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Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates with probabilities proportional to a given reward.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Evaluating the quality of approximations to the non-dominated set
Michael Pilegaard Hansen and Andrzej Jaszkiewicz · 1994
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Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
David Allen Van Veldhuizen · 1999
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A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm
Carlos A Coello Coello and Margarita Reyes Sierra · 2004
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An improved dimension-sweep algorithm for the hypervolume indicator
Carlos M Fonseca, Luís Paquete, and Manuel López-Ibánez · 2006
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
L. C. Blum and J.-L. Reymond · 2009
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Nupack: Analysis and design of nucleic acid systems
Joseph N Zadeh, Conrad D Steenberg, Justin S Bois, Brian R Wolfe, Marshall B Pierce, Asif R Khan, Robert M Dirks, and Niles A Pierce · 2011
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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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Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization
Oliver Schutze, Xavier Esquivel, Adriana Lara, and Carlos A Coello Coello · 2012
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Machine learning of molecular electronic properties in chemical compound space
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole von Lilienfeld · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Modified distance calculation in generational distance and inverted generational distance
Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, and Yusuke Nojima · 2015
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Survey of variation in human transcription factors reveals prevalent dna binding changes
Luis A Barrera, Anastasia Vedenko, Jesse V Kurland, Julia M Rogers, Stephen S Gisselbrecht, Elizabeth J Rossin, Jaie Woodard, Luca Mariani, Kian Hong Kock, Sachi Inukai, et al · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Hierarchical variational models
Rajesh Ranganath, Dustin Tran, and David Blei · 2016
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Bridging the gap between value and policy based reinforcement learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Metal sensing by dna
Wenhu Zhou, Runjhun Saran, and Juewen Liu · 2017
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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MARS: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Bayesian structure learning with generative flow networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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Biological sequence design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F.P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, and Yoshua Bengio · 2022
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Learning gflownets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin · 2022
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Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Computational design of three-dimensional rna structure and function
Joseph D Yesselman, Daniel Eiler, Erik D Carlson, Michael R Gotrik, Anne E d’Aquino, Alexandra N Ooms, Wipapat Kladwang, Paul D Carlson, Xuesong Shi, David A Costantino, et al · 2019
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Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
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Approximate inference in discrete distributions with monte carlo tree search and value functions
Lars Buesing, Nicolas Heess, and Theophane Weber · 2020
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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Revisiting fundamentals of experience replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, and Will Dabney · 2020
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Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Evaluating generalization in gflownets for molecule design
Andrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M Bronstein, and Yoshua Bengio · 2022
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Bayesian learning of causal structure and mechanisms with gflownets and variational bayes
Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, and Laurent Charlin · 2022
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Generative augmented flow networks
Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, and Yoshua Bengio · 2022
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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson · 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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Multi-objective gflownets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
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torchgfn: A pytorch gflownet library
Salem Lahlou, Joseph D Viviano, and Victor Schmidt · 2023
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Better training of gflownets with local credit and incomplete trajectories
Ling Pan, Nikolay Malkin, Dinghuai Zhang, and Yoshua Bengio · 2023
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Thompson sampling for improved exploration in gflownets
Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, and Yoshua Bengio · 2023
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Goal-conditioned gflownets for controllable multi-objective molecular design
Julien Roy, Pierre-Luc Bacon, Christopher Pal, and Emmanuel Bengio · 2023
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Towards understanding and improving gflownet training
Max W Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, and Tommaso Biancalani · 2023
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