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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 501–525 of 813 papers

TitleStatusHype
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks—0
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language ModelsCode2
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
A Nonmyopic Approach to Cost-Constrained Bayesian OptimizationCode0
Meta-Learning for Symbolic Hyperparameter DefaultsCode0
Stability and Generalization of Bilevel Programming in Hyperparameter OptimizationCode0
Provably Faster Algorithms for Bilevel OptimizationCode1
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing—0
k-Mixup Regularization for Deep Learning via Optimal TransportCode0
BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter—0
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit modelsCode0
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition—0
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance—0
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter OptimizationCode1
Implicit differentiation for fast hyperparameter selection in non-smooth convex learningCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
One Billion Audio Sounds from GPU-enabled Modular SynthesisCode1
Efficient Hyperparameter Optimization for Physics-based Character Animation—0
Automatic Termination for Hyperparameter OptimizationCode0
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction—0
Promoting Fairness through Hyperparameter OptimizationCode1
Use of static surrogates in hyperparameter optimization—0
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