b'Max Welling'

research

∙ 04/05/2022

Complex-Valued Autoencoders for Object Discovery

Object-centric representations form the basis of human perception and en...

Sindy Löwe, et al. ∙

research

∙ 03/31/2022

Equivariant Diffusion for Molecule Generation in 3D

This work introduces a diffusion model for molecule generation in 3D tha...

Emiel Hoogeboom, et al. ∙

research

∙ 03/19/2022

Adversarial Defense via Image Denoising with Chaotic Encryption

In the literature on adversarial examples, white box and black box attac...

Shi Hu, et al. ∙

research

∙ 03/18/2022

Defending Variational Autoencoders from Adversarial Attacks with MCMC

Variational autoencoders (VAEs) are deep generative models used in vario...

Anna Kuzina, et al. ∙

research

∙ 03/15/2022

Neural RF SLAM for unsupervised positioning and mapping with channel state information

We present a neural network architecture for jointly learning user locat...

Shreya Kadambi, et al. ∙

research

∙ 02/15/2022

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Neural networks are increasingly being used to solve partial differentia...

Johannes Brandstetter, et al. ∙

research

∙ 02/07/2022

Message Passing Neural PDE Solvers

The numerical solution of partial differential equations (PDEs) is diffi...

Johannes Brandstetter, et al. ∙

research

∙ 11/26/2021

Particle Dynamics for Learning EBMs

Energy-based modeling is a promising approach to unsupervised learning, ...

Kirill Neklyudov, et al. ∙

research

∙ 11/19/2021

An Expectation-Maximization Perspective on Federated Learning

Federated learning describes the distributed training of models across m...

Christos Louizos, et al. ∙

research

∙ 10/09/2021

Multi-Agent MDP Homomorphic Networks

This paper introduces Multi-Agent MDP Homomorphic Networks, a class of n...

Elise van der Pol, et al. ∙

research

∙ 10/06/2021

Geometric and Physical Quantities improve E(3) Equivariant Message Passing

Including covariant information, such as position, force, velocity or sp...

Johannes Brandstetter, et al. ∙

research

∙ 09/26/2021

Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking

We propose Hypernetwork Kalman Filter (HKF) for tracking applications wi...

Kumar Pratik, et al. ∙

research

∙ 09/03/2021

Topographic VAEs learn Equivariant Capsules

In this work we seek to bridge the concepts of topographic organization ...

T. Anderson Keller, et al. ∙

research

∙ 07/14/2021

Federated Mixture of Experts

Federated learning (FL) has emerged as the predominant approach for coll...

Matthias Reisser, et al. ∙

research

∙ 06/18/2021

Deterministic Gibbs Sampling via Ordinary Differential Equations

Deterministic dynamics is an essential part of many MCMC algorithms, e.g...

Kirill Neklyudov, et al. ∙

research

∙ 06/15/2021

Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent

We focus on the problem of efficient sampling and learning of probabilit...

Priyank Jaini, et al. ∙

research

∙ 06/10/2021

Coordinate Independent Convolutional Networks – Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds

Motivated by the vast success of deep convolutional networks, there is a...

Maurice Weiler, et al. ∙

research

∙ 05/19/2021

E(n) Equivariant Normalizing Flows for Molecule Generation in 3D

This paper introduces a generative model equivariant to Euclidean symmet...

Victor Garcia Satorras, et al. ∙

research

∙ 04/19/2021

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Symmetries and equivariance are fundamental to the generalization of neu...

Marc Finzi, et al. ∙

research

∙ 04/18/2021

Federated Learning of User Verification Models Without Sharing Embeddings

We consider the problem of training User Verification (UV) models in fed...

Hossein Hosseini, et al. ∙

research

∙ 03/10/2021

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks

In this work, we explore adversarial attacks on the Variational Autoenco...

Anna Kuzina, et al. ∙

research

∙ 03/08/2021

The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning

In this work we develop a quantum field theory formalism for deep learni...

Roberto Bondesan, et al. ∙

research

∙ 03/08/2021

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions

Unobserved confounding is one of the main challenges when estimating cau...

Maximilian Ilse, et al. ∙

research

∙ 02/26/2021

Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels

In this work we propose a batch Bayesian optimization method for combina...

