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09/11/2023
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
Pool-based active learning (AL) is a promising technology for increasing...
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09/03/2023
Traveling Waves Encode the Recent Past and Enhance Sequence Learning
Traveling waves of neural activity have been observed throughout the bra...
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08/14/2023
Efficient Neural PDE-Solvers using Quantization Aware Training
In the past years, the application of neural networks as an alternative ...
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06/01/2023
Rotating Features for Object Discovery
The binding problem in human cognition, concerning how the brain represe...
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04/25/2023
Latent Traversals in Generative Models as Potential Flows
Despite the significant recent progress in deep generative models, the u...
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04/14/2023
The END: An Equivariant Neural Decoder for Quantum Error Correction
Quantum error correction is a critical component for scaling up quantum ...
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02/13/2023
Geometric Clifford Algebra Networks
We propose Geometric Clifford Algebra Networks (GCANs) that are based on...
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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...
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10/24/2022
Structure-based Drug Design with Equivariant Diffusion Models
Structure-based drug design (SBDD) aims to design small-molecule ligands...
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10/11/2022
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Fragment-based drug discovery has been an effective paradigm in early-st...
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09/13/2022
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation
Efficiency and robustness are increasingly needed for applications on 3D...
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09/08/2022
Clifford Neural Layers for PDE Modeling
Partial differential equations (PDEs) see widespread use in sciences and...
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07/18/2022
Bayesian Optimization for Macro Placement
Macro placement is the problem of placing memory blocks on a chip canvas...
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04/05/2022
Complex-Valued Autoencoders for Object Discovery
Object-centric representations form the basis of human perception and en...
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03/31/2022
Equivariant Diffusion for Molecule Generation in 3D
This work introduces a diffusion model for molecule generation in 3D tha...
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03/19/2022
Adversarial Defense via Image Denoising with Chaotic Encryption
In the literature on adversarial examples, white box and black box attac...
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03/18/2022
Defending Variational Autoencoders from Adversarial Attacks with MCMC
Variational autoencoders (VAEs) are deep generative models used in vario...
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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...
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02/15/2022
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Neural networks are increasingly being used to solve partial differentia...
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02/07/2022
Message Passing Neural PDE Solvers
The numerical solution of partial differential equations (PDEs) is diffi...
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11/26/2021
Particle Dynamics for Learning EBMs
Energy-based modeling is a promising approach to unsupervised learning, ...
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11/19/2021
An Expectation-Maximization Perspective on Federated Learning
Federated learning describes the distributed training of models across m...
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10/09/2021
Multi-Agent MDP Homomorphic Networks
This paper introduces Multi-Agent MDP Homomorphic Networks, a class of n...
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10/06/2021
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
Including covariant information, such as position, force, velocity or sp...
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09/26/2021
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
We propose Hypernetwork Kalman Filter (HKF) for tracking applications wi...
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09/03/2021
Topographic VAEs learn Equivariant Capsules
In this work we seek to bridge the concepts of topographic organization ...
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07/14/2021
Federated Mixture of Experts
Federated learning (FL) has emerged as the predominant approach for coll...
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06/18/2021
Deterministic Gibbs Sampling via Ordinary Differential Equations
Deterministic dynamics is an essential part of many MCMC algorithms, e.g...
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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...
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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...
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05/19/2021
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D
This paper introduces a generative model equivariant to Euclidean symmet...
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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...
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04/18/2021
Federated Learning of User Verification Models Without Sharing Embeddings
We consider the problem of training User Verification (UV) models in fed...
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03/10/2021
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
In this work, we explore adversarial attacks on the Variational Autoenco...
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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...
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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...
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02/26/2021
Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels
In this work we propose a batch Bayesian optimization method for combina...
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02/25/2021
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels
The sample efficiency of Bayesian optimization(BO) is often boosted by G...
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02/23/2021
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practic...
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02/19/2021
E(n) Equivariant Graph Neural Networks
This paper introduces a new model to learn graph neural networks equivar...
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02/10/2021
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models
The field of language modelling has been largely dominated by autoregres...
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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...
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11/14/2020
Self Normalizing Flows
Efficient gradient computation of the Jacobian determinant term is a cor...
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10/30/2020
Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routin...
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10/21/2020
Probabilistic Numeric Convolutional Neural Networks
Continuous input signals like images and time series that are irregularl...
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10/21/2020
Quantum Deformed Neural Networks
We develop a new quantum neural network layer designed to run efficientl...
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10/15/2020
Orbital MCMC
Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
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07/16/2020
Natural Graph Networks
Conventional neural message passing algorithms are invariant under permu...
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07/09/2020
Federated Learning of User Authentication Models
Machine learning-based User Authentication (UA) models have been widely ...
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07/06/2020