Create powerful Hydra applications without the yaml files and boilerplate code.
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Updated
May 29, 2024 - Python
Create powerful Hydra applications without the yaml files and boilerplate code.
Pytorch & Lightning based framework for research and ml-pipeline automation.
Online Handwritten Text Recognition (HTR) system implemented with PyTorch. Based on https://doi.org/10.1007/s10032-020-00350-4.
A standard framework for modelling Deep Learning Models for tabular data
A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected a…
Config-based framework for organized and reproducible deep learning. MONAI Bundle + PyTorch Lightning.
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
PyTorch-IE: State-of-the-art Information Extraction in PyTorch
Lightweight framework for data-loading with PyTorch and harvesting the power of (PyTorch) lightning
Quadra: Effortless and reproducible deep learning workflows with configuration files.
專題分類動作的程式
Japanese Spelling Error Corrector using BERT(Masked-Language Model). BERTに基づいて日本語校正
Library for metric learning pipelines and models.
so-vits-svc fork with realtime support, improved interface and more features.
Pytorch implementation of "Genie: Generative Interactive Environments", Bruce et al. (2024).
An autoregressive forecasting implementation of a LSTM network, NBEATS architecture and Autoformer architecture on rupee dollar exchange rates using pytorch, pytorch lightning, pytorch-forecasting, and GluonTS
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