Publications related to Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Edoardo Charbon, Claudio Bruschini, Andrei Ardelean, Paul Mos, Yang Lin

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Berlin2024

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Daniel Gatica-Perez, Sina Sajadmanesh

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

Assoc Computing Machinery2024

Intraday solar irradiance forecasting using public cameras

Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Pergamon-Elsevier Science Ltd2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Marco Picasso, Alexandre Caboussat, Maude Girardin

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Lausanne2024

Enabling Uncertainty Estimation in Iterative Neural Networks

Pascal Fua, Nikita Durasov, Doruk Oner, Minh Hieu Lê

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

2024

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Graph Chatbot

Chat with Graph Search

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Incorporating Projective Geometry into Deep Learning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Intraday solar irradiance forecasting using public cameras

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Enabling Uncertainty Estimation in Iterative Neural Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Intraday solar irradiance forecasting using public cameras

Incorporating Projective Geometry into Deep Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Enabling Uncertainty Estimation in Iterative Neural Networks

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees