Fetching the paper…
Reading the bibliography…
The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
Earlier work this paper cites.
Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
Parallelizing linear recurrent neural nets over sequence length
Eric Martin and Chris Cundy · 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.
Adversarial reprogramming of neural networks
Gamaleldin F Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
Earlier work this paper cites.
Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2018
Earlier work this paper cites.
Deep transfer learning for art classification problems
Matthia Sabatelli, Mike Kestemont, Walter Daelemans, and Pierre Geurts · 2018
Earlier work this paper cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Earlier work this paper cites.
Explicit inductive bias for transfer learning with convolutional networks
LI Xuhong, Yves Grandvalet, and Franck Davoine · 2018
Earlier work this paper cites.
Spottune: transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
Towards understanding the transferability of deep representations
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Pytorch image models
Ross Wightman · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
Cited alongside, same era.
Adafilter: Adaptive filter fine-tuning for deep transfer learning
Yunhui Guo, Yandong Li, Liqiang Wang, and Tajana Rosing · 2020
Cited alongside, same era.
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
Cited alongside, same era.
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.
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
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Later among the works it cites.
Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
Later among the works it cites.
Hugo Touvron, Matthieu Cord, and Herve Jegou · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2020
Cited alongside, same era.
Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources
Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho · 2020
Cited alongside, same era.
Learning to transfer learn: Reinforcement learning-based selection for adaptive transfer learning
Linchao Zhu, Sercan Ö Arık, Yi Yang, and Tomas Pfister · 2020
Cited alongside, same era.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
Cited alongside, same era.
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, et al · 2021
Cited alongside, same era.
An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Junxiong Wang, Jing Nathan Yan, Albert Gu, and Alexander M Rush · 2022
Later among the works it cites.
Fairness reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, and Shiyu Chang · 2022
Later among the works it cites.
Understanding and improving visual prompting: A label-mapping perspective
Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, and Sijia Liu · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces, 2023
Albert Gu and Tri Dao · 2023
Later among the works it cites.
Gateloop: Fully data-controlled linear recurrence for sequence modeling, 2023
Tobias Katsch · 2023
Later among the works it cites.
Structured state space models for in-context reinforcement learning
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, and Feryal Behbahani · 2023
Later among the works it cites.
Diganta Misra, Agam Goyal, Bharat Runwal, and Pin Yu Chen · 2023
Later among the works it cites.
Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al · 2023
Later among the works it cites.
Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
Later among the works it cites.
Simplified state space layers for sequence modeling, 2023
Jimmy T. H. Smith, Andrew Warrington, and Scott W. Linderman · 2023
Later among the works it cites.
Retentive network: A successor to transformer for large language models, 2023
Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Later among the works it cites.
Reprogramming pretrained language models for protein sequence representation learning
Ria Vinod, Pin-Yu Chen, and Payel Das · 2023
Later among the works it cites.
Shida Wang and Beichen Xue · 2023
Later among the works it cites.
Online learning of long-range dependencies, 2023
Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika, and João Sacramento · 2023
Later among the works it cites.
The hidden attention of mamba models
Ameen Ali, Itamar Zimerman, and Lior Wolf · 2024
Closest in time.
Theoretical foundations of deep selective state-space models
Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, and Terry Lyons · 2024
Closest in time.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
Closest in time.