Results 71 to 80 of about 73,468 (309)
Structural Compression of Convolutional Neural Networks with Applications in Interpretability
Frontiers in Big Data, 2021 Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs make them difficult for human interpretation or understanding in ...
Reza Abbasi-Asl +4 more
doaj +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
CNNs Avoid the Curse of Dimensionality by Learning on Patches
IEEE Open Journal of Signal Processing, 2023 Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori ...
Vamshi C. Madala +2 more
doaj +1 more source
Electrically Coded Retinomorphic Spectrophotodetector
Advanced Materials, EarlyView.Self‐powered retinomorphic pyro‐photodetector is demonstrated that avoids machine‐learning post‐processing and covers 365–940 nm. Electrostatic balancing of built‐in potential produces an electrical wavelength code, delivering <3 nm wavelength decoding accuracy with ∼46 µs response.
Mohit Kumar, Hyunmin Dang, Hyungtak Seo
wiley +1 more source
Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users
Applied SciencesIn recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems.
Ilaria Cacciari, Anedio Ranfagni
doaj +1 more source
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
Entropy, 2023 Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability.
Kristoffer K. Wickstrøm +5 more
doaj +1 more source
Advanced Materials, EarlyView.Organic electrochemical synaptic transistors based on sidechain‐engineered conjugated polyelectrolytes reveal that cationic sidechains enable efficient volumetric ion penetration and dense backbone doping, leading to enhanced transconductance and long‐term synaptic retention.
Haim Kwon +6 more
wiley +1 more source
Learning Convolutional Neural Networks in presence of Concept Drift [PDF]
, 2019 Designing adaptive machine learning systems able to operate in nonstationary conditions, also called concept drift, is a novel and promising research area.
DIsabato S., Roveri M.
core +1 more source
Two-Microphone End-to-End Speaker Joint Identification and Localization Via Convolutional Neural Networks [PDF]
, 2020 We present an end-to-end scheme based on convolutional neural networks (CNNs) for speaker joint identification and localization. We investigate the possibility to estimate both the direction of arrival (DOA) and the identity of the speaker in far-field ...
Gian Luca Foresti +5 more
core +1 more source
Transform Domain Learning for Image Recognition
IEEE AccessImage and video classification are distinct tasks in computer vision. Three-dimensional convolutional neural networks (3D CNNs) are commonly employed for video classification, while two-dimensional convolutional neural networks (2D CNNs) are more ...
Dengtai Tan, Jinlong Zhao, Shichao Li
doaj +1 more source