Zeinab Rahbar's repositories

GCNRW-Graph-Convolutional-Network-With-Random-Weights-

Here is an implementation of Title: "Are Graph Convolutional Networks With Random Weights Feasible?" Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Year: 2022

Language:Jupyter NotebookStargazers:4Issues:0Issues:0
Language:Jupyter NotebookStargazers:2Issues:0Issues:0

Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS

This ia a simple implementation of SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS on three datasets(Cora, PubMed and Citeseer) and inspecting changing the first layer activation function of this model from ReLU to Linear

Language:Jupyter NotebookStargazers:2Issues:0Issues:0
Language:PythonStargazers:2Issues:0Issues:0

translate-english-subtitles-to-persian-

This python code gets the English subtitle file (.srt) and provide Persian subtitle file (.srt)

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:2Issues:0Issues:0

Cayleynets-Graph-convolutional-neural-networks-with-complex-rational-spectral-filters-

reimplement paper: Cayleynets: Graph convolutional neural networks with complex rational spectral filters,

Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0

Evolutionary_algorithm_for_solving_nQueen_restricted_board_with_pawns

Here I employed an evolutionary algorithm that solve n queen problem, but it is not the traditional n queen problem. In this problem there are fixed soldiers in our board which are randomly generated and then regarding this we should have employed the evolutionary algorithm. The Persian report is available via contact, but English report not yet!

Language:Jupyter NotebookStargazers:1Issues:0Issues:0

googlescholar_scraper

The "googlescholar_scraper" project is a Python script that utilizes web scraping techniques to extract paper data from Google Scholar. The script searches for papers based on a specific query, in this case, "graph network multimodal." It then scrapes the search results from the first 20 pages of Google Scholar.

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0

LLM_Science_Competition

A code to a solution on this competition

Language:Jupyter NotebookStargazers:1Issues:1Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0

Neural-Network-Homework

Here is my university Neural Network course homework containing implementing perceptron and convolutional networks from scratch and experimenting using different activation, loss function, ... on them.

Language:Jupyter NotebookStargazers:1Issues:0Issues:0

OfficeCaltechDomainAdaptation

Code, Images and Features for the Domain Adaptation benchmark dataset Office-Caltech

Stargazers:1Issues:0Issues:0

Picture_dictionary_android_application

Implementing an android application through android studio with java

Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:PythonStargazers:1Issues:0Issues:0
Stargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Evolutionary_algorithm_for_night_tour_problem-chessboard-restricted-with-the-pawns-and-nights-

This is a code to an evolutionary algorithm that help us find the best chess board situation in 300 generations. there is one knight which can be moved through board and there are some pawns and other nights in the board which threatening them has negative and positive points based on their type

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Genetic-Algorithm-for-Polynomial-Curve-Fitting

This repository contains the implementation of a genetic algorithm for polynomial curve fitting using Python. The algorithm utilizes a population-based approach to approximate complex polynomial functions and discover optimal coefficients for curve fitting.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0