Normalizing flows and autoregressive models have been successfully combined to produce
state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF)(…

… , Jae Hyun Lim, Aaron Courville University of Montreal & Mila {chin-wei.huang, jae.hyun.lim,
aaron.courville}@umontreal.ca … Chin-Wei is supported by the Google PhD fellowship. …

Diffusion models are recent state-of-the-art methods for image generation and likelihood
estimation. In this work, we generalize continuous-time diffusion models to arbitrary …

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

We propose a neural language model capable of unsupervised syntactic structure induction.
The model leverages the structure information to form better semantic representations and …

Although prior research has addressed the influence of production activity and research and
development (R&D) on productivity, it is not clear whether production and R&D affect the …

We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural
networks. A Bayesian hypernetwork $\h$ is a neural network which learns to transform a …

Objective: We compared clinical characteristics of seizures at ischemic stroke presentation (SSP)
to seizures during hospitalization post ischemic stroke (SDH), and their impacts on …

This study develops a modified two-stage model to evaluate productive efficiency, occupancy,
and catering service effectiveness of Taiwan׳s international tourist hotels. The difference …

This paper focuses on two fundamental tasks of graph analysis: community detection and
node representation learning, which capture the global and local structures of graphs …