Penny Liang: Understanding Large Models for Humanities Students (1.0)

Penny Liang's book, "Understanding Large Models for Humanities Students (1.0)," explains the core technologies of large ...

Machine Learning Enhances Quantum Physics: Revealing Hidden Dynamics in Bose Gases

Overall, the application of the Variational Autoencoder not only enhances the analytical capabilities of quantum simulations but also offers new perspectives for understanding the physical properties ...
GitHub

A Step Towards Music Generation Foundation Model

We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic ...
Scientific Research Publishing

Machine Learning for Financial Risk Management: Modeling Time-Varying Factor Sensitivities Using Factor Variational Autoencoders ()

Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management.
T&D World

Sandia National Laboratories’ Research Team Develop AI Algorithms to Detect Physical Issues, Cyberattacks within Grid

Sandia National Laboratories cybersecurity expert Adrian Chavez, left, and computer scientist Logan Blakely work to integrate a single-board computer with their neural-network AI into the Public ...
IGN

The Best Streaming Services for Watching NFL Games Live in 2025

The 2025 NFL season officially kicked off last week and if you're hoping to stream all of the rest of the games online this year, it is once again unnecessarily confusing. There is no single place ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
C&EN

A Learning Framework for Atomic-Level Polymer Structure Generation

School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive, Atlanta, Georgia 30332, United States School of Computational Science and Engineering, Georgia Institute ...
IEEE

XChange: An Explainable Dynamic Convolutional Autoencoder for Unsupervised Change Detection

Abstract: Explainable artificial intelligence (XAI) approaches started to be studied in the last period to improve the interpretability of increasingly complex deep learning (DL) methods for remote ...
EurekAlert!

Zhu harnessing interpretable neuro-symbolic learning for reliable ranking

Ziwei Zhu, Assistant Professor, Computer Science, College of Engineering and Computing (CEC), received funding for the project: “III: Small: Harnessing Interpretable Neuro-Symbolic Learning for ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...