Attilio Fiandrotti — accepted AI-conference papers

2024

Find the Lady: Permutation and Re-synchronization of Deep Neural Networks

Carl De Sousa Trias, Mihai Petru Mitrea, Attilio Fiandrotti, Marco Cagnazzo, Sumanta Chaudhuri, Enzo Tartaglione

AAAI 2024technical

Deep neural networks are characterized by multiple symmetrical, equi-loss solutions that are redundant. Thus, the order of neurons in a layer and feature maps can be given arbitrary permutations, without affecting (or minimally affecting) their output. If we shuffle these neurons, or if we apply to…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2019

Enhancing HEVC Spatial Prediction by Context-based Learning

Li Wang, Attilio Fiandrotti, Andrei I. Purica, Giuseppe Valenzise, Marco Cagnazzo

ICASSP 2019accepted

Deep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, in this work we study how learning-based approaches might be…

Cited by 7 Source ↗Scholar ↗

2018

Learning sparse neural networks via sensitivity-driven regularization

Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini

NeurIPS 2018poster

The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights. In this context we quantify the output sensitivity to the parameters (i.e. their relevan…

Cited by 103 Source ↗PDF ↗Scholar ↗