Results 71 to 80 of about 75,080 (277)
Advanced Science, EarlyView.UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou +4 more
wiley +1 more source
Advanced Science, EarlyView.By combining ionic nonvolatile memories and transistors, this work proposes a compact synaptic unit to enable low‐precision neural network training. The design supports in situ weight quantization without extra programming and achieves accuracy comparable to ideal methods. This work obtains energy consumption advantage of 25.51× (ECRAM) and 4.84× (RRAM)
Zhen Yang +9 more
wiley +1 more source
BMJ Open, 2020 Objectives To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.Design This is a cross-sectional, proof-of ...
Jing Sun +2 more
doaj +1 more source
Neural Information Processing and Time‐Series Prediction with Only Two Dynamical Memristors
Advanced Electronic Materials, EarlyView.The present study demonstrates how simple circuits with only two memristive devices are utilized to perform high complexity temporal information processing tasks, like neural spike detection in noisy environment, or time‐series prediction. This circuit simplicity is enabled by the dynamical complexity of the memristive devices, i.e.
Dániel Molnár +12 more
wiley +1 more source
Applied Sciences, 2020 In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative ...
Iqra Hameed, Pham-Viet Tuan, Insoo Koo
doaj +1 more source
Advanced Electronic Materials, EarlyView.A physics‐based compact model for Conductive‐Metal‐Oxide/HfOx ReRAM, accounting for ion dynamics, electronic conduction, and thermal effects, is presented. Accurate and versatile simulations of analog non‐volatile conductance modulation and memory state stabilization enable reliable circuit‐level studies, advancing the optimization of neuromorphic and ...
Matteo Galetta +9 more
wiley +1 more source
A survey of efficient deep neural network
Dianxin kexue, 2020 Recently,deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure,DNN’s performance is rapidly increasing.However,deeper and lager deep
Rui MIN
doaj +2 more sources
A Worm Detection System Based on Deep Learning
IEEE Access, 2020 In today's cyber world, worms pose a great threat to the global network infrastructure. In this paper, we propose a worm detection system based on deep learning.
Hanxun Zhou +5 more
doaj +1 more source
Advanced Electronic Materials, EarlyView.This review surveys oxide‐semiconductor devices for in‐memory and neuromorphic computing, highlighting recent progress and remaining challenges in charge‐trap, ferroelectric, and two‐transistor devices. Oxide semiconductors, featuring ultra‐low leakage, low‐temperature processing, and back‐end‐of‐line compatibility, are explored for analog in‐memory ...
Suwon Seong +4 more
wiley +1 more source
Knowing what you know in brain segmentation using Bayesian deep neural networks
, 2019 In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Bandettini, Peter +9 more
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