本内容遵循CC 4.0 BY-SA版权协议 简介：本资源是一份面向机器学习初学者与MATLAB实践者的Autoencoder自编码器教学仿真项目，聚焦数据降维、无监督特征提取与下游分类任务的端到端实现。资源包含 ...

本内容遵循CC 4.0 BY-SA版权协议 简介：这个Matlab降维工具包整合了40多种主流降维方法，包括PCA、LDA、t-SNE、AutoEncoder、Isomap、LLE、Laplacian Eigenmaps、Kernel PCA、MVU、SNE、NPE、LPP、CCA等，覆盖线性/非 ...

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how ...

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly ...

A variational autoencoder (VAE) is one of several generative models that use deep learning to generate new content, detect anomalies and remove noise. Suited for generating synthetic time series data ...

Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...

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 ...

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 ...

Abstract: This paper presents an innovative guide for optimizing autoencoder performance, specifically targeting anomaly detection tasks. In addressing prevalent issues in deep learning algorithms, ...

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 ...