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BIG-bench Machine Learning

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전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 2
보존된 평가 기록: 2

This branch include most common machine learning fundamental algorithms.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jmdvinodjmd/LIBS2ML

공식 코드

Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning

https://github.com/jmoraispk/position-beam-prediction

공식 코드

Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?

https://github.com/jnhujnhu/VR-SGD

공식 코드

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

https://github.com/joey-shen/mlph

공식 코드

Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring

https://github.com/johannbrehmer/higgs_inference

공식 코드

A Guide to Constraining Effective Field Theories with Machine Learning

https://github.com/johannesbrust/SNLLS

공식 코드

Nonlinear Least Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates

https://github.com/johkruse/xai-for-afrr-activation

공식 코드

Secondary control activation analysed and predicted with explainable AI

https://github.com/john-hawkins/MinViME

공식 코드

Minimum Viable Model Estimates for Machine Learning Projects

https://github.com/john-tornblom/VoTE-scaling-experiments

공식 코드

Scaling up Memory-Efficient Formal Verification Tools for Tree Ensembles

https://github.com/john-tornblom/vote

공식 코드

Formal Verification of Input-Output Mappings of Tree Ensembles

https://github.com/jonathanking/sidechainnet

공식 코드

SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

https://github.com/josesousaribeiro/Pred2Town

공식 코드

Prediction of Homicides in Urban Centers: A Machine Learning Approach

https://github.com/jostmey/rwa

공식 코드

Machine Learning on Sequential Data Using a Recurrent Weighted Average

https://github.com/joymallyac/Fair-SMOTE

공식 코드

Bias in Machine Learning Software: Why? How? What to do?

https://github.com/jpadantas/effective-missile-launch

공식 코드

Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations

https://github.com/jphall663/interpretable_machine_learning_with_python

공식 코드

On the Art and Science of Machine Learning Explanations

https://github.com/jphall663/jsm_2018_paper

공식 코드

On the Art and Science of Machine Learning Explanations

https://github.com/jphall663/kdd_2019

공식 코드

Proposed Guidelines for the Responsible Use of Explainable Machine Learning

https://github.com/jphall663/responsible_xai

공식 코드

Proposed Guidelines for the Responsible Use of Explainable Machine Learning

https://github.com/jpkli/p6

공식 코드

P6: A Declarative Language for Integrating Machine Learning in Visual Analytics

https://github.com/jrash/chemmodlab

공식 코드

chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models

https://github.com/jschuetzke/synthetic-spectra-generation

공식 코드

A universal synthetic dataset for machine learning on spectroscopic data

https://github.com/jsfunc/best-arm-identification

공식 코드

Learning the distribution with largest mean: two bandit frameworks

https://github.com/jspieler/QBAF-Learning

공식 코드

Learning Gradual Argumentation Frameworks using Genetic Algorithms

https://github.com/jsyoon0823/SRA_TSTR

공식 코드

Measuring the quality of Synthetic data for use in competitions

https://github.com/juhgiyo/deepsim

공식 코드

DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo

https://github.com/jules-leguy/bbomol

공식 코드

Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties

https://github.com/juliaherbinger/repid

공식 코드

REPID: Regional Effect Plots with implicit Interaction Detection

https://github.com/juliuskunze/eve

공식 코드

Adaptive Optimization with Examplewise Gradients

https://github.com/junlulocky/AirTicketPredicting

공식 코드

Machine learning modeling for time series problem: Predicting flight ticket prices

https://github.com/jussitohka/ML_evaluation_tutorial

공식 코드

Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial

https://github.com/jwangjie/An-Intuitive-Tutorial-to-Gaussian-Processes-Regression

공식 코드

An Intuitive Tutorial to Gaussian Process Regression

https://github.com/jwangjie/Gaussian-Processes-Regression-Tutorial

공식 코드

An Intuitive Tutorial to Gaussian Process Regression

https://github.com/jwangjie/gaussian-process-regression-tutorial

공식 코드

An Intuitive Tutorial to Gaussian Process Regression

https://github.com/jwwthu/DL4Climate

공식 코드

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

https://github.com/kach/gradient-descent-the-ultimate-optimizer

공식 코드

Gradient Descent: The Ultimate Optimizer

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