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no code implementations • 2 Feb 2023 • Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where the equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).

no code implementations • 16 Jan 2023 • Joowon Lim, Jannes Gladrow, Douglas Kelly, Greg O'Shea, Govert Verkes, Ioan Stefanovici, Sebastian Nowozin, Benn Thomsen

We maximize information transmission by combining a bandwidth-optimized homodyne detector with a differentiable hybrid neural-network consisting of a digital twin of the experiment setup and a U-Net.

1 code implementation • 20 Jun 2022 • Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner

In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context).

Ranked #3 on Few-Shot Image Classification on Meta-Dataset

1 code implementation • 17 Jun 2022 • Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner

The approach is parameter efficient which is key for enabling few-shot learning, inexpensive model updates for personalization, and communication efficient federated learning.

2 code implementations • NeurIPS 2021 • John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner

This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken.

no code implementations • NeurIPS 2021 • Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann

The dataset curation hypothesis of Aitchison (2020): we show empirically that the CPE does not arise in a real curated data set but can be produced in a controlled experiment with varying curation strength.

no code implementations • NeurIPS 2021 • Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann

Recent works on Bayesian neural networks (BNNs) have highlighted the need to better understand the implications of using Gaussian priors in combination with the compositional structure of the network architecture.

2 code implementations • ICML 2020 • John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner

Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.

no code implementations • ICML 2020 • Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.

1 code implementation • ICML 2020 • Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.

1 code implementation • 14 Jan 2020 • Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton

As a result, the diversity of the ensemble predictions, stemming from each member, is lost.

1 code implementation • NeurIPS 2019 • Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.

no code implementations • 25 Sep 2019 • Jakub Świątkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Variational Bayesian Inference is a popular methodology for approximating posterior distributions in Bayesian neural networks.

no code implementations • 5 Sep 2019 • Jan Stühmer, Richard E. Turner, Sebastian Nowozin

Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations.

1 code implementation • 13 Aug 2019 • Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang

In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.

1 code implementation • NeurIPS 2019 • James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner

We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.

Ranked #6 on Few-Shot Image Classification on Meta-Dataset Rank

2 code implementations • NeurIPS 2019 • Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.

no code implementations • ICLR 2019 • Jan Stühmer, Richard Turner, Sebastian Nowozin

Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement.

7 code implementations • CVPR 2019 • Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.

no code implementations • 19 Nov 2018 • Daniel C. Castro, Sebastian Nowozin

Current face recognition systems typically operate via classification into known identities obtained from supervised identity annotations.

2 code implementations • ICLR 2019 • Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances.

1 code implementation • ICLR 2019 • Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.

no code implementations • ECCV 2018 • Daniel C. Castro, Sebastian Nowozin

There are two problems with this current paradigm: (1) current systems are unable to benefit from unlabelled data which may be available in large quantities; and (2) current systems equate successful recognition with labelling a given input image.

1 code implementation • ICLR 2019 • Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner

2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass.

no code implementations • 22 May 2018 • Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann

We propose a novel data-dependent structured gradient regularizer to increase the robustness of neural networks vis-a-vis adversarial perturbations.

1 code implementation • ECCV 2018 • Sergey Prokudin, Peter Gehler, Sebastian Nowozin

However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.

9 code implementations • ICML 2018 • Lars Mescheder, Andreas Geiger, Sebastian Nowozin

In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent.

1 code implementation • ICLR 2018 • Sebastian Nowozin

The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models.

no code implementations • ICLR 2018 • Charlie Nash, Sebastian Nowozin, Nate Kushman

Using the Shapeworld dataset, we show that our representation both enables a better generative model of images, leading to higher quality image samples, as well as creating more semantically useful representations that improve performance over purely dicriminative models on a simple natural language yes/no question answering task.

no code implementations • ICLR 2018 • Mary Phuong, Max Welling, Nate Kushman, Ryota Tomioka, Sebastian Nowozin

Thus, we decouple the choice of decoder capacity and the latent code dimensionality from the amount of information stored in the code.

no code implementations • 30 Nov 2017 • Sergey Tulyakov, Andrew Fitzgibbon, Sebastian Nowozin

We show that such a combination is beneficial because the unlabeled data acts as a data-driven form of regularization, allowing generative models trained on few labeled samples to reach the performance of fully-supervised generative models trained on much larger datasets.

