Dinghuai Zhang | DeepAI

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∙ 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 ...

0 Dinghuai Zhang, et al. ∙

∙ 02/19/2023

Stochastic Generative Flow Networks

Generative Flow Networks (or GFlowNets for short) are a family of probab...

0 Ling Pan, et al. ∙

∙ 02/11/2023

Distributional GFlowNets with Quantile Flows

Generative Flow Networks (GFlowNets) are a new family of probabilistic s...

0 Dinghuai Zhang, et al. ∙

∙ 02/03/2023

Better Training of GFlowNets with Local Credit and Incomplete Trajectories

Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov ...

0 Ling Pan, et al. ∙

∙ 01/30/2023

A theory of continuous generative flow networks

Generative flow networks (GFlowNets) are amortized variational inference...

0 Salem Lahlou, et al. ∙

∙ 10/24/2022

GFlowOut: Dropout with Generative Flow Networks

Bayesian Inference offers principled tools to tackle many critical probl...

7 Dianbo Liu, et al. ∙

∙ 10/07/2022

Generative Augmented Flow Networks

The Generative Flow Network is a probabilistic framework where an agent ...

0 Ling Pan, et al. ∙

∙ 10/03/2022

Latent State Marginalization as a Low-cost Approach for Improving Exploration

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...

0 Dinghuai Zhang, et al. ∙

∙ 10/02/2022

GFlowNets and variational inference

This paper builds bridges between two families of probabilistic algorith...

0 Nikolay Malkin, et al. ∙

∙ 09/06/2022

Unifying Generative Models with GFlowNets

There are many frameworks for deep generative modeling, each often prese...

2 Dinghuai Zhang, et al. ∙

∙ 02/03/2022

Generative Flow Networks for Discrete Probabilistic Modeling

We present energy-based generative flow networks (EB-GFN), a novel proba...

26 Dinghuai Zhang, et al. ∙

∙ 10/06/2021

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond

Black-box optimization formulations for biological sequence design have ...

5 Dinghuai Zhang, et al. ∙

∙ 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...

4 Kartik Ahuja, et al. ∙

∙ 06/05/2021

Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?

Can models with particular structure avoid being biased towards spurious...

0 Dinghuai Zhang, et al. ∙

∙ 10/20/2020

Neural Approximate Sufficient Statistics for Implicit Models

We consider the fundamental problem of how to automatically construct su...

0 Yanzhi Chen, et al. ∙

∙ 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...

32 Baifeng Shi, et al. ∙

∙ 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...

0 Dinghuai Zhang, et al. ∙

∙ 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...

0 Dinghuai Zhang, et al. ∙

∙ 05/02/2019

You Only Propagate Once: Painless Adversarial Training Using Maximal Principle

Deep learning achieves state-of-the-art results in many areas. However r...

0 Dinghuai Zhang, et al. ∙

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