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Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a series of decision-making steps.
Theory of games and economic behavior
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Fragment based drug design: from experimental to computational approaches
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Human-level control through deep reinforcement learning
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A distributional perspective on reinforcement learning
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Fully parameterized quantile function for distributional reinforcement learning
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A distributional code for value in dopamine-based reinforcement learning
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A graph to graphs framework for retrosynthesis prediction
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Fourier features let networks learn high frequency functions in low dimensional domains
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Distributional reinforcement learning with quantile regression
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Neural message passing for quantum chemistry
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On calibration of modern neural networks
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What uncertainties do we need in bayesian deep learning for computer vision?
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Proximal policy optimization algorithms
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Distributed distributional deterministic policy gradients
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An introduction to electrocatalyst design using machine learning for renewable energy storage
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Highly accurate protein structure prediction with alphafold
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Unifying likelihood-free inference with black-box optimization and beyond
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Bayesian structure learning with generative flow networks
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Gflowout: Dropout with generative flow networks
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Learning gflownets from partial episodes for improved convergence and stability
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Bayesian learning of causal structure and mechanisms with gflownets and variational bayes
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Generative augmented flow networks
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Predictive inference with feature conformal prediction
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A variational perspective on generative flow networks
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Distributional Reinforcement Learning
Marc G. Bellemare, Will Dabney, and Mark Rowland · 2023
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A theory of continuous generative flow networks
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