Publications related to Graph Signal Processing for Machine Learning: A Review and New Perspectives

Robust machine learning for neuroscientific inference

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

Michaël Unser, Dimitri Nestor Alice Van De Ville, Michael Stefan Daniel Liebling

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Model agnostic methods meta-learn despite misspecifications

Nicolas Henri Bernard Flammarion, Oguz Kaan Yüksel, Etienne Patrice Boursier

Due to its empirical success on few shot classification and reinforcement learning, meta-learning recently received a lot of interest. Meta-learning leverages data from previous tasks to quickly learn a new task, despite limited data. In particular, model ...

2023

A Framework for Autonomic Computing for In Situ Imageomics

Nina Marion Aurélia Van Tiel

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

The Institute of Electrical and Electronics Engineers, Inc2023

SKA Science Data Challenge 2: analysis and results

Yi Zhang, Xin Chen, Jean-Paul Richard Kneib, Frédéric Courbin, Jean-Luc Starck, Aymeric Alexandre Galan, Austin Chandler Peel, Emma Elizabeth Tolley, Lei Zhang, Hua Zhang, Xiuwei Zhang, Libo Yu, Claudio Gheller, Mark Thomas Sargent, Yi Wang

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of ad ...

OXFORD UNIV PRESS2023

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

Hubert Girault, Yingdi Zhu

Macromolecular mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a quick tool for biochemical analysis. In combination with algorithms for automated data analysis and interpretation, it has wide appli ...

WILEY2023

Quantization for Distributed Processing and Learning of Structured Data

Isabela Cunha Maia Nobre

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

EPFL2022

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Aleksandra Radenovic, Andras Kis, Mukesh Kumar Tripathi, Zhenyu Wang, Guilherme Migliato Marega

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...

2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Graph Signal Processing for Machine Learning: A Review and New Perspectives

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Robust machine learning for neuroscientific inference

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

Model agnostic methods meta-learn despite misspecifications

A Framework for Autonomic Computing for In Situ Imageomics

SKA Science Data Challenge 2: analysis and results

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

Quantization for Distributed Processing and Learning of Structured Data

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

SKA Science Data Challenge 2: analysis and results

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Robust machine learning for neuroscientific inference

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Model agnostic methods meta-learn despite misspecifications

A Framework for Autonomic Computing for In Situ Imageomics

Algorithms push forward the application of MALDI-TOF mass fingerprinting in rapid precise diagnosis

Quantization for Distributed Processing and Learning of Structured Data

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee