Name: Parsa Kavehzadeh
Type: User
Company: York University
Bio: MSc student.
Interested in Information Retrieval, Data Mining, Information Visualization, Social Network Analysis and Machine Learning :)
Location: Toronto, Ontario
Blog: parsareal.github.io
Parsa Kavehzadeh's Projects
A defense method employing masked language modeling feature of BERT to be robust against adversarial examples in text
Finding optimum routes between cities by implementing search algorithms Astar, BFS and DFS.
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Midterm project of Computer Network fall 2018
Implementation and experiments of graph embedding algorithms.
Solving Graph Painting problem by implementing Hill Climbing and Genetic algorithms.
Image Processing and Video Analysing using OpenCV
A user interface of Kilid, a platform for selling and buying houses, implemented by Vuejs.
A webserver for an online platform for selling and buying houses.
Clustering of LFP signals including preprocessing the data and employing clustering methods like DBSCAN, Hierarchical, and KMeans.
An online news search engine with various features such as vectorizing, calculating tf-idf scores, clustering, classification text documents in Web.
A Peer to Perr network for Sharing File using java
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
Implementation of a secure chatroom with encrypting and decrypting the text data transferred between peers by AES and RSA algorithms.
User Requairment, System Analysis and Design using UML, ProcessModel, ERD Diagram and All Documents
This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
Must-read Papers on Textual Adversarial Attack and Defense
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP
An attempt to defend from typo and synonyms attacks simultaneously.
This repository contains demos I made with the Transformers library by HuggingFace.
A playbook for systematically maximizing the performance of deep learning models.
What I learnt from the optimization course, under supervision of Dr. Maryam Amirmazlaghani at AUT.