Chin-Wei Huang | DeepAI

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∙ 02/01/2023

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics

Coarse-grained (CG) molecular dynamics enables the study of biological p...

0 Marloes Arts, et al. ∙

∙ 08/16/2022

Riemannian Diffusion Models

Diffusion models are recent state-of-the-art methods for image generatio...

16 Chin-Wei Huang, et al. ∙

∙ 06/05/2021

A Variational Perspective on Diffusion-Based Generative Models and Score Matching

Discrete-time diffusion-based generative models and score matching metho...

0 Chin-Wei Huang, et al. ∙

∙ 12/10/2020

Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization

Flow-based models are powerful tools for designing probabilistic models ...

0 Chin-Wei Huang, et al. ∙

∙ 11/30/2020

RealCause: Realistic Causal Inference Benchmarking

There are many different causal effect estimators in causal inference. H...

0 Brady Neal, et al. ∙

∙ 06/09/2020

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Entropy is ubiquitous in machine learning, but it is in general intracta...

12 Jae Hyun Lim, et al. ∙

∙ 02/17/2020

Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models

In this work, we propose a new family of generative flows on an augmente...

0 Chin-Wei Huang, et al. ∙

∙ 06/23/2019

Investigating Biases in Textual Entailment Datasets

The ability to understand logical relationships between sentences is an ...

0 Shawn Tan, et al. ∙

∙ 06/18/2019

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

This paper focuses on two fundamental tasks of graph analysis: community...

8 Fan-Yun Sun, et al. ∙

∙ 06/10/2019

Stochastic Neural Network with Kronecker Flow

Recent advances in variational inference enable the modelling of highly ...

1 Chin-Wei Huang, et al. ∙

∙ 06/09/2019

Note on the bias and variance of variational inference

In this note, we study the relationship between the variational gap and ...

0 Chin-Wei Huang, et al. ∙

∙ 05/13/2019

Hierarchical Importance Weighted Autoencoders

Importance weighted variational inference (Burda et al., 2015) uses mult...

0 Chin-Wei Huang, et al. ∙

∙ 09/06/2018

Improving Explorability in Variational Inference with Annealed Variational Objectives

Despite the advances in the representational capacity of approximate dis...

0 Chin-Wei Huang, et al. ∙

∙ 04/03/2018

Neural Autoregressive Flows

Normalizing flows and autoregressive models have been successfully combi...

0 Chin-Wei Huang, et al. ∙

∙ 03/07/2018

Generating Contradictory, Neutral, and Entailing Sentences

Learning distributed sentence representations remains an interesting pro...

0 Yikang Shen, et al. ∙

∙ 11/02/2017

Neural Language Modeling by Jointly Learning Syntax and Lexicon

We propose a neural language model capable of unsupervised syntactic str...

0 Yikang Shen, et al. ∙

∙ 10/13/2017

Bayesian Hypernetworks

We propose Bayesian hypernetworks: a framework for approximate Bayesian ...

0 David Krueger, et al. ∙

∙ 10/06/2017

Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE)...

0 Chin-Wei Huang, et al. ∙

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