SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Showing 125 of 10033 papers

TitleStatusHype
Synthetic Dataset Generation for Adversarial Machine Learning ResearchCode6
TensorIR: An Abstraction for Automatic Tensorized Program OptimizationCode6
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and ValuesCode5
Flashlight: Enabling Innovation in Tools for Machine LearningCode5
Pen and Paper Exercises in Machine LearningCode4
Deepchecks: A Library for Testing and Validating Machine Learning Models and DataCode4
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree EnsemblesCode4
XGBoost: A Scalable Tree Boosting SystemCode4
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
Sintel: A Machine Learning Framework to Extract Insights from SignalsCode3
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug DiscoveryCode3
Declarative Machine Learning SystemsCode3
River: machine learning for streaming data in PythonCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment EffectCode3
YOLOv4: Optimal Speed and Accuracy of Object DetectionCode3
CausalML: Python Package for Causal Machine LearningCode3
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligenceCode3
SQLFlow: A Bridge between SQL and Machine LearningCode3
pyannote.audio: neural building blocks for speaker diarizationCode3
InterpretML: A Unified Framework for Machine Learning InterpretabilityCode3
MMLSpark: Unifying Machine Learning Ecosystems at Massive ScalesCode3
Benchmarking Automatic Machine Learning FrameworksCode3
Adversarial Robustness Toolbox v1.0.0Code3
mlpack 3: a fast, flexible machine learning libraryCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100Unverified