Enzo Tartaglione

research

∙ 08/12/2023

Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?

Pruning is a widely used technique for reducing the size of deep neural ...

0 Zhu Liao, et al. ∙

research

∙ 07/26/2023

Sparse Double Descent in Vision Transformers: real or phantom threat?

Vision transformers (ViT) have been of broad interest in recent theoreti...

0 Victor Quétu, et al. ∙

research

∙ 05/05/2023

Mining bias-target Alignment from Voronoi Cells

Despite significant research efforts, deep neural networks are still vul...

0 Rémi Nahon, et al. ∙

research

∙ 05/05/2023

Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis

Image-based rendering techniques stand at the core of an immersive exper...

0 Marta Milovanović, et al. ∙

research

∙ 03/20/2023

Optimized preprocessing and Tiny ML for Attention State Classification

In this paper, we present a new approach to mental state classification ...

0 Yinghao Wang, et al. ∙

research

∙ 03/02/2023

Dodging the Sparse Double Descent

This paper presents an approach to addressing the issue of over-parametr...

0 Victor Quétu, et al. ∙

research

∙ 02/26/2023

Can we avoid Double Descent in Deep Neural Networks?

Finding the optimal size of deep learning models is very actual and of b...

0 Victor Quétu, et al. ∙

research

∙ 11/10/2022

Unbiased Supervised Contrastive Learning

Many datasets are biased, namely they contain easy-to-learn features tha...

0 Carlo Alberto Barbano, et al. ∙

research

∙ 10/23/2022

Compressing Explicit Voxel Grid Representations: fast NeRFs become also small

NeRFs have revolutionized the world of per-scene radiance field reconstr...

0 Chenxi Lola Deng, et al. ∙

research

∙ 10/17/2022

Packed-Ensembles for Efficient Uncertainty Estimation

Deep Ensembles (DE) are a prominent approach achieving excellent perform...

15 Olivier Laurent, et al. ∙

research

∙ 09/30/2022

Information Removal at the bottleneck in Deep Neural Networks

Deep learning models are nowadays broadly deployed to solve an incredibl...

0 Enzo Tartaglione, et al. ∙

research

∙ 08/01/2022

UniToBrain dataset: a Brain Perfusion Dataset

The CT perfusion (CTP) is a medical exam for measuring the passage of a ...

0 Daniele Perlo, et al. ∙

research

∙ 07/19/2022

To update or not to update? Neurons at equilibrium in deep models

Recent advances in deep learning optimization showed that, with some a-p...

0 Andrea Bragagnolo, et al. ∙

research

∙ 07/05/2022

Disentangling private classes through regularization

Deep learning models are nowadays broadly deployed to solve an incredibl...

0 Enzo Tartaglione, et al. ∙

research

∙ 04/26/2022

Unsupervised Learning of Unbiased Visual Representations

Deep neural networks are known for their inability to learn robust repre...

0 Carlo Alberto Barbano, et al. ∙

research

∙ 04/04/2022

REM: Routing Entropy Minimization for Capsule Networks

Capsule Networks ambition is to build an explainable and biologically-in...

0 Riccardo Renzulli, et al. ∙

research

∙ 02/24/2022

The rise of the lottery heroes: why zero-shot pruning is hard

Recent advances in deep learning optimization showed that just a subset ...

0 Enzo Tartaglione, et al. ∙

research

∙ 07/12/2021

HEMP: High-order Entropy Minimization for neural network comPression

We formulate the entropy of a quantized artificial neural network as a d...

0 Enzo Tartaglione, et al. ∙

research

∙ 03/02/2021

EnD: Entangling and Disentangling deep representations for bias correction

Artificial neural networks perform state-of-the-art in an ever-growing n...

0 Enzo Tartaglione, et al. ∙

research

∙ 02/10/2021

Dysplasia grading of colorectal polyps through CNN analysis of WSI

Colorectal cancer is a leading cause of cancer death for both men and wo...

0 Daniele Perlo, et al. ∙

research

∙ 02/07/2021

SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks

Deep neural networks include millions of learnable parameters, making th...

0 Enzo Tartaglione, et al. ∙

research

∙ 01/25/2021

A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images

Early screening of patients is a critical issue in order to assess immed...

0 Carlo Alberto Barbano, et al. ∙

research

∙ 01/25/2021

UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

Histopathological characterization of colorectal polyps allows to tailor...

0 Carlo Alberto Barbano, et al. ∙

research

∙ 01/15/2021

Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke

Purpose: In this study we investigate whether a Convolutional Neural Net...

0 Umberto A. Gava, et al. ∙

research

∙ 11/16/2020

LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks

LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training...

0 Enzo Tartaglione, et al. ∙

research

∙ 08/04/2020

A non-discriminatory approach to ethical deep learning

Artificial neural networks perform state-of-the-art in an ever-growing n...

0 Enzo Tartaglione, et al. ∙

research

∙ 04/30/2020

Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima

Recently, a race towards the simplification of deep networks has begun, ...

0 Enzo Tartaglione, et al. ∙

research

∙ 04/11/2020

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

The possibility to use widespread and simple chest X-ray (CXR) imaging f...

0 Enzo Tartaglione, et al. ∙

research

∙ 07/19/2019

Post-synaptic potential regularization has potential

Improving generalization is one of the main challenges for training deep...

0 Enzo Tartaglione, et al. ∙

research

∙ 10/28/2018

Learning Sparse Neural Networks via Sensitivity-Driven Regularization

The ever-increasing number of parameters in deep neural networks poses c...

0 Enzo Tartaglione, et al. ∙

research

∙ 10/26/2017

On the role of synaptic stochasticity in training low-precision neural networks

Stochasticity and limited precision of synaptic weights in neural networ...

0 Carlo Baldassi, et al. ∙

Enzo Tartaglione

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