Fetching the paper…
Reading the bibliography…
Meta-learning is a practical learning paradigm to transfer skills across tasks from a few examples.
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 · 1901
Earlier work this paper cites.
The theory of the market economy
Heinrich Von Stackelberg and Stackelberg Heinrich Von. 1952 · 1952
Earlier work this paper cites.
Convergence of stochastic processes
David Pollard. 1984 · 1984
Earlier work this paper cites.
A local based approach for path planning of manipulators with a high number of degrees of freedom. In Proceedings. 1987 IEEE international conference on robotics and automation , Vol. 4. IEEE, 1152–1159
Bernard Faverjon and Pierre Tournassoud. 1987 · 1987
Earlier work this paper cites.
Nash equilibrium
David M Kreps. 1989 · 1989
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Introduction to reinforcement learning . Vol. 135
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Jacques Hadamard: a universal mathematician
Vladimir G Maz’ya and Tatyana O Shaposhnikova. 1999 · 1999
Earlier work this paper cites.
The nature of statistical learning theory
Vladimir Vapnik. 1999 · 1999
Earlier work this paper cites.
Learning to learn using gradient descent. In International conference on artificial neural networks . Springer, 87–94
Sepp Hochreiter, A Steven Younger, and Peter R Conwell. 2001 · 2001
Earlier work this paper cites.
An introduction to game theory . Vol. 3
Martin J Osborne et al · 2004
Earlier work this paper cites.
Gradient estimation
Michael C Fu. 2006 · 2006
Earlier work this paper cites.
Curriculum learning. In Proceedings of the 26th annual international conference on machine learning . 41–48
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Learning bounds for importance weighting
Corinna Cortes, Yishay Mansour, and Mehryar Mohri. 2010 · 2010
Earlier work this paper cites.
Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 5026–5033
Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012 · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning . PMLR, 1278–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
Distributionally robust convex optimization
Wolfram Wiesemann, Daniel Kuhn, and Melvyn Sim. 2014 · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Global indices for kinematic and force transmission performance in parallel robots
Kevin C Olds. 2015 · 2015
Earlier work this paper cites.
Variational inference with normalizing flows. In International conference on machine learning . PMLR, 1530–1538
Danilo Rezende and Shakir Mohamed. 2015 · 2015
Earlier work this paper cites.
Trust region policy optimization. In International conference on machine learning . PMLR, 1889–1897
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. 2015 · 2015
Earlier work this paper cites.
Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le. 2016 · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks. In International conference on machine learning . PMLR, 1842–1850
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016 · 2016
Earlier work this paper cites.
High-Dimensional Continuous Control Using Generalized Advantage Estimation. In International Conference on Learning Representations
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Meta networks. In International Conference on Machine Learning . PMLR, 2554–2563
Tsendsuren Munkhdalai and Hong Yu. 2017 · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Earlier work this paper cites.
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh. 2018b · 2018
Earlier work this paper cites.
Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths. 2018 · 2018
Cited alongside, same era.
Attentive Neural Processes. In International Conference on Learning Representations
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh. 2018 · 2018
Cited alongside, same era.
Security analysis and enhancement of model compressed deep learning systems under adversarial attacks. In 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC) . IEEE, 721–726
Qi Liu, Tao Liu, Zihao Liu, Yanzhi Wang, Yier Jin, and Wujie Wen. 2018 · 2018
Cited alongside, same era.
Meta-Learning with Latent Embedding Optimization. In International Conference on Learning Representations
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell. 2018 · 2018
Cited alongside, same era.
Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2021 · 2021
Later among the works it cites.
Local descriptor-based multi-prototype network for few-shot learning
Hongwei Huang, Zhangkai Wu, Wenbin Li, Jing Huo, and Yang Gao. 2021 · 2021
Later among the works it cites.
Understanding continual learning settings with data distribution drift analysis
Timothée Lesort, Massimo Caccia, and Irina Rish. 2021 · 2021
Later among the works it cites.
Dreca: A general task augmentation strategy for few-shot natural language inference. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1113–1125
Shikhar Murty, Tatsunori B Hashimoto, and Christopher D Manning. 2021 · 2021
Later among the works it cites.
Data augmentation for meta-learning. In International Conference on Machine Learning . PMLR, 8152–8161
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kelsey Allen, Evan Shelhamer, Hanul Shin, and Joshua Tenenbaum. 2019 · 2019
Cited alongside, same era.
Robust few-shot learning with adversarially queried meta-learners
Micah Goldblum, Liam Fowl, and Tom Goldstein. 2019 · 2019
Cited alongside, same era.
Convolutional Conditional Neural Processes. In International Conference on Learning Representations
Jonathan Gordon, Wessel P Bruinsma, Andrew YK Foong, James Requeima, Yann Dubois, and Richard E Turner. 2019 · 2019
Cited alongside, same era.
