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20172022
most citedExplaining Deep Graph Networks with Molecular Counterfactuals

18 citations · 82 across the 26 of their papers we have counts for

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37 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.LG20211 cited

Inductive learning for product assortment graph completion

Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe +2

Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, "bought together", "…

cs.LG2021

GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation

Marco Podda, Davide Bacciu

The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several…