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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 176–200 of 813 papers

TitleStatusHype
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Knowledge-augmented Pre-trained Language Models for Biomedical Relation ExtractionCode0
A General Approach of Automated Environment Design for Learning the Optimal Power Flow—0
HyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural NetworksCode0
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
Causal-Copilot: An Autonomous Causal Analysis Agent—0
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization—0
A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization—0
Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning—0
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings—0
The Role of Hyperparameters in Predictive Multiplicity—0
Discriminative versus Generative Approaches to Simulation-based Inference—0
Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning modelsCode0
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning—0
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee—0
MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System—0
Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining—0
AutoQML: A Framework for Automated Quantum Machine LearningCode0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods—0
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