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Hyperparameter Optimization

Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

Papers

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Showing 451–475 of 813 papers

TitleStatusHype
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
A Simple and Fast Baseline for Tuning Large XGBoost Models—0
Searching in the Forest for Local Bayesian Optimization—0
Importance of Kernel Bandwidth in Quantum Machine LearningCode0
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection—0
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
Personalized Benchmarking with the Ludwig Benchmarking ToolkitCode3
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for LassoCode1
Meta-Learning to Improve Pre-Training—0
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCupCode1
Concepts for Automated Machine Learning in Smart Grid Applications—0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System—0
Improving Hyperparameter Optimization by Planning Ahead—0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
Combining Differential Privacy and Byzantine Resilience in Distributed SGD—0
Online Hyperparameter Meta-Learning with Hypergradient Distillation—0
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization—0
Genealogical Population-Based Training for Hyperparameter OptimizationCode0
Coherence-Based Document Clustering—0
A Theoretical and Empirical Model of the Generalization Error under Time-Varying Learning Rate—0
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization—0
Demystifying Hyperparameter Optimization in Federated Learning—0
Transfer Learning for Bayesian HPO with End-to-End Meta-Features—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime—0
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