Results 61 to 70 of about 176,660 (282)
, 2017 Previous RNN architectures have largely been superseded by LSTM, or "Long Short-Term Memory". Since its introduction, there have been many variations on this simple design. However, it is still widely used and we are not aware of a gated-RNN architecture
Lyu, Siwei, Pulver, Andrew
core +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Zhejiang Daxue xuebao. Lixue ban, 2019 针对目前大部分PM2.5 预测模型预测效果不稳定、泛化能力不强的现状，以记忆能力较强的循环神经网络（RNN） 和特征表达能力较强的卷积神经网络（CNN） 为基础，采取Stacking 集成策略对两者进行融合，提出了RNN-CNN 集成深度学习预测模型。该模型不仅充分利用时间轴上的前后关联信息去预测未来的浓度，而且在不同层次上将自动提取的高维时序数据通用特征用于预测，以保证预测结果的稳定性。最后，对集成之前的 RNN、CNN 和集成之后的RNN-CNN 模型，以2016 年中国大陆地区1 466 ...
HUANGJie(黄婕) +4 more
doaj +1 more source
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
, 2017 In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other ...
Lee, Honglak +3 more
core +2 more sources
DB-RNN: An RNN for Precipitation Nowcasting Deblurring
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingPrecipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years. While new models are continuously proposed to refresh the forecasting performance, the problem of gradual blurring of forecast maps as the forecast period extends is still serious.
Zhifeng Ma, Hao Zhang, Jie Liu
openaire +2 more sources
Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis
sinkron, 2023 This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets.
openaire +2 more sources
Annals of Gastroenterological Surgery, EarlyView.Deep learning has shown promise in predicting postoperative complications, particularly when using image or time‐series data. However, on tabular clinical data such as the NCD, it often underperforms compared to conventional machine learning. Integrating multimodal data may enhance predictive accuracy and interpretability in surgical care.
Ryosuke Fukuyo +4 more
wiley +1 more source
Label-Dependencies Aware Recurrent Neural Networks
, 2017 In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited.
JL Elman +11 more
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, 2023 Transformers have completely taken by storm the field of sequence modelling with deep networks, becoming the standard for text processing, video, even images. RNNs that were once a very active engineering field have slowly faded into the void. All of them? No, some RNNs are bravely fighting back to claim state-of-the-art results in sequence tasks.
openaire +1 more source
Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source