Name: Suhas Shrinivasan
Type: User
Company: https://sinzlab.org
Bio: Doctoral Researcher in Machine Learning and Computational Neuroscience at the University of Göttingen.
Twitter: suhas_shrinivas
Location: Göttingen, Germany
Blog: https://sinzlab.org/people/suhas_shrinivasan.html
Suhas Shrinivasan's Projects
Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".
Predicting travel speed given car-perspective video
Source code examples from the Parallel Forall Blog
A compiler for Cool. Cool (Classroom Object Oriented Language) is an object oriented programming language developed at Stanford for their Compilers course. Here's my implementation of the compiler in C. NOTE: WIP. DO NOT RELY ON CODE YET.
LaTeX files for the Deep Learning book notation
Docker image for scientific computing with PyTorch
This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a community of users and contributors by focusing initially on complete infra and documentation for how to use and create components.
A library for generative and sampling-based modeling and learning.
A toolset for generating image stimuli for insilico experiments
A car detection model implemented in Tensorflow.
Collection of simple-to-use methods to make visualization more convenient
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
Repo consisting of Python scripts to do music genre classification.
Code samples for my book "Neural Networks and Deep Learning"
Code for the paper Shrinivasan et al 2023, "Taking the Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system" published in NeurIPS 2023
Machine Learning Utils of Sinzlab
Audio processing
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
A Collection of Variational Autoencoders (VAE) in PyTorch.
So I've been interested in systems programming and tried my hand in writing a primitive assembler for SIC. Illustrates basic functioning of assemblers with minimal error checking.
Code base for the SENSORIUM competition.
Pipeline test
A bunch of methods to query and manipulate data from the Wikidata knowledge base (and also Wikipedia).