Minmax optimization: Stable limit points of gradient descent ascent are locally optimal
Chi Jin, Praneeth Netrapalli, and Michael I Jordan. 2019 · 2019
Cited alongside, same era.
Revisiting local descriptor based image-to-class measure for few-shot learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 7260–7268
Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, and Jiebo Luo. 2019 · 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, et al · 2019
Cited alongside, same era.
Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine. 2019 · 2019
Cited alongside, same era.
Fast and flexible multi-task classification using conditional neural adaptive processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, and Richard E Turner. 2019 · 2019
Cited alongside, same era.
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein. 2021 · 2021
Later among the works it cites.
Scenegen: Learning to generate realistic traffic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 892–901
Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun. 2021 · 2021
Later among the works it cites.
Mining latent classes for few-shot segmentation. In Proceedings of the IEEE/CVF international conference on computer vision . 8721–8730
Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao. 2021 · 2021
Later among the works it cites.
Meta-learning with an adaptive task scheduler
Huaxiu Yao, Yu Wang, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, and Chelsea Finn. 2021b · 2021
Later among the works it cites.
Adaptive risk minimization: Learning to adapt to domain shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn. 2021 · 2021
Later among the works it cites.
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. In International Conference on Learning Representations
Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, and Masashi Sugiyama. 2022 · 2022
Later among the works it cites.
Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. 2022 · 2022
Later among the works it cites.
Set-based meta-interpolation for few-task meta-learning
Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, and Sung Ju Hwang. 2022 · 2022
Later among the works it cites.
Defensive Few-Shot Learning
Wenbin Li, Lei Wang, Xingxing Zhang, Lei Qi, Jing Huo, Yang Gao, and Jiebo Luo. 2022 · 2022
Later among the works it cites.
Evolving curricula with regret-based environment design. In International Conference on Machine Learning . PMLR, 17473–17498
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, and Tim Rocktäschel. 2022 · 2022
Later among the works it cites.
Generating useful accident-prone driving scenarios via a learned traffic prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 17305–17315
Davis Rempe, Jonah Philion, Leonidas J Guibas, Sanja Fidler, and Or Litany. 2022 · 2022
Later among the works it cites.
Efficient distributionally robust Bayesian optimization with worst-case sensitivity. In International Conference on Machine Learning . PMLR, 21180–21204
Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, and Bryan Kian Hsiang Low. 2022 · 2022
Later among the works it cites.
Learning expressive meta-representations with mixture of expert neural processes. In Advances in neural information processing systems
Qi Wang and Herke van Hoof. 2022 · 2022
Later among the works it cites.
Model-based meta reinforcement learning using graph structured surrogate models and amortized policy search. In International Conference on Machine Learning . PMLR, 23055–23077
Qi Wang and Herke Van Hoof. 2022 · 2022
Later among the works it cites.
Adversarial task up-sampling for meta-learning
Yichen Wu, Long-Kai Huang, and Ying Wei. 2022 · 2022
Later among the works it cites.
Learning to generalize across domains on single test samples
Zehao Xiao, Xiantong Zhen, Ling Shao, and Cees GM Snoek. 2022 · 2022
Later among the works it cites.
Sequential Modeling Enables Scalable Learning for Large Vision Models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros. 2023 · 2023
Later among the works it cites.
Supported trust region optimization for offline reinforcement learning. In International Conference on Machine Learning . PMLR, 23829–23851
Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, and Xiangyang Ji. 2023 · 2023
Later among the works it cites.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
Later among the works it cites.
Episodic Multi-Task Learning with Heterogeneous Neural Processes
Jiayi Shen, Xiantong Zhen, Qi Wang, and Marcel Worring. 2023 · 2023
Later among the works it cites.
Large-scale generative simulation artificial intelligence: The next hotspot
Qi Wang, Yanghe Feng, Jincai Huang, Yiqin Lv, Zheng Xie, and Xiaoshan Gao. 2023b · 2023
Later among the works it cites.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Qi Wang, Yiqin Lv, Yanghe Feng, Zheng Xie, and Jincai Huang. 2023c · 2023
Later among the works it cites.
Energy-based test sample adaptation for domain generalization
Zehao Xiao, Xiantong Zhen, Shengcai Liao, and Cees GM Snoek. 2023 · 2023
Later among the works it cites.
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?. In Neural Information Processing Systems
Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, and Bo Han. 2024 · 2024
Closest in time.
Supported value regularization for offline reinforcement learning
Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, and Xiangyang Ji. 2024 · 2024
Closest in time.
Any-Shift Prompting for Generalization over Distributions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 13849–13860
Zehao Xiao, Jiayi Shen, Mohammad Mahdi Derakhshani, Shengcai Liao, and Cees GM Snoek. 2024 · 2024
Closest in time.