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06/30/2023
Thompson sampling for improved exploration in GFlowNets
Generative flow networks (GFlowNets) are amortized variational inference...
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06/26/2023
BatchGFN: Generative Flow Networks for Batch Active Learning
We introduce BatchGFN – a novel approach for pool-based active learning ...
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06/20/2023
Multi-Fidelity Active Learning with GFlowNets
In the last decades, the capacity to generate large amounts of data in s...
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02/19/2023
Stochastic Generative Flow Networks
Generative Flow Networks (or GFlowNets for short) are a family of probab...
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02/13/2023
GFlowNet-EM for learning compositional latent variable models
Latent variable models (LVMs) with discrete compositional latents are an...
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02/01/2023
GFlowNets for AI-Driven Scientific Discovery
Tackling the most pressing problems for humanity, such as the climate cr...
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11/01/2022
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Generative Flow Networks (GFlowNets) have demonstrated significant perfo...
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10/24/2022
GFlowOut: Dropout with Generative Flow Networks
Bayesian Inference offers principled tools to tackle many critical probl...
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10/23/2022
Multi-Objective GFlowNets
In many applications of machine learning, like drug discovery and materi...
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09/26/2022
Learning GFlowNets from partial episodes for improved convergence and stability
Generative flow networks (GFlowNets) are a family of algorithms for trai...
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01/31/2022
Trajectory Balance: Improved Credit Assignment in GFlowNets
Generative Flow Networks (GFlowNets) are a method for learning a stochas...
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06/08/2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
This paper is about the problem of learning a stochastic policy for gene...
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02/16/2021
DEUP: Direct Epistemic Uncertainty Prediction
Epistemic uncertainty is the part of out-of-sample prediction error due ...
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02/28/2020
DROCC: Deep Robust One-Class Classification
Classical approaches for one-class problems such as one-class SVM (Schol...
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10/01/2019
