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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 151–200 of 813 papers

TitleStatusHype
Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach—0
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates—0
Rethinking Losses for Diffusion Bridge Samplers—0
Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum—0
Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated ImageryCode0
Temporal horizons in forecasting: a performance-learnability trade-off—0
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning—0
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL—0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational SpeechCode0
Minimizing False-Positive Attributions in Explanations of Non-Linear ModelsCode0
POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence—0
Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning—0
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints—0
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification—0
Dynamic Domain Information Modulation Algorithm for Multi-domain Sentiment Analysis—0
Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints—0
A critical assessment of reinforcement learning methods for microswimmer navigation in complex flowsCode0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning—0
Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data—0
Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models—0
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization—0
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models—0
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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