research
∙
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...
share
research
∙
08/16/2022
Riemannian Diffusion Models
Diffusion models are recent state-of-the-art methods for image generatio...
research
∙
06/05/2021
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Discrete-time diffusion-based generative models and score matching metho...
research
∙
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 ...
research
∙
11/30/2020
RealCause: Realistic Causal Inference Benchmarking
There are many different causal effect estimators in causal inference. H...
research
∙
06/09/2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
research
∙
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...
research
∙
06/23/2019
Investigating Biases in Textual Entailment Datasets
The ability to understand logical relationships between sentences is an ...
research
∙
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...
research
∙
06/10/2019
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
research
∙
06/09/2019
Note on the bias and variance of variational inference
In this note, we study the relationship between the variational gap and ...
research
∙
05/13/2019
Hierarchical Importance Weighted Autoencoders
Importance weighted variational inference (Burda et al., 2015) uses mult...
research
∙
09/06/2018
Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate dis...
research
∙
04/03/2018
Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combi...
research
∙
03/07/2018
Generating Contradictory, Neutral, and Entailing Sentences
Learning distributed sentence representations remains an interesting pro...
research
∙
11/02/2017
Neural Language Modeling by Jointly Learning Syntax and Lexicon
We propose a neural language model capable of unsupervised syntactic str...
research
∙
10/13/2017
Bayesian Hypernetworks
We propose Bayesian hypernetworks: a framework for approximate Bayesian ...
research
∙
10/06/2017