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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 301–350 of 813 papers

TitleStatusHype
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
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis—0
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship TablesCode0
Adaptive Hyperparameter Optimization for Continual Learning Scenarios—0
Hyperparameter Tuning MLPs for Probabilistic Time Series ForecastingCode0
Rethinking of Encoder-based Warm-start Methods in Hyperparameter OptimizationCode0
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
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization—0
Universal Link Predictor By In-Context Learning on Graphs—0
Poisson Process for Bayesian Optimization—0
Glocal Hypergradient Estimation with Koopman Operator—0
Breaking MLPerf Training: A Case Study on Optimizing BERT—0
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Explainable Bayesian OptimizationCode0
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization—0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence AnalysisCode0
Hypercomplex neural network in time series forecasting of stock data—0
Flying By ML -- CNN Inversion of Affine Transforms—0
Tuning the activation function to optimize the forecast horizon of a reservoir computer—0
Provably Convergent Federated Trilevel Learning—0
Can LLMs Configure Software Tools—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Teaching Specific Scientific Knowledge into Large Language Models through Additional TrainingCode0
Hyperparameter Optimization for Large Language Model Instruction-Tuning—0
Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery—0
Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing—0
A systematic study comparing hyperparameter optimization engines on tabular data—0
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
Xputer: Bridging Data Gaps with NMF, XGBoost, and a Streamlined GUI Experience—0
A Single-Loop Algorithm for Decentralized Bilevel Optimization—0
AutoML for Large Capacity Modeling of Meta's Ranking Systems—0
Impact of HPO on AutoML Forecasting Ensembles—0
Saturn: Efficient Multi-Large-Model Deep Learning—0
Predicting Ground Reaction Force from Inertial Sensors—0
Hodge-Compositional Edge Gaussian ProcessesCode0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Hyperparameter Optimization for Multi-Objective Reinforcement LearningCode0
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