b'Max Welling'

research

∙ 09/11/2023

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

Pool-based active learning (AL) is a promising technology for increasing...

0 Tim Bakker, et al. ∙

research

∙ 09/03/2023

Traveling Waves Encode the Recent Past and Enhance Sequence Learning

Traveling waves of neural activity have been observed throughout the bra...

0 T. Anderson Keller, et al. ∙

research

∙ 08/14/2023

Efficient Neural PDE-Solvers using Quantization Aware Training

In the past years, the application of neural networks as an alternative ...

0 Winfried van den Dool, et al. ∙

research

∙ 06/01/2023

Rotating Features for Object Discovery

The binding problem in human cognition, concerning how the brain represe...

0 Sindy Löwe, et al. ∙

research

∙ 04/25/2023

Latent Traversals in Generative Models as Potential Flows

Despite the significant recent progress in deep generative models, the u...

0 Yue Song, et al. ∙

research

∙ 04/14/2023

The END: An Equivariant Neural Decoder for Quantum Error Correction

Quantum error correction is a critical component for scaling up quantum ...

0 Evgenii Egorov, et al. ∙

research

∙ 02/13/2023

Geometric Clifford Algebra Networks

We propose Geometric Clifford Algebra Networks (GCANs) that are based on...

0 David Ruhe, et al. ∙

research

∙ 01/10/2023

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines

Modeling strong gravitational lenses in order to quantify the distortion...

0 Alexandre Adam, et al. ∙

research

∙ 10/24/2022

Structure-based Drug Design with Equivariant Diffusion Models

Structure-based drug design (SBDD) aims to design small-molecule ligands...

20 Arne Schneuing, et al. ∙

research

∙ 10/11/2022

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

Fragment-based drug discovery has been an effective paradigm in early-st...

5 Ilia Igashov, et al. ∙

research

∙ 09/13/2022

SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation

Efficiency and robustness are increasingly needed for applications on 3D...

4 Zhuo Su, et al. ∙

research

∙ 09/08/2022

Clifford Neural Layers for PDE Modeling

Partial differential equations (PDEs) see widespread use in sciences and...

10 Johannes Brandstetter, et al. ∙

research

∙ 07/18/2022

Bayesian Optimization for Macro Placement

Macro placement is the problem of placing memory blocks on a chip canvas...

10 ChangYong Oh, et al. ∙

research

∙ 04/05/2022

Complex-Valued Autoencoders for Object Discovery

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

7 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...

0 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...

0 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...

0 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...

0 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...

2 Johannes Brandstetter, et al. ∙

research

∙ 02/07/2022

Message Passing Neural PDE Solvers

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

26 Johannes Brandstetter, et al. ∙

research

∙ 11/26/2021

Particle Dynamics for Learning EBMs

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

1 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...

0 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...

0 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...

0 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...

0 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 ...

9 T. Anderson Keller, et al. ∙

research

∙ 07/14/2021

Federated Mixture of Experts

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

22 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...

0 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...

5 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...

0 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...

14 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...

0 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...

0 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...

0 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...

0 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...

16 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...

10 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...

13 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...

0 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...

0 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...

2 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...

11 Priyank Jaini, et al. ∙

research

∙ 11/14/2020

Self Normalizing Flows

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

7 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...

9 Tim Bakker, et al. ∙

research

∙ 10/21/2020

Probabilistic Numeric Convolutional Neural Networks

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

0 Marc Finzi, et al. ∙

research

∙ 10/21/2020

Quantum Deformed Neural Networks

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

0 Roberto Bondesan, et al. ∙

research

∙ 10/15/2020

Orbital MCMC

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

0 Kirill Neklyudov, et al. ∙

research

∙ 07/16/2020

Natural Graph Networks

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

179 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 ...

47 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...

12 Didrik Nielsen, et al. ∙

Max Welling

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