no code implementations • 6 Dec 2023 • Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Ryota Tomioka, Tian Xie
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
no code implementations • 1 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.
no code implementations • 16 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.
no code implementations • 16 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.
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 $q(z)$.
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.
2 code implementations • ICLR 2021 • Chin-wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville
Flow-based models are powerful tools for designing probabilistic models with tractable density.
no code implementations • 30 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.
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.
3 code implementations • 8 Jul 2020 • Tianshi Cao, Chin-wei Huang, David Yu-Tung Hui, Joseph Paul Cohen
However it is unclear which OoDD method should be used in practice.
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.
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.
1 code implementation • 17 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.
Ranked #6 on Image Generation on CelebA 256x256
no code implementations • 23 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.
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.
no code implementations • 10 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.
1 code implementation • 9 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.
1 code implementation • 13 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.
no code implementations • 27 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.
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.
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).
no code implementations • 7 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).
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.
Ranked #13 on Constituency Grammar Induction on PTB Diagnostic ECG Database (Max F1 (WSJ) metric)
no code implementations • ICLR 2018 • David Krueger, Chin-wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron Courville
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
no code implementations • 6 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.