Sebastian Nowozin

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Google ∙ Microsoft

Principal Researcher at Microsoft Research, Cambridge, UK, managing the Machine Intelligence and Perception group.

TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increas...
03/06/2020 ∙ by John Bronskill, et al. ∙ 12 ∙ share

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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...
02/07/2020 ∙ by Jakub Świątkowski, et al. ∙ 6 ∙ share

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How Good is the Bayes Posterior in Deep Neural Networks Really?

During the past five years the Bayesian deep learning community has deve...
02/06/2020 ∙ by Florian Wenzel, et al. ∙ 27 ∙ share

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Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...
01/14/2020 ∙ by Linh Tran, et al. ∙ 1 ∙ share

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Recently there has been an increased interest in unsupervised learning o...
09/05/2019 ∙ by Jan Stühmer, et al. ∙ 24 ∙ share

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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...
08/13/2019 ∙ by Wenbo Gong, et al. ∙ 3 ∙ share

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Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

The goal of this paper is to design image classification systems that, a...
06/18/2019 ∙ by James Requeima, et al. ∙ 6 ∙ share

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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Modern machine learning methods including deep learning have achieved gr...
06/06/2019 ∙ by Yaniv Ovadia, et al. ∙ 9 ∙ share

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Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3...
12/10/2018 ∙ by Lars Mescheder, et al. ∙ 2 ∙ share

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Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model

Current face recognition systems typically operate via classification in...
11/19/2018 ∙ by Daniel Coelho de Castro, et al. ∙ 14 ∙ share

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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
10/09/2018 ∙ by Anqi Wu, et al. ∙ 6 ∙ share

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EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Making decisions requires information relevant to the task at hand. Many...
09/28/2018 ∙ by Chao Ma, et al. ∙ 6 ∙ share

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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 ...
07/20/2018 ∙ by Daniel Coelho de Castro, et al. ∙ 8 ∙ share

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Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning

This paper develops a general framework for data efficient and versatile...
05/24/2018 ∙ by Jonathan Gordon, et al. ∙ 0 ∙ share

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Adversarially Robust Training through Structured Gradient Regularization

We propose a novel data-dependent structured gradient regularizer to inc...
05/22/2018 ∙ by Kevin Roth, et al. ∙ 2 ∙ share

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Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Modern deep learning systems successfully solve many perception tasks su...
05/09/2018 ∙ by Sergey Prokudin, et al. ∙ 2 ∙ share

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Which Training Methods for GANs do actually Converge?

Recent work has shown local convergence of GAN training for absolutely c...
01/13/2018 ∙ by Lars Mescheder, et al. ∙ 0 ∙ share

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Hybrid VAE: Improving Deep Generative Models using Partial Observations

Deep neural network models trained on large labeled datasets are the sta...
11/30/2017 ∙ by Sergey Tulyakov, et al. ∙ 0 ∙ share

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The Atari Grand Challenge Dataset

Recent progress in Reinforcement Learning (RL), fueled by its combinatio...
05/31/2017 ∙ by Vitaly Kurin, et al. ∙ 0 ∙ share

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Stabilizing Training of Generative Adversarial Networks through Regularization

Deep generative models based on Generative Adversarial Networks (GANs) h...
05/25/2017 ∙ by Kevin Roth, et al. ∙ 0 ∙ share

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Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

We would like to learn a representation of the data which decomposes an ...
05/24/2017 ∙ by Diane Bouchacourt, et al. ∙ 0 ∙ share

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PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

State-of-the-art computer vision algorithms often achieve efficiency by ...
12/12/2016 ∙ by Alexander Krull, et al. ∙ 0 ∙ share

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Memory Lens: How Much Memory Does an Agent Use?

We propose a new method to study the internal memory used by reinforceme...
11/21/2016 ∙ by Christoph Dann, et al. ∙ 0 ∙ share

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Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

We present a new notion of probabilistic duality for random variables in...
11/21/2016 ∙ by Lars Mescheder, et al. ∙ 0 ∙ share

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DSAC - Differentiable RANSAC for Camera Localization

RANSAC is an important algorithm in robust optimization and a central bu...
11/17/2016 ∙ by Eric Brachmann, et al. ∙ 0 ∙ share

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DISCO Nets: DISsimilarity COefficient Networks

We present a new type of probabilistic model which we call DISsimilarity...
06/08/2016 ∙ by Diane Bouchacourt, et al. ∙ 0 ∙ share

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

Generative neural samplers are probabilistic models that implement sampl...
06/02/2016 ∙ by Sebastian Nowozin, et al. ∙ 0 ∙ share

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Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo

We propose a computational model for shape, illumination and albedo infe...
07/22/2015 ∙ by Amit Adam, et al. ∙ 0 ∙ share

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Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...
02/14/2014 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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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...
02/04/2014 ∙ by Varun Jampani, et al. ∙ 0 ∙ share

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Sebastian Nowozin

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