de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.

An ocean-mining company has funded some of the most comprehensive scientific studies of the deep seabed to date, and peer-reviewed results have begun to emerge. A collage of foraminifera, a kind of ...

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 ...

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: With the advancement of technology and the growth of human demand, pedestrian re-identification is a key technology of intelligent systems and plays an important role in daily life.

Dive into the concept of Local Response Normalization (LRN) in deep learning. This video breaks down its role in neural networks and how it helps improve model performance. The Lisa Cook Case Could Be ...

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 ...