Popular GitHub Repositories for Grid Cells

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Official PyTorch implementation of VoxFormer [CVPR 2023 Highlight]

AI-powered Jupyter Notebook — use local AI to generate and edit code cells, automatically fix errors, and chat with your data

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Not only automatic, but also intelligent. An Intelligent data Visualization System, based on LLM.

Grid2Op a testbed platform to model sequential decision making in power systems.

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

Generate, downscale, change palletes and restore pixel art images with SDXL. Nodes for Astropulse/pixeldetector, Palette Loader from Image, Grid view for palettes etc.

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

A python package for simulating movement and spatial cell types (e.g. place cells, grid cells) in continuous environments.

Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"

SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving

A collection of algorithms used for any-angle pathfinding with visualisations.

Language Modeling

Code for Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, CVPR '19