ChangYong Oh, et al. ∙

research

∙ 02/25/2021

Mixed Variable Bayesian Optimization with Frequency Modulated Kernels

The sample efficiency of Bayesian optimization(BO) is often boosted by G...

ChangYong Oh, et al. ∙

research

∙ 02/23/2021

Deep Policy Dynamic Programming for Vehicle Routing Problems

Routing problems are a class of combinatorial problems with many practic...

Wouter Kool, et al. ∙

research

∙ 02/19/2021

E(n) Equivariant Graph Neural Networks

This paper introduces a new model to learn graph neural networks equivar...

Victor Garcia Satorras, et al. ∙

research

∙ 02/10/2021

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models

The field of language modelling has been largely dominated by autoregres...

Emiel Hoogeboom, et al. ∙

research

∙ 02/04/2021

Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC

Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sam...

Priyank Jaini, et al. ∙

research

∙ 11/14/2020

Self Normalizing Flows

Efficient gradient computation of the Jacobian determinant term is a cor...

T. Anderson Keller, et al. ∙

research

∙ 10/30/2020

Experimental design for MRI by greedy policy search

In today's clinical practice, magnetic resonance imaging (MRI) is routin...

Tim Bakker, et al. ∙

research

∙ 10/21/2020

Probabilistic Numeric Convolutional Neural Networks

Continuous input signals like images and time series that are irregularl...

Marc Finzi, et al. ∙

research

∙ 10/21/2020

Quantum Deformed Neural Networks

We develop a new quantum neural network layer designed to run efficientl...

Roberto Bondesan, et al. ∙

research

∙ 10/15/2020

Orbital MCMC

Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...

Kirill Neklyudov, et al. ∙

research

∙ 07/16/2020

Natural Graph Networks

Conventional neural message passing algorithms are invariant under permu...

Pim de Haan, et al. ∙

research

∙ 07/09/2020

Federated Learning of User Authentication Models

Machine learning-based User Authentication (UA) models have been widely ...

Hossein Hosseini, et al. ∙

research

∙ 07/06/2020

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Normalizing flows and variational autoencoders are powerful generative m...

Didrik Nielsen, et al. ∙

research

∙ 06/30/2020

RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

In this paper, we present a novel neural network for MIMO symbol detecti...

Kumar Pratik, et al. ∙

research

∙ 06/30/2020

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

This paper introduces MDP homomorphic networks for deep reinforcement le...

Elise van der Pol, et al. ∙

research

∙ 06/30/2020

Involutive MCMC: a Unifying Framework

Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...

Kirill Neklyudov, et al. ∙

research

∙ 06/18/2020

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Standard causal discovery methods must fit a new model whenever they enc...

Sindy Löwe, et al. ∙

research

∙ 06/18/2020

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

We introduce the SE(3)-Transformer, a variant of the self-attention modu...

Fabian B. Fuchs, et al. ∙

research

∙ 06/02/2020

The Convolution Exponential and Generalized Sylvester Flows

This paper introduces a new method to build linear flows, by taking the ...

Emiel Hoogeboom, et al. ∙

research

∙ 05/14/2020

Bayesian Bits: Unifying Quantization and Pruning

We introduce Bayesian Bits, a practical method for joint mixed precision...

Mart van Baalen, et al. ∙

research

∙ 04/21/2020

A Data and Compute Efficient Design for Limited-Resources Deep Learning

Thanks to their improved data efficiency, equivariant neural networks ha...

Mirgahney Mohamed, et al. ∙

research

∙ 04/02/2020

Guided Variational Autoencoder for Disentanglement Learning

We propose an algorithm, guided variational autoencoder (Guided-VAE), th...

Zheng Ding, et al. ∙

research

∙ 03/11/2020

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs

A common approach to define convolutions on meshes is to interpret them ...

Pim de Haan, et al. ∙

research

∙ 03/04/2020

Neural Enhanced Belief Propagation on Factor Graphs

A graphical model is a structured representation of locally dependent ra...

Victor Garcia Satorras, et al. ∙

research

∙ 02/18/2020

Gradient ℓ_1 Regularization for Quantization Robustness

We analyze the effect of quantizing weights and activations of neural ne...

Milad Alizadeh, et al. ∙

research

∙ 02/14/2020

Estimating Gradients for Discrete Random Variables by Sampling without Replacement

We derive an unbiased estimator for expectations over discrete random va...

Wouter Kool, et al. ∙

Max Welling

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