Results 71 to 80 of about 118,488 (274)
Advanced Science, EarlyView.Integrated multi‐omic profiling maps the gene‐regulatory landscape of the coelomic mesothelium across heart, lung, and pancreas. A cardiac‐restricted regulatory program is uncovered in which TBX20 activates heart mesothelial (epicardial) cis‐regulatory elements, while MAF emerges as a conserved regulator of mesothelial identity.
Quang Minh Dang +3 more
wiley +1 more source
A comparison of dropout and weight decay for regularizing deep neural networks [PDF]
, 2014 In recent years, deep neural networks have become the state-of-the art in many machine learning domains. Despite many advances, these networks are still extremely prone to overfit.
Slatton, Thomas Grant
core +1 more source
Shakeout: A New Approach to Regularized Deep Neural Network Training
, 2018 Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper,
Kang, Guoliang, Li, Jun, Tao, Dacheng
core +1 more source
NeuFair: Neural Network Fairness Repair with Dropout
Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and AnalysisThis paper investigates neuron dropout as a post-processing bias mitigation for deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in socially critical domains with significant fairness implications. While neural networks are exceptionally good at finding statistical patterns from data, they may encode and amplify ...
Vishnu Asutosh Dasu +3 more
openaire +2 more sources
Dropout with Tabu Strategy for Regularizing Deep Neural Networks [PDF]
The Computer Journal, 2019 Abstract Dropout has been proven to be an effective technique for regularizing and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability of p during the training stage of DNN to avoid overfitting.
Zongjie Ma +4 more
openaire +3 more sources
Learnable Diffusion Framework for Mouse V1 Neural Decoding
Advanced Science, EarlyView.We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng +2 more
wiley +1 more source
An Efficient Dropout for Robust Deep Neural Networks
Applied SciencesOverfitting remains a major difficulty in training deep neural networks, especially when attempting to achieve good generalization in complex classification tasks.
Yavuz Çapkan, Aydın Yeşildirek
doaj +1 more source
A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application
Advanced Science, EarlyView.This review presents a strategy‐centric framework for spatial omics technologies, organizing methods by how spatial information is experimentally encoded. It compares key performance trade‐offs across sequencing‐ and imaging‐based approaches, examines computational and practical limitations, and highlights biomedical applications. The analysis provides
Xiaofeng Wu +5 more
wiley +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Advanced Science, EarlyView.Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source