Search Results for author:
Found 9 papers, 8 papers with code
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint.
Continual Learning with Invertible Generative Models
We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
Pixle: a fast and effective black-box attack based on rearranging pixels
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 wrong prediction that misclassifies the adversarial sample.
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
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.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
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., interpretability, multi-task learning, and calibration.
DeepRICH: Learning Deeply Cherenkov Detectors
Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data.
Efficient Continual Learning in Neural Networks with Embedding Regularization
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 architectures.
