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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 201–250 of 813 papers

TitleStatusHype
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
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence AnalysisCode0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Hypercomplex neural network in time series forecasting of stock data—0
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
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
Using Large Language Models for Hyperparameter OptimizationCode1
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
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML ApplicationsCode6
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
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting—0
Hyperparameter Optimization for Multi-Objective Reinforcement LearningCode0
Scrap Your Schedules with PopDescent—0
Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates—0
A Hyperparameter Study for Quantum Kernel Methods—0
Fairer and More Accurate Tabular Models Through NAS—0
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector MachinesCode0
Target Variable Engineering—0
Improving Fast Minimum-Norm Attacks with Hyperparameter OptimizationCode1
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent—0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym—0
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives—0
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions—0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone—0
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference LearningCode0
AutoML-GPT: Large Language Model for AutoML—0
PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connectionsCode0
Where Did the Gap Go? Reassessing the Long-Range Graph BenchmarkCode1
ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm—0
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