STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Abstract: Seismic erratic noise, characterized by large isolated events following non-Gaussian distributions, significantly degrades seismic data quality by masking useful signals. Methods based on ...

Abstract: Previous research has proposed a number of techniques for the extraction of single-trial event-related neural activity (ERNA). However, these single-trial extraction techniques did not ...

Overview. DiffusionRenderer is a general-purpose framework that achieves high-quality geometry and material estimation from real-world videos (inverse rendering), and photorealistic image/video ...

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...