activity
20162022
most citedVideoLSTM Convolves, Attends and Flows for Action Recognition

64 citations · 204 across the 19 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2022

Differentiable Mathematical Programming for Object-Centric Representation Learning

Adeel Pervez, Phillip Lippe, Efstratios Gavves

We propose topology-aware feature partitioning into disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose…

cs.LG20219 cited

Federated Mixture of Experts

Matthias Reisser, Christos Louizos, Efstratios Gavves +1

Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a…

cs.LG2020

Categorical Normalizing Flows via Continuous Transformations

Phillip Lippe, Efstratios Gavves

Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a co…

cs.LG201919 cited

SafeCritic: Collision-Aware Trajectory Prediction

Tessa van der Heiden, Naveen Shankar Nagaraja, Christian Weiss +1

Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, t…

cs.LG2018

Relaxed Quantization for Discretized Neural Networks

Christos Louizos, Matthias Reisser, Tijmen Blankevoort +2

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks…