48 citations · 140 across the 16 of their papers we have counts for
13 papers · 1 filter
Adaptive Semantic Token Communication for Transformer-based Edge Inference
Alessio Devoto, Jary Pomponi, Mattia Merluzzi +2
This paper presents an adaptive framework for edge inference based on a dynamically configurable transformer-powered deep joint source channel coding (DJSCC) architecture. Motivate…
Goal-oriented Communications based on Recursive Early Exit Neural Networks
Jary Pomponi, Mattia Merluzzi, Alessio Devoto +3
This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First,…
How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies
Piotr Kubaty, Bartosz Wójcik, Bartłomiej Krzepkowski +4
Early exits enable the network's forward pass to terminate early by attaching trainable internal classifiers to the backbone network. Existing early-exit methods typically adopt ei…
Conditional computation in neural networks: principles and research trends
Simone Scardapane, Alessandro Baiocchi, Alessio Devoto +3
This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus o…
Class incremental learning with probability dampening and cascaded gated classifier
Jary Pomponi, Alessio Devoto, Simone Scardapane
Humans are capable of acquiring new knowledge and transferring learned knowledge into different domains, incurring a small forgetting. The same ability, called Continual Learning,…
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks
Matteo Gambella, Jary Pomponi, Simone Scardapane +1
Early Exit Neural Networks (EENNs) endow astandard Deep Neural Network (DNN) with Early Exit Classifiers (EECs), to provide predictions at intermediate points of the processing whe…
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new tas…
Pixle: a fast and effective black-box attack based on rearranging pixels
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a…
Continual Learning with Invertible Generative Models
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of…
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
Jary Pomponi, Simone Scardapane, Aurelio Uncini
In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In part…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpre…
Efficient Continual Learning in Neural Networks with Embedding Regularization
Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco +1
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architec…