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Found 14 papers, 11 papers with code
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution
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.
Structure-based Drug Design with Equivariant Diffusion Models
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets.
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis
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.
Approximate Latent Force Model Inference
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
Logic Explained Networks
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.
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.
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Gene Regulatory Network Inference with Latent Force Models
Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data.
Graph representation forecasting of patient's medical conditions: towards a digital twin
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.
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders
The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years.
Proximal Distilled Evolutionary Reinforcement Learning
We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning.
Factorised Neural Relational Inference for Multi-Interaction Systems
Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles.
Learning Rare Word Representations using Semantic Bridging
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.
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
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.
Bayesian Hybrid Matrix Factorisation for Data Integration
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.