1 code implementation • ICLR 2018 • Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.

no code implementations • CVPR 2017 • Michael Schober, Amit Adam, Omer Yair, Shai Mazor, Sebastian Nowozin

Operating in this mode the camera essentially forgets all information previously captured - and performs depth inference from scratch for every frame.

2 code implementations • 31 May 2017 • Vitaly Kurin, Sebastian Nowozin, Katja Hofmann, Lucas Beyer, Bastian Leibe

Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce.

4 code implementations • NeurIPS 2017 • Lars Mescheder, Sebastian Nowozin, Andreas Geiger

In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs).

1 code implementation • NeurIPS 2017 • Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann

Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters.

2 code implementations • 24 May 2017 • Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin

We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control.

1 code implementation • ICML 2017 • Lars Mescheder, Sebastian Nowozin, Andreas Geiger

We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation.

no code implementations • CVPR 2017 • Alexander Krull, Eric Brachmann, Sebastian Nowozin, Frank Michel, Jamie Shotton, Carsten Rother

In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation.

no code implementations • NeurIPS 2016 • Diane Bouchacourt, Pawan K. Mudigonda, Sebastian Nowozin

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets).

no code implementations • 21 Nov 2016 • Christoph Dann, Katja Hofmann, Sebastian Nowozin

The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms.

no code implementations • 21 Nov 2016 • Lars Mescheder, Sebastian Nowozin, Andreas Geiger

We present a new notion of probabilistic duality for random variables involving mixture distributions.

4 code implementations • CVPR 2017 • Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.

3 code implementations • 7 Nov 2016 • Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

no code implementations • 8 Jun 2016 • Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets).

1 code implementation • NeurIPS 2016 • Sebastian Nowozin, Botond Cseke, Ryota Tomioka

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights.

no code implementations • ICCV 2015 • Jan Stuhmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, Jamie Shotton

In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera.

no code implementations • ICCV 2015 • Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar

To this end, we propose a novel prediction criterion that includes as special cases all previous prediction criteria that have been used in the literature.

no code implementations • 22 Jul 2015 • Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin

We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera.

no code implementations • CVPR 2014 • Sebastian Nowozin

A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory.

no code implementations • CVPR 2014 • Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin

The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods.

no code implementations • 8 Apr 2014 • Uwe Schmidt, Jeremy Jancsary, Sebastian Nowozin, Stefan Roth, Carsten Rother

We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability.

no code implementations • 2 Apr 2014 • Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

no code implementations • 14 Feb 2014 • Andrew Gordon Wilson, Yuting Wu, Daniel J. Holland, Sebastian Nowozin, Mick D. Mantle, Lynn F. Gladden, Andrew Blake

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules.

1 code implementation • 4 Feb 2014 • Varun Jampani, Sebastian Nowozin, Matthew Loper, Peter V. Gehler

Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion.

no code implementations • NeurIPS 2013 • Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi

Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.

no code implementations • CVPR 2013 • Uwe Schmidt, Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, Stefan Roth

From this analysis, we derive a discriminative model cascade for image deblurring.

no code implementations • 18 Jun 2012 • Sebastian Nowozin

Ensembles of classification and regression trees remain popular machine learning methods because they define flexible non-parametric models that predict well and are computationally efficient both during training and testing.

no code implementations • NeurIPS 2011 • Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo

For many of the state-of-the-art computer vision algorithms, image segmentation is an important preprocessing step.

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