BIG-bench Machine Learning
2개 벤치마크 · 논문 10,033편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
YOLOv4: Optimal Speed and Accuracy of Object Detection
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Density estimation using Real NVP
XGBoost: A Scalable Tree Boosting System
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Papers
(1,1)-Cluster Editing is Polynomial-time Solvable
A graph $H$ is a clique graph if $H$ is a vertex-disjoin union of cliques. Abu-Khzam (2017) introduced the $(a,d)$-{Cluster Editing} problem, where for fixed natural numbers $a,d$, given a graph $G$ and vertex-weights $a…
BIG-bench Machine LearningAnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framewor…
BIG-bench Machine LearningGPUHyperparameter OptimizationManagementDiscover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principle…
BIG-bench Machine LearningImage SegmentationSemantic SegmentationTailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Expected risk minimization (ERM) is at the core of many machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its average. In this paper, we propose a ge…
BIG-bench Machine LearningSensitivityExplanation of Machine Learning Models of Colon Cancer Using SHAP Considering Interaction Effects
When using machine learning techniques in decision-making processes, the interpretability of the models is important. Shapley additive explanation (SHAP) is one of the most promising interpretation methods for machine le…
BIG-bench Machine LearningDecision MakingMachine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders
Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy…
BIG-bench Machine Learning