1 code implementation • 2 Jun 2023 • Xiaoyong Mei, Yi Yang, Ming Li, Changqin Huang, Kai Zhang, Pietro Lió
In this study, we propose a feature reuse framework that guides the step-by-step texture reconstruction process through different stages, reducing the negative impacts of perceptual and adversarial loss.
2 code implementations • 24 Oct 2022 • Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets.
1 code implementation • 22 Aug 2022 • Han Xuanyuan, Pietro Barbiero, Dobrik Georgiev, Lucie Charlotte Magister, Pietro Lió
We propose a novel approach for producing global explanations for GNNs using neuron-level concepts to enable practitioners to have a high-level view of the model.
1 code implementation • 24 Sep 2021 • Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
1 code implementation • 11 Aug 2021 • Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Lió, Marco Maggini, Stefano Melacci
The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience.
3 code implementations • 12 Jun 2021 • Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, Stefano Melacci
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.
Ranked #1 on Image Classification on CUB
no code implementations • 6 Oct 2020 • Jacob Moss, Pietro Lió
Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data.
1 code implementation • 17 Sep 2020 • Pietro Barbiero, Ramon Viñas Torné, Pietro Lió
Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients.
no code implementations • 10 Jul 2019 • Helena Andrés-Terré, Pietro Lió
The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years.
1 code implementation • 24 Jun 2019 • Cristian Bodnar, Ben Day, Pietro Lió
We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning.
2 code implementations • 21 May 2019 • Ezra Webb, Ben Day, Helena Andres-Terre, Pietro Lió
Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles.
no code implementations • 24 Jul 2017 • Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lió, Nigel Collier
We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge.
1 code implementation • 13 Jul 2017 • Thomas Brouwer, Jes Frellsen, Pietro Lió
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data.
2 code implementations • 17 Apr 2017 • Thomas Brouwer, Pietro Lió
We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction of missing values.