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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
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN—0
Adapters Strike BackCode1
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO—0
Distributional bias compromises leave-one-out cross-validationCode0
Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy—0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
Derivatives of Stochastic Gradient Descent in parametric optimization—0
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter OptimizationCode0
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs—0
Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter OptimisationCode0
Restless Bandit Problem with Rewards Generated by a Linear Gaussian Dynamical System—0
Hyperparameter Importance Analysis for Multi-Objective AutoMLCode0
Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs—0
Aequitas Flow: Streamlining Fair ML ExperimentationCode4
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
Transductive Spiking Graph Neural Networks for Loihi—0
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter OptimizationCode1
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It—0
Self-adaptive PSRO: Towards an Automatic Population-based Game Solver—0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization ApproachCode7
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning—0
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization—0
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks—0
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—0
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in TransformersCode0
Simple Hack for Transformers against Heavy Long-Text Classification on a Time- and Memory-Limited GPU Service—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties—0
Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship TablesCode0
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis—0
Adaptive Hyperparameter Optimization for Continual Learning Scenarios—0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
Hyperparameter Tuning MLPs for Probabilistic Time Series ForecastingCode0
A machine learning workflow to address credit default prediction—0
Statistical Mechanics of Dynamical System Identification—0
Transformers for Low-Resource Languages:Is Féidir Linn!—0
Parallel Hyperparameter Optimization Of Spiking Neural NetworkCode0
Exploratory Landscape Analysis for Mixed-Variable Problems—0
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization—0
Universal Link Predictor By In-Context Learning on Graphs—0
Glocal Hypergradient Estimation with Koopman Operator—0
Poisson Process for Bayesian Optimization—0
Breaking MLPerf Training: A Case Study on Optimizing BERT—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble—0
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