6 citations · 15 across the 11 of their papers we have counts for
7 papers · 1 filter
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
Gianluigi Silvestri, Fabio Valerio Massoli, Tribhuvanesh Orekondy +2
A promising way to mitigate the expensive process of obtaining a high-dimensional signal is to acquire a limited number of low-dimensional measurements and solve an under-determine…
Variational Learning ISTA
Fabio Valerio Massoli, Christos Louizos, Arash Behboodi
Compressed sensing combines the power of convex optimization techniques with a sparsity-inducing prior on the signal space to solve an underdetermined system of equations. For many…
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization
Fabio Valerio Massoli, Tim Bakker, Thomas Hehn +2
In recent years, solving optimization problems involving black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and en…
Algebraic Topological Networks via the Persistent Local Homology Sheaf
Gabriele Cesa, Arash Behboodi
In this work, we introduce a novel approach based on algebraic topology to enhance graph convolution and attention modules by incorporating local topological properties of the data…
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental +7
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wir…
Quantized Sparse Weight Decomposition for Neural Network Compression
Andrey Kuzmin, Mart van Baalen, Markus Nagel +1
In this paper, we introduce a novel method of neural network weight compression. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is comput…