Nuclear Engineering International

CFS leverages DeepMind AI for SPARC

Google DeepMind has partnered with Commonwealth Fusion Systems to apply AI simulations and plasma control tools to advance ...

Learning The Most Rigorous Approaches To Validating Algorithms And Greatly Boosting AI Safety

Validating AI is increasingly getting societal attention. AI safety has been a low priority. No more. I explore validation as ...

Apple researchers explore how AI can predict bugs, write tests, and even fix code

Apple has published three interesting studies that offer some insight into how AI can improve workflows, quality, and productivity for developers.
Scientific Research Publishing

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE ...

ABSTRACT: Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor ...
IEEE

Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network

Abstract: We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep Sparse ...
Frontiers

BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders

Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
GitHub

Enhancing EV Cost Prediction Using AutoEncoder-Based Feature Learning

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
GitHub

Slow Deepsparse Benchmarking Result from Trained Model Using SparseML.

And here are related dependencies and training environment Libraries: torch==2.5.1 sparseml==1.8.0 deepsparse==1.8.0 ultralytics==8.0.124 onnx==1.14.1 onnxruntime==1.17.0 Training Environment: NVIDIA ...