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Ferroelectric memory enables one chip to sample randomness and compute for generative AI

For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...
IEEE

Autoencoder-Based Recommender System Exploiting Natural Noise Removal

Abstract: Collaborative filtering (CF) is a widely used technique in recommender systems by automatically predicting the user’s latent interests based on many users’ historical rating data. To improve ...
IEEE

Optimized Deep Autoencoder Model for Internet of Things Intruder Detection

Abstract: The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of ...
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 ...
Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
GitHub

Generic Deep Autoencoder for Time-Series

This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural ...