b'Pietro Li\xc3\xb2'

research

∙ 09/02/2023

A 3D explainability framework to uncover learning patterns and crucial sub-regions in variable sulci recognition

Precisely identifying sulcal features in brain MRI is made challenging b...

0 Michail Mamalakis, et al. ∙

research

∙ 08/23/2023

Graph Neural Stochastic Differential Equations

We present a novel model Graph Neural Stochastic Differential Equations ...

0 Richard Bergna, et al. ∙

research

∙ 08/23/2023

Will More Expressive Graph Neural Networks do Better on Generative Tasks?

Graph generation poses a significant challenge as it involves predicting...

0 Xiandong Zou, et al. ∙

research

∙ 08/21/2023

Topological Graph Signal Compression

Recently emerged Topological Deep Learning (TDL) methods aim to extend c...

0 Guillermo Bernárdez, et al. ∙

research

∙ 07/18/2023

Neural Priority Queues for Graph Neural Networks

Graph Neural Networks (GNNs) have shown considerable success in neural a...

0 Rishabh Jain, et al. ∙

research

∙ 07/01/2023

SHARCS: Shared Concept Space for Explainable Multimodal Learning

Multimodal learning is an essential paradigm for addressing complex real...

0 Gabriele Dominici, et al. ∙

research

∙ 06/08/2023

Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs

Graphs are widely used to encapsulate a variety of data formats, but rea...

0 Zehui Li, et al. ∙

research

∙ 06/08/2023

Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models

Tabular data is often hidden in text, particularly in medical diagnostic...

12 Aleksa Bisercic, et al. ∙

research

∙ 06/07/2023

Neural Embeddings for Protein Graphs

Proteins perform much of the work in living organisms, and consequently ...

0 Francesco Ceccarelli, et al. ∙

research

∙ 06/06/2023

Graph Classification Gaussian Processes via Spectral Features

Graph classification aims to categorise graphs based on their structure ...

0 Felix L. Opolka, et al. ∙

research

∙ 06/06/2023

CIN++: Enhancing Topological Message Passing

Graph Neural Networks (GNNs) have demonstrated remarkable success in lea...

0 Lorenzo Giusti, et al. ∙

research

∙ 06/02/2023

A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution

Reference-based super-resolution (RefSR) has gained considerable success...

0 Xiaoyong Mei, et al. ∙

research

∙ 06/01/2023

Renormalized Graph Neural Networks

Graph Neural Networks (GNNs) have become essential for studying complex ...

0 Francesco Caso, et al. ∙

research

∙ 05/30/2023

Group Invariant Global Pooling

Much work has been devoted to devising architectures that build group-eq...

0 Kamil Bujel, et al. ∙

research

∙ 05/24/2023

Multi-State RNA Design with Geometric Multi-Graph Neural Networks

Computational RNA design has broad applications across synthetic biology...

0 Chaitanya K. Joshi, et al. ∙

research

∙ 05/18/2023

Neural Algorithmic Reasoning for Combinatorial Optimisation

Solving NP-hard/complete combinatorial problems with neural networks is ...

0 Dobrik Georgiev, et al. ∙

research

∙ 05/17/2023

Deep Multiple Instance Learning with Distance-Aware Self-Attention

Traditional supervised learning tasks require a label for every instance...

0 Georg Wölflein, et al. ∙

research

∙ 05/15/2023

Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing

Recently, Koopman operator theory has become a powerful tool for develop...

0 King Fai Yeh, et al. ∙

research

∙ 04/27/2023

Categorical Foundations of Explainable AI

Explainable AI (XAI) aims to address the human need for safe and reliabl...

0 Pietro Barbiero, et al. ∙

research

∙ 04/27/2023

Interpretable Neural-Symbolic Concept Reasoning

Deep learning methods are highly accurate, yet their opaque decision pro...

0 Pietro Barbiero, et al. ∙

research

∙ 04/16/2023

Assisting clinical practice with fuzzy probabilistic decision trees

The need for fully human-understandable models is increasingly being rec...

0 Emma L. Ambag, et al. ∙

research

∙ 04/11/2023

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Although neural networks (especially deep neural networks) have achieved...

0 Mehedi Hasan, et al. ∙

research

∙ 04/07/2023

Sheaf Neural Networks for Graph-based Recommender Systems

Recent progress in Graph Neural Networks has resulted in wide adoption b...

0 Antonio Purificato, et al. ∙

research

∙ 04/06/2023

ElegansNet: a brief scientific report and initial experiments

This research report introduces ElegansNet, a neural network that mimics...

0 Francesco Bardozzo, et al. ∙

research

∙ 03/06/2023

HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

In this paper have developed a novel hybrid hierarchical attention-based...

