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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 576–600 of 813 papers

TitleStatusHype
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation—0
OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient—0
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design—0
PairNets: Novel Fast Shallow Artificial Neural Networks on Partitioned Subspaces—0
Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives—0
ParamILS: An Automatic Algorithm Configuration Framework—0
Trading Off Resource Budgets for Improved Regret Bounds—0
A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks—0
Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space—0
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs—0
A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning—0
An LP-based hyperparameter optimization model for language modeling—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
PHOTONAI -- A Python API for Rapid Machine Learning Model Development—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization—0
POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence—0
Poisson Process for Bayesian Optimization—0
A Neural Network Based on the Johnson S_U Translation System and Related Application to Electromyogram Classification—0
Scrap Your Schedules with PopDescent—0
Practical and sample efficient zero-shot HPO—0
Transductive Spiking Graph Neural Networks for Loihi—0
Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining—0
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