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Rohit Namjoshi's Projects

bicyclegan icon bicyclegan

[NIPS 2017] Toward Multimodal Image-to-Image Translation

bmad icon bmad

Resources for Bayesian Models for Astrophysical Data - Hilbe, de Souza and Ishida, 2016, Cambridge University Press

carlson icon carlson

A Mathematica package for evaluating Carlson elliptic integrals

catlab.jl icon catlab.jl

A framework for applied category theory in the Julia language

catpapers icon catpapers

Cool vision, learning, and graphics papers on Cats!

cc-pyspark icon cc-pyspark

Process Common Crawl data with Python and Spark

ccwmf icon ccwmf

Finding correlating concepts with Math Formulas using data scraped from arXiv.

char-rnn icon char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

cip icon cip

Computational Intelligence Packages (CIP) for Mathematica

citation-graph icon citation-graph

Automatic generation of a citation graph of some selected papers by inputting a .bib file exported by Mendeley

classificaio icon classificaio

This repository contains ClassificaIO, a Python package that provides a graphical user interface (GUI) for machine learning algorithms from scikit-learn.

classify-growth-rates-for-wolfram-models icon classify-growth-rates-for-wolfram-models

This is a project to classify the rates of growth of different wolfram models, which consist of rules and initial conditions. Hence, when talking about growth rates, we will be measuring growth rates for particular rules and initial conditions.

code_and_data_of_vpe_loc_alg icon code_and_data_of_vpe_loc_alg

This repository records all important source code and raw data used in our research "Distributed Localization Based on Virtual Particle Exchange Method with Applications to Shape Formation of Robot Swarm".

codeparser icon codeparser

Parse Wolfram Language source code as abstract syntax trees (ASTs) or concrete syntax trees (CSTs)

codex-1.0.0 icon codex-1.0.0

A Mathematica package to calculate the Wilson Coefficients of SMEFT operators (up to dimension - 6) to connect some Beyond Standard Model (BSM) theory with weak scale precision observables, using Covariant Derivative Expansion. Works for single and multiple degenerate heavy field propagators, at tree and one-loop level.

cognitozoo icon cognitozoo

Mathematica neural net implementations (uses Mathematica MXNet v11 Machine Learning functionality)

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