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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 251–300 of 813 papers

TitleStatusHype
Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations—0
A Comparative Study of Hyperparameter Tuning Methods—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
Flexora: Flexible Low Rank Adaptation for Large Language Models—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback—0
LMEMs for post-hoc analysis of HPO BenchmarkingCode0
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary AlgorithmsCode0
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices—0
The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines—0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Be aware of overfitting by hyperparameter optimization!—0
Quantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization—0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning—0
Crafting Efficient Fine-Tuning Strategies for Large Language Models—0
A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction—0
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems—0
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization—0
BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning AlgorithmsCode0
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses—0
Smell and Emotion: Recognising emotions in smell-related artworksCode0
Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data—0
Scalable Nested Optimization for Deep Learning—0
Enhancing supply chain security with automated machine learning—0
Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning—0
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting—0
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN—0
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control—0
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
Distributional bias compromises leave-one-out cross-validationCode0
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO—0
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
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
Transductive Spiking Graph Neural Networks for Loihi—0
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
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning—0
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks—0
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization—0
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—0
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