRR abs/1910.10683 (2019) - [i14]David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel:
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. CoRR abs/1911.09785 (2019) - 2018
- [j1]Danny C. Price, Sebastien Celles, Pieter T. Eendebak, Michael M. McKerns, Eben M. Olson, Colin Raffel, Bairen Yi:
Hickle: A HDF5-based python pickle replacement. J. Open Source Softw. 3(32): 1115 (2018) - [c20]Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. ICASSP 2018: 5799-5803 - [c19]Jacob Buckman, Aurko Roy, Colin Raffel, Ian J. Goodfellow:
Thermometer Encoding: One Hot Way To Resist Adversarial Examples. ICLR (Poster) 2018 - [c18]Chung-Cheng Chiu, Colin Raffel:
Monotonic Chunkwise Attention. ICLR (Poster) 2018 - [c17]Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Semi-Supervised Learning Algorithms. ICLR (Workshop) 2018 - [c16]Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow:
Is Generator Conditioning Causally Related to GAN Performance? ICML 2018: 3846-3855 - [c15]Adam Roberts, Jesse H. Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck:
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. ICML 2018: 4361-4370 - [c14]Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse H. Engel, Sageev Oore, Douglas Eck:
Onsets and Frames: Dual-Objective Piano Transcription. ISMIR 2018: 50-57 - [c13]Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. NeurIPS 2018: 3239-3250 - [i13]Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow:
Is Generator Conditioning Causally Related to GAN Performance? CoRR abs/1802.08768 (2018) - [i12]Adam Roberts, Jesse H. Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck:
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. CoRR abs/1803.05428 (2018) - [i11]Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. CoRR abs/1804.09170 (2018) - [i10]Ian Simon, Adam Roberts, Colin Raffel, Jesse H. Engel, Curtis Hawthorne, Douglas Eck:
Learning a Latent Space of Multitrack Measures. CoRR abs/1806.00195 (2018) - [i9]David Berthelot, Colin Raffel, Aurko Roy, Ian J. Goodfellow:
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer. CoRR abs/1807.07543 (2018) - 2017
- [c12]Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein:
Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) 2017 - [c11]Colin Raffel, Dieterich Lawson:
Training a Subsampling Mechanism in Expectation. ICLR (Workshop) 2017 - [c10]