Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Last update: Nov 03, 2021

Related tags

Deep Learning iMOCA

Overview

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Requirements

The code is implemented in Python and requires the following packages:

Citation

If you use this code in your academic work please cite our JAIR paper: "A General Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization " and our workshop paper "Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations" , Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa.

Owner

GitHub Repository

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Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Related tags

Overview

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Requirements

Citation

Owner

Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Awesome-AI-books - Some awesome AI related books and pdfs for learning and downloading

Repository of our paper 'Refer-it-in-RGBD' in CVPR 2021

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Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

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PenguinSpeciesPredictionML - Basic model to predict Penguin species based on beak size and sex.

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