SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Showing 125 of 10033 papers

TitleStatusHype
TensorIR: An Abstraction for Automatic Tensorized Program OptimizationCode6
Synthetic Dataset Generation for Adversarial Machine Learning ResearchCode6
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and ValuesCode5
Flashlight: Enabling Innovation in Tools for Machine LearningCode5
XGBoost: A Scalable Tree Boosting SystemCode4
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree EnsemblesCode4
Pen and Paper Exercises in Machine LearningCode4
Deepchecks: A Library for Testing and Validating Machine Learning Models and DataCode4
Sintel: A Machine Learning Framework to Extract Insights from SignalsCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded ModesCode3
Designing and building the mlpack open-source machine learning libraryCode3
pyannote.audio: neural building blocks for speaker diarizationCode3
River: machine learning for streaming data in PythonCode3
SQLFlow: A Bridge between SQL and Machine LearningCode3
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligenceCode3
CausalML: Python Package for Causal Machine LearningCode3
mlpack 3: a fast, flexible machine learning libraryCode3
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment EffectCode3
Benchmarking Automatic Machine Learning FrameworksCode3
InterpretML: A Unified Framework for Machine Learning InterpretabilityCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
Efficient and Robust Automated Machine LearningCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Adversarial Robustness Toolbox v1.0.0Code3
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Benchmark Results

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