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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 5801–5825 of 10033 papers

TitleStatusHype
Machine Learning based Post Processing Artifact Reduction in HEVC Intra Coding—0
Variance Reduced Local SGD with Lower Communication ComplexityCode0
Machine Learning from a Continuous Viewpoint—0
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and BeyondCode0
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel DataCode0
Differentially Private M-band Wavelet-Based Mechanisms in Machine Learning EnvironmentsCode0
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systemsCode0
Using massive health insurance claims data to predict very high-cost claimants: a machine learning approach—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Deep learning surrogate models for spatial and visual connectivity—0
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability—0
A Machine Learning Solution for Beam Tracking in mmWave Systems—0
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creepCode0
Classifying topological sector via machine learning—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Efficient Data Analytics on Augmented Similarity Triplets—0
Fast Generalized Matrix Regression with Applications in Machine Learning—0
Apricot variety classification using image processing and machine learning approaches—0
A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot—0
A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning—0
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review—0
Text Classification for Azerbaijani Language Using Machine Learning and Embedding—0
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)—0
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records—0
On Sharing Models Instead of Data using Mimic learning for Smart Health Applications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified