research
∙
05/26/2023
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Combinatorial optimization (CO) problems are often NP-hard and thus out ...
share
research
∙
02/19/2023
Stochastic Generative Flow Networks
Generative Flow Networks (or GFlowNets for short) are a family of probab...
research
∙
02/11/2023
Distributional GFlowNets with Quantile Flows
Generative Flow Networks (GFlowNets) are a new family of probabilistic s...
research
∙
02/03/2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov ...
research
∙
01/30/2023
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference...
research
∙
10/24/2022
GFlowOut: Dropout with Generative Flow Networks
Bayesian Inference offers principled tools to tackle many critical probl...
research
∙
10/07/2022
Generative Augmented Flow Networks
The Generative Flow Network is a probabilistic framework where an agent ...
research
∙
10/03/2022
Latent State Marginalization as a Low-cost Approach for Improving Exploration
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...
research
∙
10/02/2022
GFlowNets and variational inference
This paper builds bridges between two families of probabilistic algorith...
research
∙
09/06/2022
Unifying Generative Models with GFlowNets
There are many frameworks for deep generative modeling, each often prese...
research
∙
02/03/2022
Generative Flow Networks for Discrete Probabilistic Modeling
We present energy-based generative flow networks (EB-GFN), a novel proba...
research
∙
10/06/2021
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond
Black-box optimization formulations for biological sequence design have ...
research
∙
06/11/2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
The invariance principle from causality is at the heart of notable appro...
research
∙
06/05/2021
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Can models with particular structure avoid being biased towards spurious...
research
∙
10/20/2020
Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct su...
research
∙
08/10/2020
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Convolutional Neural Networks (CNNs) are known to rely more on local tex...
research
∙
02/21/2020
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Randomized classifiers have been shown to provide a promising approach f...
research
∙
05/02/2019
You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle
Deep learning achieves state-of-the-art results in many areas. However r...
research
∙
05/02/2019
