Davide Bacciu
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View article: Non-Dissipative Graph Propagation for Non-Local Community Detection
Non-Dissipative Graph Propagation for Non-Local Community Detection Open
Community detection in graphs aims to cluster nodes into meaningful groups, a task particularly challenging in heterophilic graphs, where nodes sharing similarities and membership to the same community are typically distantly connected. Th…
View article: CoEvolution: A Comprehensive Trustworthy Framework For Connected Machine Learning And Secure Interconnected AI Solutions
CoEvolution: A Comprehensive Trustworthy Framework For Connected Machine Learning And Secure Interconnected AI Solutions Open
International audience
View article: HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity Open
In this work, we present a new deep-learning model for microseismicity monitoring that utilizes continuous spatiotemporal relationships between seismic station recordings, forming an end-to-end pipeline for seismic catalog creation. It emp…
View article: Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning
Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Open
The recent focus and release of pre-trained models have been a key components to several advancements in many fields (e.g. Natural Language Processing and Computer Vision), as a matter of fact, pre-trained models learn disparate latent emb…
View article: Lifelong Evolution of Swarms
Lifelong Evolution of Swarms Open
Adapting to task changes without forgetting previous knowledge is a key skill for intelligent systems, and a crucial aspect of lifelong learning. Swarm controllers, however, are typically designed for specific tasks, lacking the ability to…
View article: Real-time and personalized product recommendations for large e-commerce platforms
Real-time and personalized product recommendations for large e-commerce platforms Open
We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal…
View article: Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling Open
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modules to sequences ex…
View article: Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder
Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder Open
Autism Spectrum Disorder (ASD) is a multi-factorial neurodevelopmental disorder, whose causes are still poorly understood. Effective therapies to reduce all the heterogeneous symptoms of the disorder do not exists yet, but behavioural prog…
View article: Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform
Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform Open
Automatic music transcription (AMT) is the problem of analyzing an audio recording of a musical piece and detecting notes that are being played. AMT is a challenging problem, particularly when it comes to polyphonic music. The goal of AMT …
View article: Learning and Transferring Physical Models through Derivatives
Learning and Transferring Physical Models through Derivatives Open
We propose Derivative Learning (DERL), a supervised approach that models physical systems by learning their partial derivatives. We also leverage DERL to build physical models incrementally, by designing a distillation protocol that effect…
View article: On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems
On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems Open
A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes. Oversquashing is attributed to the exponential decay in information transmission as…
View article: Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts Open
Concept Bottleneck Models (CBMs) are machine learning models that improve interpretability by grounding their predictions on human-understandable concepts, allowing for targeted interventions in their decision-making process. However, when…
View article: Leveraging Deep-Learning Methods for Operational Analysis at Enhanced Geothermal Systems
Leveraging Deep-Learning Methods for Operational Analysis at Enhanced Geothermal Systems Open
In recent decades, geothermal systems have gained increasing importance and attention. They have the potential to greatly contribute to the transition toward green energy and the establishment of a climate-neutral economy. Enhanced Geother…
View article: A Waveform-Based Graph Neural Network Approach for Microseismic Monitoring
A Waveform-Based Graph Neural Network Approach for Microseismic Monitoring Open
In this work, we introduce HEIMDALL, a grapH-based sEIsMic Detector And Locator specifically designed for microseismic applications. Building on recent progress in deep learning (DL), HEIMDALL employs spatiotemporal graph-neural networks t…
View article: Towards Efficient Molecular Property Optimization with Graph Energy Based Models
Towards Efficient Molecular Property Optimization with Graph Energy Based Models Open
Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generat…
View article: ECLYPSE: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum
ECLYPSE: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum Open
The Cloud-Edge continuum enhances application performance by bringing computation closer to data sources. However, it presents considerable challenges in managing resources and determining service placement, as these tasks require navigati…
View article: GRAMA: Adaptive Graph Autoregressive Moving Average Models
GRAMA: Adaptive Graph Autoregressive Moving Average Models Open
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their…
View article: Don't drift away: Advances and Applications of Streaming and Continual Learning
Don't drift away: Advances and Applications of Streaming and Continual Learning Open
Non-stationary environments subject to concept drift require the design of adaptive models that can continuously learn and update. Two primary research communities have emerged to address this challenge: Continual Learning (CL) and Streami…
View article: Sparse Reservoir Topologies for Physical Implementations of Random Oscillators Networks
Sparse Reservoir Topologies for Physical Implementations of Random Oscillators Networks Open
Physical implementation of recurrent neural networks is hindered by the fact that hidden units need to be trained and are often fully-connected. We propose to relieve both these constraints by adopting and improving on an oscillators-based…
View article: Towards Forecasting Bus Arrival Thorough A Model Based On GNN+LSTM Using GTFS and Real-time Data
Towards Forecasting Bus Arrival Thorough A Model Based On GNN+LSTM Using GTFS and Real-time Data Open
The city’s Public Transportation Network (PTN) organizes daily mobility services for millions of people. Some cities worldwide have used urban computing toolkits to handle acquisition, integration, and data analysis, which translates to im…
View article: I Know How: Combining Prior Policies to Solve New Tasks
I Know How: Combining Prior Policies to Solve New Tasks Open
Multi-Task Reinforcement Learning aims at developing agents that are able to\ncontinually evolve and adapt to new scenarios. However, this goal is\nchallenging to achieve due to the phenomenon of catastrophic forgetting and the\nhigh deman…
View article: Joint-Perturbation Simultaneous Pseudo-Gradient
Joint-Perturbation Simultaneous Pseudo-Gradient Open
We study the problem of computing an approximate Nash equilibrium of a game whose strategy space is continuous without access to gradients of the utility function. Lack of access to gradients is common in reinforcement learning settings, w…
View article: Shared Awareness Across Domain‐Specific Artificial Intelligence: An Alternative to Domain‐General Intelligence and Artificial Consciousness
Shared Awareness Across Domain‐Specific Artificial Intelligence: An Alternative to Domain‐General Intelligence and Artificial Consciousness Open
Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain‐specific artificial intelligence is mo…
View article: Continual pre-training mitigates forgetting in language and vision
Continual pre-training mitigates forgetting in language and vision Open
Pre-trained models are commonly used in Continual Learning to initialize the model before training on the stream of non-stationary data. However, pre-training is rarely applied during Continual Learning. We investigate the characteristics …