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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/schemmy/CoCoA-Experiments

공식 코드

An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning

https://github.com/schnarc/schnarc

공식 코드

Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

https://github.com/scikit-learn-contrib/imbalanced-learn

공식 코드

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

https://github.com/scikit-learn-contrib/metric-learn

공식 코드

metric-learn: Metric Learning Algorithms in Python

https://github.com/sciml/differentialequations.jl

공식 코드

Universal Differential Equations for Scientific Machine Learning

https://github.com/scotthlee/autism_classification

공식 코드

A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder

https://github.com/scotthlee/fairness

공식 코드

Blackbox Post-Processing for Multiclass Fairness

https://github.com/seijin-kobayashi/least-control

공식 코드

The least-control principle for local learning at equilibrium

https://github.com/sensing-reasoning/sensing-reasoning-pipeline

공식 코드

Improving Certified Robustness via Statistical Learning with Logical Reasoning

https://github.com/seonho-park/PGN_anomaly_detection

공식 코드

Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection

https://github.com/seraphinatarrant/systematic_reviews

공식 코드

Scaling Systematic Literature Reviews with Machine Learning Pipelines

https://github.com/sergeyprokudin/bps

공식 코드

Efficient Learning on Point Clouds with Basis Point Sets

https://github.com/sergioburdisso/pyss3

공식 코드

PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI

https://github.com/sgoldt/gaussian-equiv-2layer

공식 코드

The Gaussian equivalence of generative models for learning with shallow neural networks

https://github.com/shahbozjon/porosity-and-permeability-prediction

공식 코드

AI for Porosity and Permeability Prediction from Geologic Core X-Ray Micro-Tomography

https://github.com/shansiliu95/ckme

공식 코드

Transparent Single-Cell Set Classification with Kernel Mean Embeddings

https://github.com/shaoncsecu/BanglaEmotion

공식 코드

Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion Analysis

https://github.com/sharmapulkit/TreeInterpretability_AnomalyExplanation

공식 코드

Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter

https://github.com/shendu-sw/recon-data-generator

공식 코드

A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems

https://github.com/shendu-sw/tfr-hss-benchmark

공식 코드

A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems

https://github.com/sherbold/replication-kit-2020-smoke-testing

공식 코드

Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects

https://github.com/sherpa-ai/sherpa

공식 코드

Sherpa: Robust Hyperparameter Optimization for Machine Learning

https://github.com/sheryl-ai/NeurIPS17

공식 코드

A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations

https://github.com/shi27feng/transformers.satisfy

공식 코드

Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis

https://github.com/shikhartuli/cnn_txf_bias

공식 코드

Are Convolutional Neural Networks or Transformers more like human vision?

https://github.com/shlomihod/performative-prediction-stateful-world

공식 코드

Performative Prediction in a Stateful World

https://github.com/shouryaverma/interpretable_ml_ecg

공식 코드

Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data

https://github.com/shps/incremental-representation-learning

공식 코드

Efficient Representation Learning Using Random Walks for Dynamic Graphs

https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning

공식 코드

Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions

https://github.com/shuzhaoxie/armol

공식 코드

Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

https://github.com/siddrrsh/QEML

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QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces

https://github.com/simdis/Adaptive-TML

공식 코드

Tiny Machine Learning for Concept Drift

https://github.com/sing-group/deep-learning-colonoscopy

공식 코드

ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specification

https://github.com/singh-l/hybrrid_fn_dat_

공식 코드

Hybrid Ensemble for Fake News Detection: An attempt

https://github.com/sintel-dev/orion

공식 코드

Sintel: A Machine Learning Framework to Extract Insights from Signals

https://github.com/sirmarcel/cmlkit

공식 코드

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.