Results 71 to 80 of about 5,483,449 (298)

Combining Recurrent and Convolutional Neural Networks for Relation Classification [PDF]

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
NAACL ...
Ngoc Thang Vu, Heike Adel, Pankaj Gupta 0003, Hinrich Schütze   +3 more
openaire   +4 more sources

Polyphonic pitch detection with convolutional recurrent neural networks

CoRR, 2022
MIREX ...
Carl Thomé, Sven Ahlbäck
openaire   +2 more sources

Stereo Image Compression Using Recurrent Neural Network with a Convolutional Neural Network-Based Occlusion Detection

, 2023
126132In this work, we propose an end-to-end trainable recurrent neural network for stereo image compression. The recurrent neural network allows variable compression rates without retraining the network due to the iterative nature of the recurrent units.
Suleman, Hamid, Bätz, Michel, Keinert, Joachim, Gul, Muhammad Shahzeb Khan   +3 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

Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network

Applied Sciences, 2019
Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor ...
Kai Feng, Xitian Pi, Hongying Liu, Kai Sun   +3 more
doaj   +1 more source

Seeing in the dark with recurrent convolutional neural networks

CoRR, 2018
12 pages (with appendix), 6 figure (main text), 3 supplementary ...
openaire   +2 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Prediction of 8-state protein secondary structures by a novel deep learning architecture

BMC Bioinformatics, 2018
Background Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure ...
Buzhong Zhang, Jinyan Li, Qiang Lü
doaj   +1 more source

Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model

Shanghai Jiaotong Daxue xuebao, 2022
In order to accurately reflect the operation characteristics of office buildings, a convolutional neural network(CNN)-recurrent neural network(RNN)combined model for energy consumption prediction of office buildings is proposed by using the good feature ...
ZENG Guozhi, WEI Ziqing, YUE Bao, DING Yunxiao, ZHENG Chunyuan, ZHAI Xiaoqiang
doaj   +1 more source