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BIG-bench Machine Learning

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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/bhanukaManesha/embarrassingly-simple-classifier-comparison

공식 코드

An embarrassingly simple comparison of machine learning algorithms for indoor scene classification

https://github.com/bhargavvader/pycobra

공식 코드

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

https://github.com/bhklab/aaai21_survival_prediction

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Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

https://github.com/big-data-lab-team/accident-prediction-montreal

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High-Resolution Road Vehicle Collision Prediction for the City of Montreal

https://github.com/bio-hpc/sibila

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SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts

https://github.com/bioinf-jku/SNNs

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Self-Normalizing Neural Networks

https://github.com/biolearning-stadius/chemgrapher-self-rich-labeling

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Self-Labeling of Fully Mediating Representations by Graph Alignment

https://github.com/blaschko/ksupport

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A Note on k-support Norm Regularized Risk Minimization

https://github.com/blue-yonder/tsfresh

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Feature engineering workflow for activity recognition from synchronized inertial measurement units

https://github.com/bnachman/DCTRHunting

공식 코드

Simulation-Assisted Decorrelation for Resonant Anomaly Detection

https://github.com/bnl/pub-ml_examples

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Machine learning enabling high-throughput and remote operations at large-scale user facilities

https://github.com/bnubald/dce-schlieren-density-reconstruction

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Density reconstruction from schlieren images through Bayesian nonparametric models

https://github.com/bnubald/pof-schlieren-density-reconstruction

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Density reconstruction from schlieren images through Bayesian nonparametric models

https://github.com/bnuliujing/tn-for-unsup-ml

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Tensor networks for unsupervised machine learning

https://github.com/boettiger-lab/rl-intro

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Deep Reinforcement Learning for Conservation Decisions

https://github.com/borgwardtlab/motifiesta

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Approximate Network Motif Mining Via Graph Learning

https://github.com/bostdiek/PublicWeaklySupervised

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(Machine) Learning to Do More with Less

https://github.com/box-key/Subjective-Class-Issue

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A Pitfall of Learning from User-generated Data: In-depth Analysis of Subjective Class Problem

https://github.com/brandeis-machine-learning/influence-fairness

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Achieving Fairness at No Utility Cost via Data Reweighing with Influence

https://github.com/brechtlaperre/DTW_measure

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Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning

https://github.com/brettbj/poincareembeddings

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Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes

https://github.com/brianbullins/lissa_code

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Second-Order Stochastic Optimization for Machine Learning in Linear Time

https://github.com/bruel-gabrielsson/TopologyLayer

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A Topology Layer for Machine Learning

https://github.com/brunaw/genre_classification

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Machine learning and chord based feature engineering for genre prediction in popular Brazilian music

https://github.com/buds-lab/aldiplusplus

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ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles

https://github.com/buds-lab/ashrae-great-energy-predictor-3-error-analysis

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Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

https://github.com/bulbulahmmed/ML-to-reactive-mixing-data

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A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing

https://github.com/bziiuj/pcodebooks

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Persistence Bag-of-Words for Topological Data Analysis

https://github.com/c-hofer/nips2017

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Deep Learning with Topological Signatures

https://github.com/c-hofer/persistent_homology_toolbox

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Deep Learning with Topological Signatures

https://github.com/c2dc/ab-trap

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Bridging the gap to real-world for network intrusion detection systems with data-centric approach

https://github.com/cadurosar/benchmark_graphinference

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Graph topology inference benchmarks for machine learning

https://github.com/cae67/G2PC_L2PC

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Algorithm-Agnostic Explainability for Unsupervised Clustering

https://github.com/cam-mobsys/covid19-sounds-ios-app

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Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

https://github.com/camillegontier/delaunay_dataset

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DELAUNAY: a dataset of abstract art for psychophysical and machine learning research

https://github.com/cannylab/casual_overhypotheses

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Towards Understanding How Machines Can Learn Causal Overhypotheses

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