30 citations · 61 across the 8 of their papers we have counts for
9 papers
Temporal Modelling for Burn Scars on Sentinel-3
Luca Barco, Edoardo Arnaudo, Andrea Bragagnolo +2
Rapid and accurate burn scar delineation from satellite imagery is essential for post-fire damage assessment. Sentinel-3 OLCI, with daily revisit and 21 spectral bands, suits rapid…
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo +2
Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal…
Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data
Adson Duarte, Davide Vitturini, Emanuele Milillo +9
Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasi…
When Does Pruning Benefit Vision Representations?
Enrico Cassano, Riccardo Renzulli, Andrea Bragagnolo +1
Pruning is widely used to reduce the complexity of deep learning models, but its effects on interpretability and representation learning remain poorly understood. This paper invest…
Neural Velocity for hyperparameter tuning
Gianluca Dalmasso, Andrea Bragagnolo, Enzo Tartaglione +2
Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training a…
To update or not to update? Neurons at equilibrium in deep models
Andrea Bragagnolo, Enzo Tartaglione, Marco Grangetto
Recent advances in deep learning optimization showed that, with some a-posteriori information on fully-trained models, it is possible to match the same performance by simply traini…
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Enzo Tartaglione, Andrea Bragagnolo, Francesco Odierna +2
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic. SeReNe (Sensitivity-based Regularization of Ne…
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
Enzo Tartaglione, Andrea Bragagnolo, Attilio Fiandrotti +1
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of th…
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Enzo Tartaglione, Andrea Bragagnolo, Marco Grangetto
Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance lo…