Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Report finds ‘disturbing’ normalization of banned books in U.S. ABC News’ Stephanie Ramos speaks with Jennifer Finley Boylan, president of PEN America, discusses a report that show there are more ...

Understand Local Response Normalization (LRN) in deep learning: what it is, why it was introduced, and how it works in convolutional neural networks. This tutorial explains the intuition, mathematical ...

Introduction: Early diagnosis of Alzheimer's disease (AD) remains challenging due to the high similarity among AD, mild cognitive impairment (MCI), and cognitively normal (CN) individuals, as well as ...

@InProceedings{pstone_simba, author = {Hojoon Lee and Youngdo Lee and Takuma Seno and Donghu Kim and Peter Stone and Jaegul Choo}, title = {Hyperspherical Normalization for Scalable Deep Reinforcement ...

Abstract: Batch normalization (BN) has proven to be a critical component in speeding up the training of deep spiking neural networks in deep learning. However, conventional BN implementations face ...

Abstract: Batch normalization (BN) is widely recognized as an essential method in training deep neural networks, facilitating convergence and enhancing model stability. However, in Federated Learning ...

Accurate and timely detection of diabetic retinopathy (DR) is crucial for managing its progression and improving patient outcomes. However, developing algorithms to analyze complex fundus images ...