머신러닝 · GitHub

Classify music genre from a 10 second sound stream using a Neural Network.

Accurate and fast classification losses for SDCA and neural networks: cross-entropy, top-k, multiclass and multilabel SVM, and distillation losses for PyTorch, JAX, NumPy, Rust, C/C++ and MATLAB

MLPerf® Storage Benchmark Suite

MLDB is the Machine Learning Database

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. (ICCV 2021 Oral)

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

SDK libraries for Modal

[ICLR 2026] GRAPE: Group Representational Position Encoding (https://arxiv.org/abs/2512.07805)

• Scalability: Efficient training procedures optimized for large-scale datasets and multi-GPU setups.
• Flexible Data Support: Compatible with popular datasets like Fineweb-Edu-100B and OpenWebText.

Reversi reinforcement learning by AlphaGo Zero methods.

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

Named Entity Recognition using multilayered bidirectional LSTM

Pytorch implementation of JointBERT: "BERT for Joint Intent Classification and Slot Filling"

torch_musa is an open source repository based on PyTorch, which can make full use of the super computing power of MooreThreads graphics cards.