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Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems.
Meta-neural networks that learn by learning
Devang K Naik and Richard J Mammone · 1992
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Multitask learning: A knowledge-based source of inductive bias1
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Design and evolution of modular neural network architectures
Bart LM Happel and Jacob MJ Murre · 1994
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Continual learning in reinforcement environments
Mark Bishop Ring · 1994
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On combining artificial neural nets
AMANDA J C SHARKEY · 1996
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A bayesian/information theoretic model of learning to learn via multiple task sampling
Jonathan Baxter · 1997
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Multitask learning
Rich Caruana · 1997
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Manipulation task primitives for composing robot skills
J Daniel Morrow and Pradeep K Khosla · 1997
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Modular neural network classifiers: A comparative study
Gasser Auda and Mohamed Kamel · 1998
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Gasser Auda and Mohamed Kamel · 1999
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Task clustering and gating for bayesian multitask learning
Bart Bakker and Tom Heskes · 2003
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, Fujie Huang, et al · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Modular deep belief networks that do not forget
Leo Pape, Faustino Gomez, Mark Ring, and Jürgen Schmidhuber · 2011
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Twenty years of mixture of experts
Seniha Esen Yuksel, Joseph N Wilson, and Paul D Gader · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser
Long Duong, Trevor Cohn, Steven Bird, and Paul Cook · 2015
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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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 · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Trace norm regularised deep multi-task learning
Yongxin Yang and Timothy M Hospedales · 2016
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Prefrontal–hippocampal interactions in episodic memory
Howard Eichenbaum · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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An overview of multi-task learning in deep neural networks
S Ruder · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Modular meta-learning in abstract graph networks for combinatorial generalization
Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, and Leslie P Kaelbling · 2018
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, and Remi Munos · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
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Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
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Hierarchical reinforcement learning with advantage-based auxiliary rewards
Siyuan Li, Rui Wang, Minxue Tang, and Chongjie Zhang · 2019
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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2019
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Mcp: Learning composable hierarchical control with multiplicative compositional policies
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B Tenenbaum, Tommi S Jaakkola, and Pulkit Agrawal · 2023
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Offline reinforcement learning via high-fidelity generative behavior modeling
Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, and Jun Zhu · 2023
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Look beneath the surface: Exploiting fundamental symmetry for sample-efficient offline rl
Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, and Li Jiang · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl · 2023
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OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Early vs late fusion in multimodal convolutional neural networks
Konrad Gadzicki, Razieh Khamsehashari, and Christoph Zetzsche · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Unclear: A straightforward method for continual reinforcement learning
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, and Stephen J Roberts · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Catch & carry: reusable neural controllers for vision-guided whole-body tasks
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess · 2020
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Idql: Implicit q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin · 2023
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When data geometry meets deep function: Generalizing offline reinforcement learning
Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, and Ya-Qin Zhang · 2023
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Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
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The ideal continual learner: An agent that never forgets
Liangzu Peng, Paris Giampouras, and René Vidal · 2023
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Combining parameter-efficient modules for task-level generalisation
Edoardo Maria Ponti, Alessandro Sordoni, Yoshua Bengio, and Siva Reddy · 2023
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Continual diffusion: Continual customization of text-to-image diffusion with c-lora
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin · 2023
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Efficient multi-task and transfer reinforcement learning with parameter-compositional framework
Lingfeng Sun, Haichao Zhang, Wei Xu, and Masayoshi Tomizuka · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2023
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Offline RL with no OOD actions: In-sample learning via implicit value regularization
Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xianyuan Zhan · 2023
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Compositional foundation models for hierarchical planning
Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie Kaelbling, Akash Srivastava, and Pulkit Agrawal · 2024
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Pessimistic value iteration for multi-task data sharing in offline reinforcement learning
Chenjia Bai, Lingxiao Wang, Jianye Hao, Zhuoran Yang, Bin Zhao, Zhen Wang, and Xuelong Li · 2024
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Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J. Fleet · 2024
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Abstract representations emerge in human hippocampal neurons during inference
Hristos S Courellis, Juri Minxha, Araceli R Cardenas, Daniel L Kimmel, Chrystal M Reed, Taufik A Valiante, C Daniel Salzman, Adam N Mamelak, Stefano Fusi, and Ueli Rutishauser · 2024
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Is mamba compatible with trajectory optimization in offline reinforcement learning?
Yang Dai, Oubo Ma, Longfei Zhang, Xingxing Liang, Shengchao Hu, Mengzhu Wang, Shouling Ji, Jincai Huang, and Li Shen · 2024
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Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Laura N Driscoll, Krishna Shenoy, and David Sussillo · 2024
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Position: Compositional generative modeling: A single model is not all you need
Yilun Du and Leslie Pack Kaelbling · 2024
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Single-task continual offline reinforcement learning
Sibo Gai and Donglin Wang · 2024
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Solving continual offline reinforcement learning with decision transformer
Kaixin Huang, Li Shen, Chen Zhao, Chun Yuan, and Dacheng Tao · 2024
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Unsupervised-to-online reinforcement learning
Junsu Kim, Seohong Park, and Sergey Levine · 2024
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Merging decision transformers: Weight averaging for forming multi-task policies
Daniel Lawson and Ahmed H Qureshi · 2024
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Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al · 2024
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Lora soups: Merging loras for practical skill composition tasks
Akshara Prabhakar, Yuanzhi Li, Karthik Narasimhan, Sham Kakade, Eran Malach, and Samy Jelassi · 2024
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Diffusion policy policy optimization
Allen Z Ren, Justin Lidard, Lars L Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz · 2024
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Learning to modulate pre-trained models in rl
Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, and Sepp Hochreiter · 2024
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State regularized policy optimization on data with dynamics shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, and Bo An · 2024
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Safe offline reinforcement learning with feasibility-guided diffusion model
Yinan Zheng, Jianxiong Li, Dongjie Yu, Yujie Yang, Shengbo Eben Li, Xianyuan Zhan, and Jingjing Liu · 2024
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Multi-lora composition for image generation
Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, and Weizhu Chen · 2024
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Diffusion-based planning for autonomous driving with flexible guidance
Yinan Zheng, Ruiming Liang, Kexin Zheng, Jinliang Zheng, Liyuan Mao, Jianxiong Li, Weihao Gu, Rui Ai, Shengbo Eben Li, Xianyuan Zhan, and Jingjing Liu · 2025
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