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Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks.
A markovian decision process
Richard Bellman · 1957
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Reinforcement learning for spoken dialogue systems
Satinder Singh, Michael Kearns, Diane Litman, and Marilyn Walker · 1999
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky · 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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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ-Skerry Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Ye Jia, Fei Ren, and Rif A Saurous · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 2019
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A self regularized non-monotonic activation function [j]
Misra D Mish · 2019
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Reinforcement learning upside down: Don’t predict rewards–just map them to actions
Juergen Schmidhuber · 2019
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Training agents using upside-down reinforcement learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, and Jürgen Schmidhuber · 2019
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Improving exploration in soft-actor-critic with normalizing flows policies
Patrick Nadeem Ward, Ariella Smofsky, and Avishek Joey Bose · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 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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Reliable conditioning of behavioral cloning for offline reinforcement learning
T Nguyen, Q Zheng, and A Grover · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Diffusion policies as an expressive policy class for offline reinforcement learning
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Denoising diffusion probabilistic models
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Rvs: What is essential for offline rl via supervised learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
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Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2022
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Quan Dao, Hao Phung, Binh Nguyen, and Anh Tran · 2023
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Latent space editing in transformer-based flow matching
Vincent Tao Hu, David W Zhang, Meng Tang, Pascal Mettes, Deli Zhao, and Cees GM Snoek · 2023
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Variational diffusion models, 2023
Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2023
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Voicebox: Text-guided multilingual universal speech generation at scale
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Common diffusion noise schedules and sample steps are flawed
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Flow matching for generative modeling
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Training-free linear image inversion via flows
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Multisample flow matching: Straightening flows with minibatch couplings
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Neural codec language models are zero-shot text to speech synthesizers
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Decision stacks: Flexible reinforcement learning via modular generative models
Siyan Zhao and Aditya Grover · 2023
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Semi-supervised offline reinforcement learning with action-free trajectories
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