15.1k citations · 36.9k across the 152 of their papers we have counts for
22 papers · 1 filter
Particle Dynamics for Learning EBMs
Kirill Neklyudov, Priyank Jaini, Max Welling
Energy-based modeling is a promising approach to unsupervised learning, which yields many downstream applications from a single model. The main difficulty in learning energy-based…
An Expectation-Maximization Perspective on Federated Learning
Christos Louizos, Matthias Reisser, Joseph Soriaga +1
Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated fede…
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders
T. Anderson Keller, Qinghe Gao, Max Welling
Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from s…
Multi-Agent MDP Homomorphic Networks
Elise van der Pol, Herke van Hoof, Frans A. Oliehoek +1
This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience betw…
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol +2
Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivarian…
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
Kumar Pratik, Rana Ali Amjad, Arash Behboodi +2
We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive powe…