research
∙
02/02/2023
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulat...
share
research
∙
01/16/2023
High-bandwidth Close-Range Information Transport through Light Pipes
Image retrieval after propagation through multi-mode fibers is gaining a...
research
∙
06/20/2022
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Recent years have seen a growth in user-centric applications that requir...
research
∙
06/17/2022
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Modern deep learning systems are increasingly deployed in situations suc...
research
∙
07/02/2021
Memory Efficient Meta-Learning with Large Images
Meta learning approaches to few-shot classification are computationally ...
research
∙
06/11/2021
Precise characterization of the prior predictive distribution of deep ReLU networks
Recent works on Bayesian neural networks (BNNs) have highlighted the nee...
research
∙
06/11/2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
The "cold posterior effect" (CPE) in Bayesian deep learning describes th...
research
∙
03/06/2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Modern meta-learning approaches for image classification rely on increas...
research
∙
02/07/2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
research
∙
02/06/2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
research
∙
01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
research
∙
09/05/2019
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Recently there has been an increased interest in unsupervised learning o...
research
∙
08/13/2019
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i.e., the challenge of...
research
∙
06/18/2019
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
The goal of this paper is to design image classification systems that, a...
research
∙
06/06/2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved gr...
research
∙
12/10/2018
Occupancy Networks: Learning 3D Reconstruction in Function Space
With the advent of deep neural networks, learning-based approaches for 3...
research
∙
11/19/2018
Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model
Current face recognition systems typically operate via classification in...
research
∙
10/09/2018
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
research
∙
09/28/2018
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many...
research
∙
07/20/2018
From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data
Current face recognition systems robustly recognize identities across a ...
research
∙
05/24/2018
Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning
This paper develops a general framework for data efficient and versatile...
research
∙
05/22/2018
Adversarially Robust Training through Structured Gradient Regularization
We propose a novel data-dependent structured gradient regularizer to inc...
research
∙
05/09/2018
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks su...
research
∙
01/13/2018
Which Training Methods for GANs do actually Converge?
Recent work has shown local convergence of GAN training for absolutely c...
research
∙
11/30/2017
Hybrid VAE: Improving Deep Generative Models using Partial Observations
Deep neural network models trained on large labeled datasets are the sta...
research
∙
05/31/2017
The Atari Grand Challenge Dataset
Recent progress in Reinforcement Learning (RL), fueled by its combinatio...
research
∙
05/25/2017
Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) h...
research
∙
05/24/2017
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an ...
research
∙
12/12/2016
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
State-of-the-art computer vision algorithms often achieve efficiency by ...
research
∙
11/21/2016
Memory Lens: How Much Memory Does an Agent Use?
We propose a new method to study the internal memory used by reinforceme...
research
∙
11/21/2016
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
We present a new notion of probabilistic duality for random variables in...
research
∙
11/17/2016
DSAC - Differentiable RANSAC for Camera Localization
RANSAC is an important algorithm in robust optimization and a central bu...
research
∙
06/08/2016
DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity...
research
∙
06/02/2016
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampl...
research
∙
07/22/2015
Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo
We propose a computational model for shape, illumination and albedo infe...
research
∙
02/14/2014
Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...
research
∙
02/04/2014
