35 citations · 38 across the 8 of their papers we have counts for
10 papers · 1 filter
On-Device Learning with Binary Neural Networks
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded C…
Input Layer Binarization with Bit-Plane Encoding
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices. Nevertheless, the binarization of the fir…
Generative Negative Replay for Continual Learning
Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini +1
Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetti…
Continual Learning at the Edge: Real-Time Training on Smartphone Devices
Lorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti +1
On-device training for personalized learning is a challenging research problem. Being able to quickly adapt deep prediction models at the edge is necessary to better suit personal…
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…
Latent Replay for Real-Time Continual Learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco +1
Training deep neural networks at the edge on light computational devices, embedded systems and robotic platforms is nowadays very challenging. Continual learning techniques, where…