207 citations · 297 across the 14 of their papers we have counts for
6 papers · 1 filter
Compositional Sparsity as an Inductive Bias for Neural Architecture Design
Hongyu Lin, Antonio Briola, Yuanrong Wang +1
Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of dimensionality is a fundamental challenge in machine learning theory. Existing li…
Graph Regularized PCA
Antonio Briola, Marwin Schmidt, Fabio Caccioli +4
Multivariate data often exhibit complex dependencies that violate the assumption of isotropic residual noise. For such cases, we introduce Graph Regularized PCA (GR-PCA). It is a g…
Homological Convolutional Neural Networks
Antonio Briola, Yuanrong Wang, Silvia Bartolucci +1
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabu…
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
Yuanrong Wang, Antonio Briola, Tomaso Aste
The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computation…
Topological Feature Selection
Antonio Briola, Tomaso Aste
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We m…
Deep Reinforcement Learning for Active High Frequency Trading
Antonio Briola, Jeremy Turiel, Riccardo Marcaccioli +2
We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading in the stock market. We train DRL agents to trade one unit of…