0 Md Shofiqul Islam, et al. ∙

research

∙ 02/22/2023

A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

Super-resolution plays an essential role in medical imaging because it p...

0 Jin Zhu, et al. ∙

research

∙ 02/15/2023

A Federated Learning Benchmark for Drug-Target Interaction

Aggregating pharmaceutical data in the drug-target interaction (DTI) dom...

0 Gianluca Mittone, et al. ∙

research

∙ 02/09/2023

GCI: A (G)raph (C)oncept (I)nterpretation Framework

Explainable AI (XAI) underwent a recent surge in research on concept ext...

7 Dmitry Kazhdan, et al. ∙

research

∙ 02/06/2023

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

Message Passing Neural Networks (MPNNs) are instances of Graph Neural Ne...

0 Francesco Di Giovanni, et al. ∙

research

∙ 02/02/2023

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Graph Neural Networks (GNNs) have become the leading paradigm for learni...

0 Antonio Longa, et al. ∙

research

∙ 01/26/2023

DBGDGM: Dynamic Brain Graph Deep Generative Model

Graphs are a natural representation of brain activity derived from funct...

0 Alexander Campbell, et al. ∙

research

∙ 01/23/2023

On the Expressive Power of Geometric Graph Neural Networks

The expressive power of Graph Neural Networks (GNNs) has been studied ex...

0 Chaitanya K. Joshi, et al. ∙

research

∙ 12/07/2022

Learning Graph Search Heuristics

Searching for a path between two nodes in a graph is one of the most wel...

0 Michal Pándy, et al. ∙

research

∙ 11/30/2022

A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation

Neural networks with PDEs embedded in their loss functions (physics-info...

0 Aarjav Jain, et al. ∙

research

∙ 11/29/2022

Continuous Neural Algorithmic Planners

Neural algorithmic reasoning studies the problem of learning algorithms ...

0 Yu He, et al. ∙

research

∙ 11/28/2022

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data

Tabular biomedical data is often high-dimensional but with a very small ...

0 Andrei Margeloiu, et al. ∙

research

∙ 11/28/2022

Transductive Kernels for Gaussian Processes on Graphs

Kernels on graphs have had limited options for node-level problems. To a...

0 Yin-Cong Zhi, et al. ∙

research

∙ 11/26/2022

Latent Graph Inference using Product Manifolds

Graph Neural Networks usually rely on the assumption that the graph topo...

0 Haitz Saez de Ocariz Borde, et al. ∙

research

∙ 11/20/2022

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery

By one of the most fundamental principles in physics, a dynamical system...

0 Yana Lishkova, et al. ∙

research

∙ 11/14/2022

Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations

Recent work has suggested post-hoc explainers might be ineffective for d...

0 Shea Cardozo, et al. ∙

research

∙ 11/13/2022

Goal-Conditioned Reinforcement Learning in the Presence of an Adversary

Reinforcement learning has seen increasing applications in real-world co...

0 Carlos Purves, et al. ∙

research

∙ 11/13/2022

SCOTCH and SODA: A Transformer Video Shadow Detection Framework

Shadows in videos are difficult to detect because of the large shadow de...

0 Lihao Liu, et al. ∙

research

∙ 11/11/2022

Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data

Genome-wide studies leveraging recent high-throughput sequencing technol...

0 Andrei Margeloiu, et al. ∙

research

∙ 11/09/2022

Graph Neural Networks with Adaptive Readouts

An effective aggregation of node features into a graph-level representat...

0 David Buterez, et al. ∙

research

∙ 10/27/2022

Explaining the Explainers in Graph Neural Networks: a Comparative Study

Following a fast initial breakthrough in graph based learning, Graph Neu...

0 Antonio Longa, et al. ∙

research

∙ 10/24/2022

Structure-based Drug Design with Equivariant Diffusion Models

Structure-based drug design (SBDD) aims to design small-molecule ligands...

20 Arne Schneuing, et al. ∙

research

∙ 10/13/2022

Global Explainability of GNNs via Logic Combination of Learned Concepts

While instance-level explanation of GNN is a well-studied problem with p...

0 Steve Azzolin, et al. ∙

research

∙ 09/27/2022

DBGSL: Dynamic Brain Graph Structure Learning

Functional connectivity (FC) between regions of the brain is commonly es...

0 Alexander Campbell, et al. ∙

research

∙ 09/19/2022

Distributed representations of graphs for drug pair scoring

In this paper we study the practicality and usefulness of incorporating ...

0 Paul Scherer, et al. ∙

research

∙ 09/19/2022

Concept Embedding Models

Deploying AI-powered systems requires trustworthy models supporting effe...

0 Mateo Espinosa Zarlenga, et al. ∙

Pietro Liò

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