Results 61 to 70 of about 142,397 (310)

Dependency Parsing with Dilated Iterated Graph CNNs

, 2017
Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale.
McCallum, Andrew, Strubell, Emma
core   +1 more source

Barron Space for Graph Convolution Neural Networks

CoRR, 2023
Graph convolutional neural network (GCNN) operates on graph domain and it has achieved a superior performance to accomplish a wide range of tasks. In this paper, we introduce a Barron space of functions on a compact domain of graph signals. We prove that the proposed Barron space is a reproducing kernel Banach space, it can be decomposed into the union
Seok-Young Chung, Qiyu Sun
openaire   +2 more sources

Packed for Ossification: High‐Density Bioprinting of hPDC Spheroids in HAMA Toward Endochondral Ossification

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, Maria Paula Marks, Paula Casademunt, Adrián Seijas‐Gamardo, Ioannis Papantoniou, Lorenzo Moroni, Carlos Mota   +6 more
wiley   +1 more source

Graph-based Neural Multi-Document Summarization

, 2017
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node
Meelu, Kshitijh, Pareek, Ayush, Radev, Dragomir, Srinivasan, Krishnan, Yasunaga, Michihiro, Zhang, Rui   +5 more
core   +1 more source

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, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Sample and Aggregate Voronoi Neighborhood Weighted Graph Neural Network (SAGE-Voronoi) and Its Capability for City-Sized Vehicle Traffic Time Series Prediction

Applied Sciences
The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region.
Przemysław Bielecki, Tomasz Hachaj, Jarosław Wąs   +2 more
doaj   +1 more source

Graph neural network method for the intelligent selection of river system

Geocarto International, 2023
The spatial features and generalisation rules for river network generalisation are difficult to directly quantify using indicators. To consider dimensional information hidden in river networks and improve river network selection accuracy, this study ...
Di Wang, Haizhong Qian
doaj   +1 more source

Degree-Aware Graph Neural Network Quantization

Entropy, 2023
In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges.
Ziqin Fan, Xi Jin
doaj   +1 more source

Deep neural networks on graph signals for brain imaging analysis

, 2017
Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation
Cheung, Ngai-Man, Guo, Yiluan, Nejati, Hossein   +2 more
core   +1 more source

Higher-Order Sparse Convolutions in Graph Neural Networks

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Accepted in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ...
Giraldo, Jhony H., Javed, Sajid, Mahmood, Arif, Malliaros, Fragkiskos, Bouwmans, Thierry   +4 more
openaire   +3 more sources