GitHub

Detects anomalies in structured logs/events using unsupervised machine learning (e.g. neural-network autoencoder with embeddings and/or isolation forest).

Autoencoder score mapping: Reconstruction MSE is converted to a percentile rank within the baseline MSEs from the last retrain. The raw percentile is remapped through a piecewise-linear function so ...
Hacker

Building a Transformer From Scratch in Annotated PyTorch

A complete walkthrough of implementing the original Attention Is All You Need encoder-decoder Transformer—no torch. nn.Transformer, no shortcuts. The 2017 paper "Attention Is All You Need" by Vaswani ...
marktechpost

NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model

Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, ...
Reuters

Google works to erode Nvidia's software advantage with Meta's help

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...
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

Signal-Guided Masked Autoencoder for Wireless Positioning With Limited Labeled Samples

Abstract: In this paper, a signal-guided masked autoencoder (S-MAE) based semi-supervised learning framework is proposed for high-precision positioning with limited labeled channel impulse response ...
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 ...
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, ...