Results 41 to 50 of about 37,604 (258)
Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
, 2022 Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data.
Wang, Xinjue +2 more
core +1 more source
Tangent Graph Convolutional Network [PDF]
, 2021 Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors.
Luca Pasa +2 more
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Pseudo-Riemannian Graph Convolutional Networks
, 2022 Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.
Staab, S +5 more
core
Point Cloud Normal Estimation with Graph-Convolutional Neural Networks [PDF]
, 2020 Surface normal estimation is a basic task for many point cloud processing algorithms. However, it can be challenging to capture the local geometry of the data, especially in presence of noise.
Pistilli, Francesca +3 more
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Advanced Functional Materials, EarlyView.Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son +5 more
wiley +1 more source
Noise-Enhanced Associative Memories [PDF]
, 2013 Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms allow reliable learning and recall of exponential numbers of patterns.
Amir Hesam Salavati +7 more
core +1 more source
Advanced Healthcare Materials, EarlyView.Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez +6 more
wiley +1 more source
Convolutional Graph Neural Networks
, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters.
Ribeiro, Alejandro (author) +7 more
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Understanding Pooling in Graph Neural Networks
, 2021 Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. The great variety in the literature stems from the many
Grattarola, Daniele +7 more
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Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
Advanced Materials, EarlyView.This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong +12 more
wiley +1 more source