Results 271 to 280 of about 16,838,211 (334)

Stock-level volatility forecasting using machine learning: A comparative study of GARCH, Random Forest, LSTM, and GRU on US Equities. [PDF]

PLoS One
Pragadeeshwaran RV, Tarun Narayanan R, Bhavadharini RM.   +2 more
europepmc   +1 more source

Joint Optimization of Trajectory-Resource Allocation and Deep Task Partial Offloading for MEC-Enabled Multi-UAV. [PDF]

Sensors (Basel)
Liu C, Wang Y, Mei H, Du S, Guo B.
europepmc   +1 more source

A critical review of degradation pathways and stability engineering for high-efficiency non-fullerene acceptor-based organic solar cells <i>via</i> multiscale computational approaches. [PDF]

RSC Adv
Qurat-Ul-Ain, Majid A, Raza MT, Ahmad S.
europepmc   +1 more source

SDG driven carbon aware machine learning model recommendation framework. [PDF]

Sci Rep
Reddy KUK, Sabarish BA, Sivakumar PB.
europepmc   +1 more source

Learning the reaction coordinate: collective variables from physical intuition to generative models.

Digit Discov
Talmazan RA, Chen CG, Tang C, Megías A, Contreras Arredondo S, Chipot C.   +5 more
europepmc   +1 more source

Energy-Efficient Deep Learning using Model Compression

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MEMTONIC: A Neuromorphic Accelerator for Energy Efficient Deep Learning

2020 57th ACM/IEEE Design Automation Conference (DAC), 2020
Most deep learning accelerators in the literature focus only on improving the design of inference phase. We propose a novel photonics-based backpropagation accelerator for high performance deep learning training.
Dharanidhar Dang, Sahar Taheri, B. Lin, D. Sahoo   +3 more
semanticscholar   +3 more sources

Structured Convolution Matrices for Energy-efficient Deep learning

CoRR, 2016
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices.
R. Appuswamy, T. Nayak, J. Arthur, S. Esser, P. Merolla, Jeffrey L. McKinstry, T. Melano, M. Flickner, D. Modha   +8 more
semanticscholar   +4 more sources

Multiperspective and Energy-Efficient Deep Learning in Edge Computing

IEEE Internet of Things Journal
The deployment of billions of Internet of Things (IoT) devices is driving unprecedented data generation at the network edge, demanding high computational power for real-time deep learning (DL) while raising serious concerns about energy consumption ...
Haitao Yuan, Jing-Ye Bi, Zi-Qi Wang, Jia Zhang, Meng-Chu Zhou, R. Buyya   +5 more
semanticscholar   +3 more sources