activity
20172025
most citedExplaining Deep Graph Networks with Molecular Counterfactuals

18 citations · 82 across the 27 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.LG2022

ChemAlgebra: Algebraic Reasoning on Chemical Reactions

Andrea Valenti, Davide Bacciu, Antonio Vergari

While showing impressive performance on various kinds of learning tasks, it is yet unclear whether deep learning models have the ability to robustly tackle reasoning tasks. than by…

cs.LG2022

Modular Representations for Weak Disentanglement

Andrea Valenti, Davide Bacciu

The recently introduced weakly disentangled representations proposed to relax some constraints of the previous definitions of disentanglement, in exchange for more flexibility. How…

cs.LG20222 cited

Leveraging Relational Information for Learning Weakly Disentangled Representations

Andrea Valenti, Davide Bacciu

Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily…

cs.LG20221 cited

Continual Pre-Training Mitigates Forgetting in Language and Vision

Andrea Cossu, Tinne Tuytelaars, Antonio Carta +3

Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the s…

cs.CV2022

Deep Features for CBIR with Scarce Data using Hebbian Learning

Gabriele Lagani, Davide Bacciu, Claudio Gallicchio +3

Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \…

cs.AI20225 cited

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

Valerio De Caro, Saira Bano, Achilles Machumilane +11

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving per…