89 citations · 113 across the 11 of their papers we have counts for
16 papers · 1 filter
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia +4
How much explicit guidance is necessary for conditional diffusion? We consider the problem of conditional sampling using an unconditional diffusion model and limited explicit guida…
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas +9
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling ne…
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas
State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small inst…
What training reveals about neural network complexity
Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
This work explores the Benevolent Training Hypothesis (BTH) which argues that the complexity of the function a deep neural network (NN) is learning can be deduced by its training d…
Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, Pascal Frossard
Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning lo…
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Nikolaos Karalias, Andreas Loukas
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning fra…