Zobrazeno 1 - 10
of 16 103
pro vyhledávání: '"Lio †"'
Autor:
Sun, Bohang, Liò, Pietro
In this study, we introduce the Multi-Head Explainer (MHEX), a versatile and modular framework that enhances both the explainability and accuracy of Convolutional Neural Networks (CNNs) and Transformer-based models. MHEX consists of three core compon
Externí odkaz:
http://arxiv.org/abs/2501.01311
Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that
Externí odkaz:
http://arxiv.org/abs/2412.16833
Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to signal processing of such topological signals. Most of the
Externí odkaz:
http://arxiv.org/abs/2412.05132
Deep generative models show promise for de novo protein design, but their effectiveness within specific protein families remains underexplored. In this study, we evaluate two 3D rigid-body generative methods, score matching and flow matching, to gene
Externí odkaz:
http://arxiv.org/abs/2411.18568
Autor:
Zaki, Jihan K., Tomasik, Jakub, McCune, Jade A., Bahn, Sabine, Liò, Pietro, Scherman, Oren A.
Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SE
Externí odkaz:
http://arxiv.org/abs/2411.08082
Neural Algorithmic Reasoning (NAR) research has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. However, most previous approaches have always used a recurrent architecture, where each iteration of the GNN m
Externí odkaz:
http://arxiv.org/abs/2410.15059
We develop a novel data-driven framework as an alternative to dynamic flux balance analysis, bypassing the demand for deep domain knowledge and manual efforts to formulate the optimization problem. The proposed framework is end-to-end, which trains a
Externí odkaz:
http://arxiv.org/abs/2410.14426
Hypergraph neural networks are a class of powerful models that leverage the message passing paradigm to learn over hypergraphs, a generalization of graphs well-suited to describing relational data with higher-order interactions. However, such models
Externí odkaz:
http://arxiv.org/abs/2410.07764
Autor:
Duta, Iulia, Liò, Pietro
The importance of higher-order relations is widely recognized in a large number of real-world systems. However, annotating them is a tedious and sometimes impossible task. Consequently, current approaches for data modelling either ignore the higher-o
Externí odkaz:
http://arxiv.org/abs/2410.03208
Autor:
Singh, Vikash, Khanzadeh, Matthew, Davis, Vincent, Rush, Harrison, Rossi, Emanuele, Shrader, Jesse, Lio, Pietro
We present Bayesian Binary Search (BBS), a novel probabilistic variant of the classical binary search/bisection algorithm. BBS leverages machine learning/statistical techniques to estimate the probability density of the search space and modifies the
Externí odkaz:
http://arxiv.org/abs/2410.01771