Kunal Ghosh
kunalghosh
PhD student at Aalto University. Bayesian machine learning and its applications in materials science.
Languages
Top Repositories
Code for deep learning models to predict molecular electronic properties.
Simple Exercise to implement Forward and Reverse KL-Divergence minimization.
Using CNN to predict molecule spectra
Convert a list of arxiv urls (maybe your backlog of to-read papers) into a easy to read set of HTML Cards (with title, authors, abstract, link to pdf)
Using Gaussian processes for multi-fidelity prediction.
Repositories
56Simple Exercise to implement Forward and Reverse KL-Divergence minimization.
DScribe is a python package for creating machine learning descriptors for atomistic systems.
STM32F401 Devevelopment Board
https://opencatalystproject.org/
Using CNN to predict molecule spectra
No description provided.
Using Gaussian processes for multi-fidelity prediction.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Code for deep learning models to predict molecular electronic properties.
Zheng Zhao's doctoral dissertation from Aalto University
Declarative statistical visualization library for Python
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
No description provided.
Up-to-date version of the CV
No description provided.
Convert a list of arxiv urls (maybe your backlog of to-read papers) into a easy to read set of HTML Cards (with title, authors, abstract, link to pdf)
No description provided.
Bayesian Data Analysis course at Aalto
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
PyMC project website and blog!
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
Given PubMed and Arxiv xml dumps, returns a summary
No description provided.
Python challenge
Cookiecutter template to generate the directory structure needed for runs
Repo to make predictions using the deep learning spectroscopy code
No description provided.
Efficient, lightweight, variational inference approximation bounds
Repo of my implementation of Gaussian processes in PyTorch. Also look at AlejandroCatalina/gp-pytorch (Where Alex and I do the same thing) and also GPyTorch.
Assignments for the Kernel Methods course taught by Prof. Juho Rousu at Aalto University (https://mycourses.aalto.fi/course/view.php?id=13088)