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High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
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The mnist database of handwritten digits
Yann LeCun · 1998
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Products of experts
Geoffrey E Hinton · 1999
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Damiano Brigo · 2008
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Rdkit: Open-source cheminformatics, 2010
Greg Landrum · 2010
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
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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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Human-level control through deep reinforcement learning
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Adagan: Boosting generative models
Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Generative models of visually grounded imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, and Kevin Murphy · 2018
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Mgan: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
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Boosted generative models
Aditya Grover and Stefano Ermon · 2018
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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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
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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
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Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou · 2022
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Compositional visual generation with composable diffusion models
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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Testing relational understanding in text-guided image generation
Colin Conwell and Tomer Ullman · 2022
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Unsupervised learning of compositional energy concepts
Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch · 2021
Cited alongside, same era.
Learning to compose visual relations
Nan Liu, Shuang Li, Yilun Du, Josh Tenenbaum, and Antonio Torralba · 2021
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Unifying generative models with gflownets
Dinghuai Zhang, Ricky TQ Chen, Nikolay Malkin, and Yoshua Bengio · 2022
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Non-denoising forward-time diffusions, 2022
Stefano Peluchetti · 2022
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Trajectory balance: Improved credit assignment in GFlownets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross B. Girshick · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Chatgpt (mar 14 version)
OpenAI · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, F. Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Ho Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Peter R. Florence · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
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Vipergpt: Visual inference via python execution for reasoning
D’idac Sur’is, Sachit Menon, and Carl Vondrick · 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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Multi-objective GFlownets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
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Gflownet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Unsupervised compositional concepts discovery with text-to-image generative models
Nan Liu, Yilun Du, Shuang Li, Joshua B Tenenbaum, and Antonio Torralba · 2023
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Diffedit: Diffusion-based semantic image editing with mask guidance
Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord · 2023
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A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcıa, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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