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Hlavní autor: Muhammad, Akhyar
Médium: Recurso digital
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Vydáno: Zenodo 2026
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On-line přístup:https://doi.org/10.5281/zenodo.20072033
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author Muhammad, Akhyar
author_facet Muhammad, Akhyar
contents Oxide-JS (formerly ML-V1) is a high-performance, modular machine learning library for TypeScript, accelerated by optimized Rust kernels via NAPI-RS. This library serves as the successor to the ML-V1 stability research artifacts (v2.3.0) used in the empirical analysis of recurrent neural networks for Indonesian sentiment classification.
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_20072033
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Oxide-JS: A High-Performance Modular TypeScript ML Library with Rust Kernels
Muhammad, Akhyar
machine-learning
typescript
rust
napi-rs
monorepo
neural-networks
transformers
deep-learning
high-performance-computing
Oxide-JS (formerly ML-V1) is a high-performance, modular machine learning library for TypeScript, accelerated by optimized Rust kernels via NAPI-RS. This library serves as the successor to the ML-V1 stability research artifacts (v2.3.0) used in the empirical analysis of recurrent neural networks for Indonesian sentiment classification.
title Oxide-JS: A High-Performance Modular TypeScript ML Library with Rust Kernels
topic machine-learning
typescript
rust
napi-rs
monorepo
neural-networks
transformers
deep-learning
high-performance-computing
url https://doi.org/10.5281/zenodo.20072033