Search Results for author: Chin-wei Huang

Found 26 papers, 11 papers with code

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

no code implementations1 Feb 2023 Marloes Arts, Victor Garcia Satorras, Chin-wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg

Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.

Protein Folding

Waveform Design for Optimal PSL Under Spectral and Unimodular Constraints via Alternating Minimization

no code implementations16 Oct 2022 Chin-wei Huang, Li-Fu Chen, Borching Su

It is observed in the numerical results that the PSL of the proposed algorithm is close to the derived lower bound.

Riemannian Diffusion Models

no code implementations16 Aug 2022 Chin-wei Huang, Milad Aghajohari, Avishek Joey Bose, Prakash Panangaden, Aaron Courville

In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation.

Image Generation

Learning to Dequantise with Truncated Flows

no code implementations ICLR 2022 Shawn Tan, Chin-wei Huang, Alessandro Sordoni, Aaron Courville

Addtionally, since the support of the marginal $q(z)$ is bounded and the support of prior $p(z)$ is not, we propose renormalising the prior distribution over the support of $q(z)$.

Variational Inference

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

1 code implementation NeurIPS 2021 Chin-wei Huang, Jae Hyun Lim, Aaron Courville

Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the theoretical gap.

RealCause: Realistic Causal Inference Benchmarking

no code implementations30 Nov 2020 Brady Neal, Chin-wei Huang, Sunand Raghupathi

However, the best causal estimators on synthetic data are unlikely to be the best causal estimators on real data.

Benchmarking Causal Inference

Bijective-Contrastive Estimation

no code implementations pproximateinference AABI Symposium 2021 Jae Hyun Lim, Chin-wei Huang, Aaron Courville, Christopher Pal

In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models.


AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

2 code implementations ICML 2020 Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-wei Huang

Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable.

Continuous Control Denoising +1

Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models

no code implementations ICLR Workshop DeepDiffEq 2019 Chin-wei Huang, Laurent Dinh, Aaron Courville

Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that allows us to efficiently track the change of probability density.

Computational Efficiency

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

1 code implementation17 Feb 2020 Chin-wei Huang, Laurent Dinh, Aaron Courville

In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood.

Image Generation

Investigating Biases in Textual Entailment Datasets

no code implementations23 Jun 2019 Shawn Tan, Yikang Shen, Chin-wei Huang, Aaron Courville

The ability to understand logical relationships between sentences is an important task in language understanding.

BIG-bench Machine Learning Natural Language Inference +2

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

1 code implementation NeurIPS 2019 Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-wei Huang, Jian Tang

Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks.

Community Detection Representation Learning +1

Stochastic Neural Network with Kronecker Flow

no code implementations10 Jun 2019 Chin-wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks.

Multi-Armed Bandits Thompson Sampling +1

Note on the bias and variance of variational inference

1 code implementation9 Jun 2019 Chin-wei Huang, Aaron Courville

In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio.

Variational Inference

Hierarchical Importance Weighted Autoencoders

1 code implementation13 May 2019 Chin-wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.

Variational Inference

On Difficulties of Probability Distillation

no code implementations27 Sep 2018 Chin-wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville

Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.

Improving Explorability in Variational Inference with Annealed Variational Objectives

1 code implementation NeurIPS 2018 Chin-wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville

Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned.

Variational Inference

Neural Autoregressive Flows

5 code implementations ICML 2018 Chin-wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF).

Density Estimation Speech Synthesis

Generating Contradictory, Neutral, and Entailing Sentences

no code implementations7 Mar 2018 Yikang Shen, Shawn Tan, Chin-wei Huang, Aaron Courville

Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP).

Diversity Natural Language Inference +2

Neural Language Modeling by Jointly Learning Syntax and Lexicon

1 code implementation ICLR 2018 Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville

In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.

Constituency Grammar Induction Language Modelling

Learnable Explicit Density for Continuous Latent Space and Variational Inference

no code implementations6 Oct 2017 Chin-wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior.

Density Estimation Variational Inference

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