Fetching the paper…
Reading the bibliography…
Transformer models have been widely adopted in various domains over the last years, and especially large language models have advanced the field of AI significantly.
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.
1.1 computing’s energy problem (and what we can do about it)
M. Horowitz · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Earlier work this paper cites.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Learning sparse neural networks through
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Post-training 4-bit quantization of convolution networks for rapid-deployment
Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry · 2018
Earlier work this paper cites.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Earlier work this paper cites.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
Earlier work this paper cites.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
Earlier work this paper cites.
Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev · 2019
Earlier work this paper cites.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Revealing the dark secrets of BERT
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky · 2019
Earlier work this paper cites.
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 · 2019
Cited alongside, same era.
Same, same but different: Recovering neural network quantization error through weight factorization
Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman · 2019
Cited alongside, same era.
Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
Later among the works it cites.
Understanding and overcoming the challenges of efficient transformer quantization
Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort · 2021
Later among the works it cites.
A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2021
Later among the works it cites.
I-bert: Integer-only bert quantization
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer · 2021
Later among the works it cites.
Bert busters: Outlier dimensions that disrupt transformers
Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers, and Anna Rumshisky · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Ofir Zafrir, Guy Boudoukh, Peter Izsak, and Moshe Wasserblat · 2019
Cited alongside, same era.
Improving neural network quantization without retraining using outlier channel splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang · 2019
Cited alongside, same era.
Lsq+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, and Nojun Kwak · 2020
Cited alongside, same era.
Later among the works it cites.
Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
A white paper on neural network quantization
Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Blankevoort Tijmen · 2021
Later among the works it cites.
How do BERT embeddings organize linguistic knowledge?
Giovanni Puccetti, Alessio Miaschi, and Felice Dell’Orletta · 2021
Later among the works it cites.
Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
Later among the works it cites.
The case for 4-bit precision: k-bit inference scaling laws
Tim Dettmers and Luke Zettlemoyer · 2022
Later among the works it cites.
Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Later among the works it cites.
Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
Later among the works it cites.
Accelerate: Training and inference at scale made simple, efficient and adaptable
Sylvain Gugger, Lysandre Debu, Thomas Wolf, Philipp Schmid, Zachary Mueller, and Sourab Mangrulkar · 2022
Later among the works it cites.
Minsoo Kim, Sihwa Lee, Sukjin Hong, Du-Seong Chang, and Jungwook Choi · 2022
Later among the works it cites.
Deit iii: Revenge of the vit
Hugo Touvron, Matthieu Cord, and Hervé Jégou · 2022
Later among the works it cites.
Outlier suppression: Pushing the limit of low-bit transformer language models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu · 2022
Later among the works it cites.
Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2022
Later among the works it cites.
Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, and Yuxiong He · 2022
Later among the works it cites.
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
Later among the works it cites.
Awq: Activation-aware weight quantization for llm compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
Closest in time.
Fp8 versus int8 for efficient deep learning inference
Mart van Baalen, Andrey Kuzmin, Suparna S Nair, Yuwei Ren, Eric Mahurin, Chirag Patel, Sundar Subramanian, Sanghyuk Lee, Markus Nagel, Joseph Soriaga, and Tijmen Blankevoort · 2023
Closest in time.
Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong Liu · 2023
Closest in time.
Understanding int4 quantization for transformer models: Latency speedup, composability, and failure cases
Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, and Yuxiong He · 2023
Closest in time.