Name: Amir M. Mir
Type: User
Company: Delft University of Technology
Bio: PhD Candidate | Software Developer | Interested in the ML4SE research
Twitter: amir_mir93
Location: Delft, The Netherlands
Blog: https://mirblog.net/
Amir M. Mir's Projects
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Armadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net
Python AST read/write
A common base representation of python source code for pylint and other projects
A curated list of awesome embedding models tutorials, projects and communities.
A curated list of awesome READMEs
Set of tools to help working with "Big Code"
TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
Empirical Study of Transformers for Source Code & A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code
A toolkit for pre-processing large source code corpora
Contrastive Code Representation Learning: functionality-based JavaScript embeddings through self-supervised learning
Repository for the code of the "A Convolutional Attention Network for Extreme Summarization of Source Code" paper
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
DeepBugs is a framework for learning bug detectors from an existing code corpus.
Find and copy needed dynamic libraries into python wheels
Deep Learning Type Inference of Python Function Signatures using their Natural Language Context
Dynamic analysis framework for Python
Easy Data Program's source code
A collection of (mostly) technical things every software developer should know
Analyse package dependency networks at the call graph level
Graph Auto-Encoder in PyTorch
Sample Code for Gated Graph Neural Networks
A script that clones Github repositories of users and organizations.
A TeX template for writing a thesis (Islamic Azad University)
iFeature is a comprehensive Python-based toolkit for generating various numerical feature representation schemes from protein or peptide sequences. iFeature is capable of calculating and extracting a wide spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. Furthermore, iFeature also integrates five kinds of frequently used feature clustering algorithms, four feature selection algorithms and three dimensionality reduction algorithms.
Probabilistic Type Inference using Graph Neural Networks
Pre-built wheels for LIBTwinSVM library
A Library for Twin Support Vector Machines
LightTwinSVM Program - Simple and Fast Implementation of Standard TwinSVM Classifier