Publications related to Lippmann Photography: A Signal Processing Perspective

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

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

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

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

Graph signal processing tailored for subgraph focus and community structure

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Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilience, or prediction of missing connections. Nevertheless, ...

EPFL2022

Engineering of Existing Structures: The Need and Place for Non-destructive Evaluation

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Structural engineers today are still educated mainly to design new structures. This ignores the fact that structures already exist and need to be maintained. Students are thus often ill-equipped to deal with maintenance and preservation of existing structu ...

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Reduced representations of complexes, signals, and multifiltrations

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EPFL2022

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IEEE2022

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NATURE PORTFOLIO2022

The EEG multiverse of schizophrenia

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Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which ev ...

OXFORD UNIV PRESS INC2022

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

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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

Lippmann Photography: A Signal Processing Perspective

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Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Quantization for Distributed Processing and Learning of Structured Data

Graph signal processing tailored for subgraph focus and community structure

Engineering of Existing Structures: The Need and Place for Non-destructive Evaluation

Reduced representations of complexes, signals, and multifiltrations

Spike-Based Sensing and Communication for Highly Energy-Efficient Sensor Edge Nodes

All-optical switching in epsilon-near-zero asymmetric directional coupler

The EEG multiverse of schizophrenia

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

Quantization for Distributed Processing and Learning of Structured Data

Reduced representations of complexes, signals, and multifiltrations

Spike-Based Sensing and Communication for Highly Energy-Efficient Sensor Edge Nodes

All-optical switching in epsilon-near-zero asymmetric directional coupler

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Graph signal processing tailored for subgraph focus and community structure

Engineering of Existing Structures: The Need and Place for Non-destructive Evaluation

The EEG multiverse of schizophrenia

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