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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 ...
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07/26/2023
Sparse Double Descent in Vision Transformers: real or phantom threat?
Vision transformers (ViT) have been of broad interest in recent theoreti...
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05/05/2023
Mining bias-target Alignment from Voronoi Cells
Despite significant research efforts, deep neural networks are still vul...
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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...
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03/20/2023
Optimized preprocessing and Tiny ML for Attention State Classification
In this paper, we present a new approach to mental state classification ...
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03/02/2023
Dodging the Sparse Double Descent
This paper presents an approach to addressing the issue of over-parametr...
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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...
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11/10/2022
Unbiased Supervised Contrastive Learning
Many datasets are biased, namely they contain easy-to-learn features tha...
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10/23/2022
Compressing Explicit Voxel Grid Representations: fast NeRFs become also small
NeRFs have revolutionized the world of per-scene radiance field reconstr...
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10/17/2022
Packed-Ensembles for Efficient Uncertainty Estimation
Deep Ensembles (DE) are a prominent approach achieving excellent perform...
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09/30/2022
Information Removal at the bottleneck in Deep Neural Networks
Deep learning models are nowadays broadly deployed to solve an incredibl...
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08/01/2022
UniToBrain dataset: a Brain Perfusion Dataset
The CT perfusion (CTP) is a medical exam for measuring the passage of a ...
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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...
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07/05/2022
Disentangling private classes through regularization
Deep learning models are nowadays broadly deployed to solve an incredibl...
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04/26/2022
Unsupervised Learning of Unbiased Visual Representations
Deep neural networks are known for their inability to learn robust repre...
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04/04/2022
REM: Routing Entropy Minimization for Capsule Networks
Capsule Networks ambition is to build an explainable and biologically-in...
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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 ...
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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...
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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...
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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...
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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...
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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...
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01/25/2021
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor...
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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...
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11/16/2020
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training...
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08/04/2020
A non-discriminatory approach to ethical deep learning
Artificial neural networks perform state-of-the-art in an ever-growing n...
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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, ...
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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...
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07/19/2019
Post-synaptic potential regularization has potential
Improving generalization is one of the main challenges for training deep...
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10/28/2018
Learning Sparse Neural Networks via Sensitivity-Driven Regularization
The ever-increasing number of parameters in deep neural networks poses c...
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10/26/2017
