
How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Recently there has been an increased interest in unsupervised learning o...
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Contextual Face Recognition with a NestedHierarchical Nonparametric Identity Model
Current face recognition systems typically operate via classification in...
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TaskNorm: Rethinking Batch Normalization for MetaLearning
Modern metalearning approaches for image classification rely on increas...
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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...
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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 ...
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EDDI: Efficient Dynamic Discovery of HighValue Information with Partial VAE
Making decisions requires information relevant to the task at hand. Many...
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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...
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Fast and Flexible MultiTask Classification Using Conditional Neural Adaptive Processes
The goal of this paper is to design image classification systems that, a...
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The ktied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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Icebreaker: Elementwise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the icestart problem, i.e., the challenge of...
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Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks su...
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Adversarially Robust Training through Structured Gradient Regularization
We propose a novel datadependent structured gradient regularizer to inc...
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Occupancy Networks: Learning 3D Reconstruction in Function Space
With the advent of deep neural networks, learningbased approaches for 3...
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Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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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...
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The Atari Grand Challenge Dataset
Recent progress in Reinforcement Learning (RL), fueled by its combinatio...
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Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) h...
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MultiLevel Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an ...
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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...
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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...
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fGAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampl...
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PoseAgent: BudgetConstrained 6D Object Pose Estimation via Reinforcement Learning
Stateoftheart computer vision algorithms often achieve efficiency by ...
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DSAC  Differentiable RANSAC for Camera Localization
RANSAC is an important algorithm in robust optimization and a central bu...
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DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity...
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Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...
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Bayesian TimeofFlight for Realtime Shape, Illumination and Albedo
We propose a computational model for shape, illumination and albedo infe...
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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...
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DecisionTheoretic MetaLearning: Versatile and Efficient Amortization of FewShot Learning
This paper develops a general framework for data efficient and versatile...
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Which Training Methods for GANs do actually Converge?
Recent work has shown local convergence of GAN training for absolutely c...
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Sebastian Nowozin
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Principal Researcher at Microsoft Research, Cambridge, UK, managing the Machine Intelligence and Perception group.