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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 201–250 of 813 papers

TitleStatusHype
CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization—0
Crafting Efficient Fine-Tuning Strategies for Large Language Models—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks—0
Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting—0
Adaptive Hyperparameter Optimization for Continual Learning Scenarios—0
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization—0
Convolution Neural Network Hyperparameter Optimization Using Simplified Swarm Optimization—0
Convergence Properties of Stochastic Hypergradients—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning—0
Dataset-Agnostic Recommender Systems—0
DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models—0
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity—0
Automated Disease Diagnosis in Pumpkin Plants Using Advanced CNN Models—0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone—0
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting—0
Deep Genetic Network—0
Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization—0
Adaptive Optimizer for Automated Hyperparameter Optimization Problem—0
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics—0
Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Deep Ranking Ensembles for Hyperparameter Optimization—0
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum—0
Constrained Bayesian Optimization with Max-Value Entropy Search—0
Conditional Neural Fields—0
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic—0
Derivatives of Stochastic Gradient Descent in parametric optimization—0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization—0
Exploratory Landscape Analysis for Mixed-Variable Problems—0
Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs—0
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization—0
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework—0
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization—0
Gaussian Process on the Product of Directional Manifolds—0
Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization—0
Concepts for Automated Machine Learning in Smart Grid Applications—0
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations—0
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
ECG-Based Driver Stress Levels Detection System Using Hyperparameter Optimization—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
AMLA: an AutoML frAmework for Neural Network Design—0
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