Popular GitHub Repositories for Greedy Bfs

PyTorch implementation of beam search decoding for seq2seq models

SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas

AI research environment for the game of Snake ? .

CSE 571 Artificial Intelligence

This project is a meal planning app that generates personalized meal plans based on a user's caloric needs and Personal Information. It utilizes natural language processing to generate creative meal ideas using ingredients selected by the greedy algorithm.

implement basic and contextual MAB algorithms for recommendation system

AI plays snake game using BFS (Breadth-First Search) algorithm.

Uses GloVe embeddings and greedy sequence segmentation to semantically segment a text document into any number of k segments.

Code for "Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation"

The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism

Automatic maze generator (DFS) and maze solver (A*, DFS, BFS) in python.

Research Project on Multi-robot Target Tracking via Deep Reinforcement Learning

CS50’s Introduction to Artificial Intelligence with Python. This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation.

Python code (packaged in Docker container) to run the experiments in "A Greedy Algorithm for Quantizing Neural Networks" by Eric Lybrand and Rayan Saab (2020).

Greedy Search for Descriptive Spatial Face Features

A python package made to generate sequences (greedy and beam-search) from Pytorch (not necessarily HF transformers) models.

Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"

Spring 25 - Artificial Intelligence

A fast canonical-correlation-based greedy search algorithm for feature selection, system identification, data pruning, etc.

The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.