Scientific Research Publishing

A Spatio-Temporal Prediction Model for Wall Turbulence Based on Hybrid Neural Networks ()

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant challenge for ...
Tech Xplore

Improving AI models' ability to explain their predictions

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...
Case Western Reserve University

Required Annual Title IX and Non-Discrimination (CAMPUS Act) Training

Case Western Reserve prohibits discrimination and harassment in education and employment, including sex discrimination and sex-based harassment, and discrimination based on national or ethnic origin, ...
Electronic Design

Modular Voltage Regulator Brings the Power to Big AI Chips

Modern-day graphics processing units (GPUs) and other AI chips are massive. The unfortunate tradeoff is that they also consume an enormous amount of power, and their power needs are rising ...
marktechpost

This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost Alternative to End-to-End Sparse Autoencoder Training for Interpretability

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...
Army Times

Army slashes mandatory training requirements with regulation update

Sgt. Brenden Lopez uses a computer on Joint Base Lewis-McChord, Washington, for a four-session online intervention designed to improve connection with and promote the care of one’s body. (Christopher ...
IEEE

Spiking Generative Models Based on Variational Autoencoder and Adversarial Training

Abstract: Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking ...
GitHub

Questions About Autoencoder Training: Loss Function, Training Steps, and Learning Rate Scheduler

Which loss function did you use when training the autoencoder? Did you directly compute the MSE between the predicted SDF values and the ground truth? Additionally, how many steps did you train for?
Cloud Computing

5 of the best: cloud technology training platforms

Cloud technologies are at the heart of digital transformation today, driving innovation and agility across industries. With the rapid adoption of platforms like AWS, Azure, and Google Cloud, ...
IEEE

Pan-Cancer Histopathology WSI Pre-Training With Position-Aware Masked Autoencoder

Abstract: Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus on ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...