6 papers · 1 filter
Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land
Simone Scardapane
Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else,…
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…
Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference
Bartosz Wójcik, Alessio Devoto, Karol Pustelnik +2
While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a sma…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
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,…