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

Udacity Suse Cloud Native Foundations Scholarship Course Walkthrough

This repository will be a summary and outlook on all our open, medical, AI advancements.

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An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

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This is the repository for Learning to Generate Piano Music With Sustain Pedals

Combinatorial model of ligand-receptor binding

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing

Python parser for DTED data.

Styled Handwritten Text Generation with Transformers (ICCV 21)

Implementation of the Swin Transformer in PyTorch.

Data and Code for paper Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graph is available for research purposes.

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