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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 26–50 of 813 papers

TitleStatusHype
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random FeaturesCode2
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian OptimizationCode2
Sequential Model-Based Optimization for General Algorithm ConfigurationCode2
Fast Optimizer BenchmarkCode1
FedNest: Federated Bilevel, Minimax, and Compositional OptimizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Evolutionary Neural AutoML for Deep LearningCode1
Flexible Differentiable Optimization via Model TransformationsCode1
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems EvaluationCode1
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Efficient Hyperparameter Optimization for Differentially Private Deep LearningCode1
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRICode1
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Deep Pipeline Embeddings for AutoMLCode1
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing SystemsCode1
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
Adapters Strike BackCode1
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response JacobiansCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
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