Avinash (baiydaavi)

baiydaavi

Geek Repo

Company:University of California, Davis

Location:Sacramento, CA

Home Page:www.linkedin.com/in/baidyaavinash

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Avinash's repositories

textgrad

TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients.

License:MITStargazers:0Issues:0Issues:0

rl-baselines3-zoo

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

License:MITStargazers:0Issues:0Issues:0

stable-diffusion-tensorflow

Stable Diffusion in TensorFlow / Keras

License:NOASSERTIONStargazers:0Issues:0Issues:0
Language:Jupyter NotebookLicense:MITStargazers:1Issues:0Issues:0

OoD

Repository for theory and methods for Out-of-Distribution (OoD) generalization

License:Apache-2.0Stargazers:0Issues:0Issues:0
Language:Jupyter NotebookLicense:Apache-2.0Stargazers:0Issues:0Issues:0

vonenet

Repository for VOneNet models

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:0Issues:0Issues:0

DomainBed

DomainBed is a suite to test domain generalization algorithms

Language:PythonLicense:MITStargazers:0Issues:0Issues:0

InvarianceUnitTests

Toy datasets to evaluate algorithms for domain generalization and invariance learning.

License:MITStargazers:0Issues:0Issues:0
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icebeem

Code for ICE-BeeM paper - NeurIPS 2020

License:MITStargazers:0Issues:0Issues:0

game-of-noise

Trained model weights, training and evaluation code from the paper "Increasing the robustness of DNNs against image corruptions by playing the Game of Noise"

License:MITStargazers:0Issues:0Issues:0

Best-README-Template

An awesome README template to jumpstart your projects!

License:MITStargazers:0Issues:0Issues:0

meta-RL

This repository implements the meta-reinforcement learning algorithm for a variety of cognitive tasks using two separate network architecture.

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machine_learning_with_tensorflow2

This repository will contain simple implementation of important machine learning algorithms using Tensorflow 2.

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bayesian_inference

In this project, we will use bayesian inference to estimate the matter density parameter, the dark energy density parameter and the Hubble constant using Supernova luminosity data.

Language:Jupyter NotebookStargazers:1Issues:0Issues:0
License:GPL-3.0Stargazers:0Issues:0Issues:0

code_review_DS4S

Toy repository for learning the open source workflow

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