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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 51–100 of 813 papers

TitleStatusHype
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
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee—0
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning—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
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods—0
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping—0
MetaDE: Evolving Differential Evolution by Differential EvolutionCode3
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
Hyperparameters in Score-Based Membership Inference AttacksCode0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Renewable Energy Prediction: A Comparative Study of Deep Learning Models for Complex Dataset Analysis—0
Which price to pay? Auto-tuning building MPC controller for optimal economic cost—0
Tutorial: VAE as an inference paradigm for neuroimaging—0
Dataset-Agnostic Recommender Systems—0
Evaluation of Artificial Intelligence Methods for Lead Time Prediction in Non-Cycled Areas of Automotive Production—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Benchmarking YOLOv8 for Optimal Crack Detection in Civil Infrastructure—0
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting ModelsCode0
HyperQ-Opt: Q-learning for Hyperparameter Optimization—0
Bilevel Learning with Inexact Stochastic GradientsCode0
Automated Image Captioning with CNNs and TransformersCode0
Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization—0
Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence—0
Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach—0
Machine learning approach for mapping the stable orbits around planets—0
Hyperparameter Tuning Through Pessimistic Bilevel Optimization—0
Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing SystemsCode0
Interpretable label-free self-guided subspace clustering—0
Recursive Gaussian Process State Space ModelCode1
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML—0
Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting—0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Scientific machine learning in ecological systems: A study on the predator-prey dynamics—0
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
Hyperparameter Optimization in Machine Learning—0
Sequential Large Language Model-Based Hyper-parameter OptimizationCode0
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?—0
Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting—0
A comparative study of NeuralODE and Universal ODE approaches to solving Chandrasekhar White Dwarf equation—0
Predicting from Strings: Language Model Embeddings for Bayesian OptimizationCode3
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient—0
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