eLife

Reconstructing voice identity from noninvasive auditory cortex recordings

A low-dimensional voice latent space derived from deep learning captures speaker-identity representations in the temporal voice areas and supports reconstruction of voices preserving identity ...
EurekAlert!

Single device enables complex information processing: Memristive oscillators break computational bottlenecks at the edge of chaos

A research team from Peking University has successfully developed a vanadium oxide (VO₂)-based “locally active memristive ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
IEEE

Layer Normalization Enhanced Geometric Feature Fitting Neural Network for Flush Air Data Sensing

Abstract: The flush air data sensing (FADS) method based on artificial neural networks (ANNs) has been widely studied and applied in air data sensing for advanced aircraft. Most current methods focus ...
TMCnet

Bridging Silence: A Real-Time Sign Language to English Text Translation System Using Python, OpenCV, and Convolutional Neural Networks

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive ...
Learn With Jay on MSN

Activation functions in neural networks: Types and examples

In this video, we will see What is Activation Function in Neural network, types of Activation function in Neural Network, why ...

Syntiant Expands NDP115 Portfolio with New eWLB and Ultra-Thin Packages, Bringing Advanced Edge AI to Ultra-Compact Devices

Syntiant Corp., the recognized leader in ultra-low-power edge AI deployment, today announced two new package options for its NDP115 Neural Decision Processor(TM), extending the chip's reach into ...
IEEE

Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...