Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused on extracting these facets from reviews, there is less work underst…
Hyperparameter OptimizationSentiment AnalysisCausal InferenceCRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search
Deploying Large Language Models (LLMs) in practice incurs substantial memory and computational costs. Post-training pruning (PTP) is an effective approach to reducing these costs by removing weights without additional tr…
Hyperparameter OptimizationS$^3$LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
Networked AI systems increasingly rely on multiple agents that collaboratively learn and adapt models over communication networks. In such systems, bilevel formulations naturally arise in hyperparameter optimization, dat…
Hyperparameter OptimizationComputational EfficiencyBilevel OptimizationRAISE: RAG Design as an Architecture Search Problem
Retrieval-augmented generation (RAG) systems expose numerous design choices spanning query rewriting, chunking, retrieval depth, reranking, and context compression. In practice, these choices are often configured through…
Hyperparameter OptimizationUnification and Optimization of Robust Supervised Learning
The literature has proposed various robust alternatives to empirical risk minimisation to address failure modes such as distribution shift, label noise and finite-sample degeneracies. Examples include distributionally ro…
Hyperparameter OptimizationElectricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansio…
Hyperparameter OptimizationFeature ImportanceEnsemble LearningScalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Fast and reliable optimal power flow (OPF) approximation is essential for reliable smart-grid operation, yet many learning-based surrogates either flatten the native heterogeneous structure of power networks, target a li…
Hyperparameter OptimizationGraph Neural NetworkDiffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection
Point supervision has become a scalable solution to address dense annotation for infrared small target detection, but its performance is limited by two coupled bottlenecks: unstable pseudo-label evolution in cluttered, l…
Hyperparameter OptimizationA novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent years, Unmanned Aerial Vehicles (UAVs) …
Hyperparameter OptimizationNovel Object DetectionAgentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitative objective. Here, we use composition-ba…
Hyperparameter OptimizationFeature EngineeringAQMP: Image compression through Adaptive Quadtree Refinement and Matching Pursuit with Hyperparameter Optimization
We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that operate on fixed-size sub-images, AQMP dynamically adapts block sizes t…
Hyperparameter OptimizationImage CompressionLLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems
Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperparameter optimization (HPO) for the AutoML community. However, this type …
Hyperparameter OptimizationRobust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification
Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradation, and raw material variability that can cause model performance de…
Hyperparameter OptimizationComputational EfficiencyDon't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
We introduce CUTS-GPR, a new method for performing numerically exact Gaussian process regression (GPR) in high-dimensional settings. The key component of CUTS-GPR is an extremely fast kernel matrix-vector product, which …
Hyperparameter OptimizationGaussian ProcessesCDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries is a challenging task due to the complex interactions and expensive ev…
Hyperparameter OptimizationORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
Bayesian optimization is widely used for hyperparameter optimization when model evaluations are expensive; however, noisy acquisition estimates can lead to unstable decisions. We identify acquisition estimation noise as …
Hyperparameter OptimizationAdaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework
Localization in 5G and 6G networks is essential for important use cases such as intelligent transportation, smart factories, and smart cities. Although deep learning has enabled improving localization accuracy, depending…
Hyperparameter OptimizationFew-Shot LearningPhysiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals
An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multimodal physiological driving behavior large-scale dataset comprising …
Hyperparameter OptimizationFrom Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and neural architecture code generation. However, current LLM-based approach…
Hyperparameter OptimizationImage ClassificationCode GenerationTowards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation
The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) data for improving SDE model performance. This challenge at GenDARA in…
Hyperparameter Optimization