Results 31 to 40 of about 261,268 (307)

Improving speech recognition by revising gated recurrent units

, 2017
Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs).
Bengio, Yoshua, Brakel, Philemon, Omologo, Maurizio, Ravanelli, Mirco   +3 more
core   +1 more source

High‐Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million‐Patient Diabetes Cohort

Advanced Science, EarlyView.
This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño, Víctor M. de la Oliva Roque, Javier‐Ignacio Ramirez‐Lopez, David P. Kreil, Joaquín Dopazo, Carlos Loucera   +5 more
wiley   +1 more source

A Study of Hybrid Renewable Energy Production Scenarios Using a Long Short-Term Memory Method. A Case Study of Göksun

Elektronika ir Elektrotechnika
The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources.
Habibe Karayigit, Aykan Bolukbasi, Kadir Abaci, Ali Akdagli   +3 more
doaj   +1 more source

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

, 2016
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc.
Basu Chumki, Dai Andrew M, den Oord Aaron Van, Gopalan Prem K, He R., Jozefowicz Rafal, Melville Prem, Mikolov Tomas, Mikolov Tomas, Mnih Andriy, Rendle Steffen, Sutskever Ilya   +11 more
core   +1 more source

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source

دراسة إحصائية مقارنة للتنبؤ بإيرادات الموارد النفطية في جمهورية مصر العربية باستخدام نماذج الشبكات العصبية المتكررة [PDF]

Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗ
ملخص البحثيهدف هذا البحث إلى مقارنة كفاءة ودقة التنبؤ بإيرادات الموارد النفطية (ORR) باستخدام نماذج الشبكات العصبية المتكررة (RNN) مثل LSTM وGRU، بالإضافة إلى نماذج السلاسل الزمنية متعددة المتغيرات.
AHMED MOHAMED MOHEY ELDEEN AL HOSAFY
doaj   +1 more source

Diagonal RNNs in Symbolic Music Modeling

, 2017
In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of
Smaragdis, Paris, Subakan, Y. Cem
core   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Anharmonic phonon effects on linear thermal expansion of trigonal bismuth selenide and antimony telluride crystals

, 2018
We adopted and extended an efficient Gr\"uneisen formalism to study the phonon anharmonicity and linear thermal expansion coefficients (TECs) of trigonal bismuth selenide (Bi$_2$Se$_3$) and antimony telluride (Sb$_2$Te$_3$).
Gan, Chee Kwan, Lee, Ching Hua
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source