Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification
The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of medical terminology. This research prese…
ClassificationHyperparameter OptimizationKnowledge DistillationModel Compression+4Dynamic Domain Information Modulation Algorithm for Multi-domain Sentiment Analysis
Multi-domain sentiment classification aims to mitigate poor performance models due to the scarcity of labeled data in a single domain, by utilizing data labeled from various domains. A series of models that jointly train…
Classificationdomain classificationHyperparameter OptimizationMulti-Domain Sentiment Classification+2Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is a challenging resource allocation problem in the field of AutoML. We propose MaxUCB, a max $k$-armed bandit method to trade off exploring differe…
AutoMLHyperparameter OptimizationGenerating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints
The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring that synthetic datasets maintain high f…
Hyperparameter OptimizationSynthetic Data GenerationA critical assessment of reinforcement learning methods for microswimmer navigation in complex flows
Navigating in a fluid flow while being carried by it, using only information accessible from on-board sensors, is a problem commonly faced by small planktonic organisms. It is also directly relevant to autonomous robots …
Autonomous NavigationHyperparameter OptimizationQ-Learningreinforcement-learning+1Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data
This paper presents a multitask learning approach based on long-short-term memory (LSTM) networks for the joint prediction of arboviral outbreaks and case counts of dengue, chikungunya, and Zika in Recife, Brazil. Levera…
Binary ClassificationHyperparameter OptimizationregressionDeep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models
Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy d…
Hyperparameter OptimizationL2 RegularizationEfficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization
Bilevel optimization is a powerful tool for many machine learning problems, such as hyperparameter optimization and meta-learning. Estimating hypergradients (also known as implicit gradients) is crucial for developing gr…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningBOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Advances in deep learning and generative modeling have driven interest in data-driven molecule discovery pipelines, whereby machine learning (ML) models are used to filter and design novel molecules without requiring pro…
BenchmarkingHyperparameter OptimizationIn-Context LearningInductive Bias+3From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup
Accurate prediction of FIFA World Cup match outcomes holds significant value for analysts, coaches, bettors, and fans. This paper presents a machine learning framework specifically designed to forecast match winners in F…
Dimensionality ReductionHyperparameter OptimizationSports AnalyticsKnowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction
Automatic relationship extraction (RE) from biomedical literature is critical for managing the vast amount of scientific knowledge produced each year. In recent years, utilizing pre-trained language models (PLMs) has bec…
Hyperparameter OptimizationKnowledge GraphsRelationRelation ExtractionA General Approach of Automated Environment Design for Learning the Optimal Power Flow
Reinforcement learning (RL) algorithms are increasingly used to solve the optimal power flow (OPF) problem. Yet, the question of how to design RL environments to maximize training performance remains unanswered, both for…
Hyperparameter OptimizationReinforcement Learning (RL)Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
An adaptive physics-informed model design strategy for machine-learning interatomic potentials (MLIPs) is proposed. This strategy follows an iterative reconfiguration of composite models from single-term models, followed…
Hyperparameter OptimizationHyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural Networks
We introduce Hyperparameter Controller (HyperController), a computationally efficient algorithm for hyperparameter optimization during training of reinforcement learning neural networks. HyperController optimizes hyperpa…
Hyperparameter Optimizationreinforcement-learningReinforcement LearningData-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems
The effective contact area in rough surface contact plays a critical role in multi-physics phenomena such as wear, sealing, and thermal or electrical conduction. Although accurate numerical methods, like the Boundary Ele…
Computational EfficiencyHyperparameter OptimizationUncertainty QuantificationCausal-Copilot: An Autonomous Causal Analysis Agent
Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algorithmic complexity. This disconnect betw…
Causal DiscoveryCausal InferenceHyperparameter OptimizationLarge Language Model+1Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics …
DenoisingHyperparameter OptimizationLEMUR Neural Network Dataset: Towards Seamless AutoML
Neural networks are fundamental in artificial intelligence, driving progress in computer vision and natural language processing. High-quality datasets are crucial for their development, and there is growing interest in d…
AutoMLBenchmarkingHyperparameter Optimizationimage-classification+3Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
As model sizes grow, finding efficient and cost-effective hyperparameter optimization (HPO) methods becomes increasingly crucial for deep learning pipelines. While multi-fidelity HPO (MF-HPO) trades off computational res…
GPUHyperparameter OptimizationA Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization
This paper proposes a new method for hyperparameter optimization (HPO) that balances exploration and exploitation. While evolutionary algorithms (EAs) show promise in HPO, they often struggle with effective exploitation.…
Evolutionary AlgorithmsHyperparameter Optimization