Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Last update: Aug 19, 2022

Overview

Sarus published models

Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

The required packages are managed with pipenv and can be installed using pipenv install. Please see the pipenv documentation for more information.

Philosophy

These models' implementations are intended to be easy to read and to adapt by making use of the latest Tensorflow 2 library and Keras API.

Basic usage

To install and train a model.

pipenv install
pipenv shell
python train.py

To visualize losses and reconstructions.

tensorboard --logdir ./logs/

Available models

Owner

Sarus Technologies

GitHub Repository https://sarus.tech/

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Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Related tags

Overview

Sarus published models

Philosophy

Basic usage

Available models

Owner

Sarus Technologies

ICCV2021 Expert-Goal Trajectory Prediction

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

Simple renderer for use with MuJoCo (>=2.1.2) Python Bindings.

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

Convert scikit-learn models to PyTorch modules

[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

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