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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 3926–3950 of 10033 papers

TitleStatusHype
Why model why? Assessing the strengths and limitations of LIMECode0
Double machine learning for sample selection models—0
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning—0
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review—0
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series—0
Applied Machine Learning for Games: A Graduate School Course—0
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk—0
Twitter Spam Detection: A Systematic Review—0
Dank or Not? -- Analyzing and Predicting the Popularity of Memes on RedditCode0
Short-Term Flow-Based Bandwidth Forecasting using Machine Learning—0
Exoplanet Detection using Machine LearningCode0
A Backward SDE Method for Uncertainty Quantification in Deep Learning—0
Convening during COVID-19: Lessons learnt from organizing virtual workshops in 2020—0
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions—0
Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems—0
Machine Learning to study the impact of gender-based violence in the news media—0
CASTELO: Clustered Atom Subtypes aidEd Lead Optimization -- a combined machine learning and molecular modeling method—0
Fast IR Drop Estimation with Machine Learning—0
Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods—0
Hawkes Processes Modeling, Inference and Control: An Overview—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Towards Interpretable Multilingual Detection of Hate Speech against Immigrants and Women in Twitter at SemEval-2019 Task 5—0
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning—0
Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified