Results 51 to 60 of about 4,069,375 (260)

Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering [PDF]

, 2022
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.
Jiang, Jing, Jiang, J, Long, Guodong, Long, G, Zhang, C, Wu, Z, Pan, Shirui, Zhang, Chengqi, Wu, Zonghan, Pan, S   +9 more
core   +1 more source

Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification

IEEE Access
Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system.
Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini   +2 more
doaj   +1 more source

STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting

Mathematics, 2022
Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
doaj   +1 more source

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Graph Neural Networks with Adaptive Readouts [PDF]

, 2022
An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks.
Oglic D., Buterez D., Janet J. P., Lio P., Kiddle S. J.   +4 more
core  

Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniques

Biomédica: revista del Instituto Nacional de Salud
Introduction. PIWI-interacting RNAs are small and non-coding RNAs involved in gene regulation and transposable element repression, emerging as critical biomarkers and therapeutic targets in oncology. Advances in artificial intelligence, such as recurrent
Jheremy Sebastián Reyes, Jhonathan David Guevara, Laura Tatiana Picón, Iris Lorena Sánchez, Libia Andrea Gaona, María Paula Montoya, Luis Eduardo Pino   +6 more
doaj   +1 more source

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini, Elena Adelucci, Stefano Scatigna, Lorenzo Pugi, Alice Bruni, Benedetta Carotenuto, Chiara Pecini, Filippo Cavallo   +7 more
wiley   +1 more source

Robust Local Cluster Neural Networks (ESANN) [PDF]

, 2006
Eickhoff R, Sitte J, Rückert U. Robust Local Cluster Neural Networks (ESANN). In: Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN).
Eickhoff, Ralf, Rueckert, Ulrich, Sitte, Joaquin, Rückert, Ulrich   +3 more
core  

Graph Kernel Neural Networks [PDF]

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
Bicciato, Alessandro, Rodolà, Emanuele, Cosmo, Luca, Bronstein, MM, Minello, Giorgia, Bronstein, Michael, Rodolà, E, Torsello, Andrea, Rossi, Luca, Torsello, A, Minello, G, Bicciato, A, Rossi, L, Cosmo, L, Bronstein, Michael M.   +14 more
core   +1 more source

Bayesian Graph Convolutional Neural Networks via Tempered MCMC

IEEE Access, 2021
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data.
Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, Pavel N. Krivitsky   +3 more
doaj   +1 more source