Fetching the paper…
Reading the bibliography…
Sparse mixture of expert architectures (MoEs) scale model capacity without significant increases in training or inference costs.
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.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Conditional computation in neural networks for faster models
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup · 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.
Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
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, et al · 2018
Earlier work this paper cites.
How much attention do you need? a granular analysis of neural machine translation architectures
Tobias Domhan · 2018
Earlier work this paper cites.
Condconv: Conditionally parameterized convolutions for efficient inference
Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam · 2019
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 · 2020
Earlier work this paper cites.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
Cited alongside, same era.
Conditional convolutions for instance segmentation
Zhi Tian, Chunhua Shen, and Hao Chen · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Base layers: Simplifying training of large, sparse models
Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, and Luke Zettlemoyer · 2021
Cited alongside, same era.
Unified scaling laws for routed language models
Aidan Clark, Diego De Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, et al · 2022
Later among the works it cites.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Later among the works it cites.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Later among the works it cites.
Sparsity-constrained optimal transport
Tianlin Liu, Joan Puigcerver, and Mathieu Blondel · 2022
Later among the works it cites.
Multimodal contrastive learning with limoe: the language-image mixture of experts
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
Cited alongside, same era.
Hash layers for large sparse models
Stephen Roller, Sainbayar Sukhbaatar, Jason Weston, et al · 2021
Cited alongside, same era.
Tokenlearner: What can 8 learned tokens do for images and videos?
Michael S Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, and Anelia Angelova · 2021
Cited alongside, same era.
LAION-400m: Open dataset of CLIP-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Cited alongside, same era.
Pali: A jointly-scaled multilingual language-image model
Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, et al · 2022
Cited alongside, same era.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer
Cited in the paper.
Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer
Cited in the paper.
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, and Neil Houlsby · 2022
Later among the works it cites.
Learning to merge tokens in vision transformers
Cedric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, and Carlos Riquelme · 2022
Later among the works it cites.
Multimodal token fusion for vision transformers
Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, and Yunhe Wang · 2022
Later among the works it cites.
Mixture-of-experts with expert choice routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Quoc V Le, James Laudon, et al · 2022
Later among the works it cites.
Soft merging of experts with adaptive routing, 2023
Mohammed Muqeeth, Haokun Liu, and Colin Raffel · 2023
Closest in time.