Sebastian Nowozin

research

∙ 07/02/2021

Memory Efficient Meta-Learning with Large Images

Meta learning approaches to few-shot classification are computationally ...

John Bronskill, et al. ∙

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...

Lorenzo Noci, et al. ∙

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...

Lorenzo Noci, et al. ∙

research

∙ 03/06/2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increas...

John Bronskill, et al. ∙

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...

Jakub Świątkowski, et al. ∙

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...

Florian Wenzel, et al. ∙

research

∙ 01/14/2020

Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...

Linh Tran, et al. ∙

research

∙ 09/05/2019

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Recently there has been an increased interest in unsupervised learning o...

Jan Stühmer, et al. ∙

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...

Wenbo Gong, et al. ∙

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...

James Requeima, et al. ∙

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...

Yaniv Ovadia, et al. ∙

research

∙ 12/10/2018

Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3...

Lars Mescheder, et al. ∙

research

∙ 11/19/2018

Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model

Current face recognition systems typically operate via classification in...

Daniel Coelho de Castro, et al. ∙

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...

Anqi Wu, et al. ∙

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...

Chao Ma, et al. ∙

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 ...

Daniel Coelho de Castro, et al. ∙

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...

Jonathan Gordon, et al. ∙

research

∙ 05/22/2018

Adversarially Robust Training through Structured Gradient Regularization

We propose a novel data-dependent structured gradient regularizer to inc...

Kevin Roth, et al. ∙

research

∙ 05/09/2018

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Modern deep learning systems successfully solve many perception tasks su...

Sergey Prokudin, et al. ∙

research

∙ 01/13/2018

Which Training Methods for GANs do actually Converge?

Recent work has shown local convergence of GAN training for absolutely c...

Lars Mescheder, et al. ∙

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...

Sergey Tulyakov, et al. ∙

research

∙ 05/31/2017

The Atari Grand Challenge Dataset

Recent progress in Reinforcement Learning (RL), fueled by its combinatio...

Vitaly Kurin, et al. ∙

research

∙ 05/25/2017

Stabilizing Training of Generative Adversarial Networks through Regularization

Deep generative models based on Generative Adversarial Networks (GANs) h...

Kevin Roth, et al. ∙

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 ...

Diane Bouchacourt, et al. ∙

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 ...

Alexander Krull, et al. ∙

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...

Christoph Dann, et al. ∙

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...

Lars Mescheder, et al. ∙

research

∙ 11/17/2016

DSAC - Differentiable RANSAC for Camera Localization

RANSAC is an important algorithm in robust optimization and a central bu...

Eric Brachmann, et al. ∙

research

∙ 06/08/2016

DISCO Nets: DISsimilarity COefficient Networks

We present a new type of probabilistic model which we call DISsimilarity...

Diane Bouchacourt, et al. ∙

research

∙ 06/02/2016

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Generative neural samplers are probabilistic models that implement sampl...

Sebastian Nowozin, et al. ∙

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...

Amit Adam, et al. ∙

research

∙ 02/14/2014

Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...

Andrew Gordon Wilson, et al. ∙

research

∙ 02/04/2014

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

Computer vision is hard because of a large variability in lighting, shap...

Varun Jampani, et al. ∙

Sebastian Nowozin

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