Results 111 to 120 of about 3,605,315 (303)
Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications
SensorsIn order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations.
Sara E. Abdelbaset +4 more
doaj +1 more source
TI-CNN: Convolutional Neural Networks for Fake News Detection
CoRR, 2018 With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking.
Yang Yang 0122 +5 more
openaire +2 more sources
Transfer learning between texture classification tasks using convolutional neural networks [PDF]
, 2015 Conteúdo online de acesso restrito pelo editorConvolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years.
Oliveira, Luiz S. +3 more
core
Gradient-Based Pooling for Convolutional Neural Networks
, 2019 Pooling layers are an important part of convolutional neural networks (CNNs). They reduce the dimensionality of feature maps and pass salient information to subsequent layers. In this paper, we introduce a novel gradient-based feature pooling method that
Gao, Y +5 more
core +1 more source
Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Advanced Science, EarlyView.FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +1 more source
Comparing Incremental Learning Strategies for Convolutional Neural Networks
, 2016 In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features.
LOMONACO, VINCENZO +3 more
core +1 more source
Advanced Science, EarlyView.Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li +6 more
wiley +1 more source
Training convolutional neural networks with the Forward–Forward Algorithm
Scientific ReportsRecent successes in image analysis with deep neural networks are achieved almost exclusively with Convolutional Neural Networks (CNNs), typically trained using the backpropagation (BP) algorithm.
Riccardo Scodellaro +3 more
doaj +1 more source
State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs). [PDF]
J Healthc Inform Res, 2023 Harrison P, Hasan R, Park K.
europepmc +1 more source
Ferroelectric Devices for In‐Memory and In‐Sensor Computing
Advanced Science, EarlyView.Inspired by biological systems, in‐memory and in‐sensor computing overcome von Neumann bottlenecks. Ferroelectric devices can mimic synaptic functions and sense stimuli like light or force, therefore are ideal for these paradigms. This review introduces the ferroelectric devices applied for in‐memory and in‐sensor computing, covering their structures ...
Hong Fang +5 more
wiley +1 more source