Scientific Research Publishing

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...
IEEE

Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms

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

Swapping Autoencoder for Deep Image Manipulation

We provide the pretrained models and also several images that reproduce the figures of the paper. Please download and unzip them here (2.1GB) (Note: this is a http ...
IEEE

Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection

Abstract: Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal ...