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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 451–500 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
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
Evaluating Transferability of BERT Models on Uralic LanguagesCode0
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package—0
YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter OptimizationCode1
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
To tune or not to tune? An Approach for Recommending Important Hyperparameters—0
CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning ModelsCode0
MOFit: A Framework to reduce Obesity using Machine learning and IoT—0
An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applicationsCode1
Is Differentiable Architecture Search truly a One-Shot Method?—0
Efficient Hyperparameter Optimization for Differentially Private Deep LearningCode1
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Transformers for Low-Resource Languages: Is Féidir Linn!—0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis—0
Enhanced Bilevel Optimization via Bregman Distance—0
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization—0
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies—0
Multi-objective Asynchronous Successive HalvingCode3
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