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BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

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Showing 1376–1400 of 10033 papers

TitleStatusHype
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique—0
Self-Compression in Bayesian Neural Networks—0
Automatically detecting data drift in machine learning classifiers—0
Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities—0
Classifying DNS Servers based on Response Message Matrix using Machine Learning—0
An Interactive Visualization Tool for Understanding Active LearningCode0
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection—0
Generalization in quantum machine learning from few training data—0
Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives—0
Machine-learning custom-made basis functions for partial differential equations—0
Importance of Kernel Bandwidth in Quantum Machine LearningCode0
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine LearningCode1
Defense Against Explanation Manipulation—0
Get a Model! Model Hijacking Attack Against Machine Learning Models—0
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine LearningCode1
Spirometry-based airways disease simulation and recognition using Machine Learning approaches—0
Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination—0
Representation Learning via Quantum Neural Tangent Kernels—0
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities—0
Machine Learning-Assisted E-jet Printing of Organic Flexible Biosensors—0
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data PipelinesCode1
DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified