82 citations · 108 across the 9 of their papers we have counts for
9 papers
Bridge the Inference Gaps of Neural Processes via Expectation Maximization
Qi Wang, Marco Federici, Herke van Hoof
The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal perfo…
On the Effectiveness of Hybrid Mutual Information Estimation
Marco Federici, David Ruhe, Patrick Forré
Estimating the mutual information from samples from a joint distribution is a challenging problem in both science and engineering. In this work, we realize a variational bound that…
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang +6
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accur…
Compositional Mixture Representations for Vision and Text
Stephan Alaniz, Marco Federici, Zeynep Akata
Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model tha…
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations
Jan Zuiderveld, Marco Federici, Erik J. Bekkers
The high temporal resolution of audio and our perceptual sensitivity to small irregularities in waveforms make synthesizing at high sampling rates a complex and computationally int…
A Bayesian Approach to Invariant Deep Neural Networks
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos +2
We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes. We s…
An Information-theoretic Approach to Distribution Shifts
Marco Federici, Ryota Tomioka, Patrick Forré
Safely deploying machine learning models to the real world is often a challenging process. Models trained with data obtained from a specific geographic location tend to fail when q…
Learning Robust Representations via Multi-View Information Bottleneck
Marco Federici, Anjan Dutta, Patrick Forré +2
The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for pr…
Improved Bayesian Compression
Marco Federici, Karen Ullrich, Max Welling
Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying th…