Papers AutoML
“AutoML” 태그가 달린 논문 641편 · 필터 해제
Imbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionOptimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage
Extended Stability Runge-Kutta (ESRK) methods are crucial for solving large-scale computational problems in science and engineering, including weather forecasting, aerodynamic analysis, and complex biological modelling. …
AutoMLComputational EfficiencyHeuristic SearchReinforcement Learning (RL)+1Multimodal Representation Learning and Fusion
Multi-modal learning is a fast growing area in artificial intelligence. It tries to help machines understand complex things by combining information from different sources, like images, text, and audio. By using the stre…
AutoMLRepresentation Learningspeech-recognitionSpeech RecognitionOvertuning in Hyperparameter Optimization
Hyperparameter optimization (HPO) aims to identify an optimal hyperparameter configuration (HPC) such that the resulting model generalizes well to unseen data. As the expected generalization error cannot be optimized dir…
AutoMLHyperparameter OptimizationFrom Tiny Machine Learning to Tiny Deep Learning: A Survey
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). Whil…
AutoMLModel OptimizationNeural Architecture SearchQuantization+1CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Caco-2 permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates during early-stage drug discovery. To enhance the accuracy and efficiency of computational predictions, we…
AutoMLDiversityDrug DiscoveryFeature Importance+1Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation
Foundation model fine-tuning faces a fundamental challenge: existing AutoML platforms rely on single optimisation strategies that explore only a fraction of viable hyperparameter configurations. In this white paper, We i…
AutoMLVirnyFlow: A Design Space for Responsible Model Development
Developing machine learning (ML) models requires a deep understanding of real-world problems, which are inherently multi-objective. In this paper, we present VirnyFlow, the first design space for responsible model develo…
AutoMLBayesian OptimizationMulti-Armed BanditsOptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
Hyperparameter optimization (HPO) is a critical yet challenging aspect of machine learning model development, significantly impacting model performance and generalization. Traditional HPO methods often struggle with high…
AutoMLDecision MakingHyperparameter OptimizationModel SelectionZeroML: A Next Generation AutoML Language
ZeroML is a new generation programming language for AutoML to drive the ML pipeline in a compiled and multi-paradigm way, with a pure functional core. Meeting the shortcomings introduced by Python, R, or Julia such as sl…
AutoMLAuto-nnU-Net: Towards Automated Medical Image Segmentation
Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net aut…
AutoMLComputational EfficiencyHyperparameter OptimizationImage Segmentation+5MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
Existing AutoML systems have advanced the automation of machine learning (ML); however, they still require substantial manual configuration and expert input, particularly when handling multimodal data. We introduce MLZer…
AutoMLCode GenerationSEAL: Searching Expandable Architectures for Incremental Learning
Incremental learning is a machine learning paradigm where a model learns from a sequential stream of tasks. This setting poses a key challenge: balancing plasticity (learning new tasks) and stability (preserving past kno…
AutoMLCapacity EstimationIncremental LearningNeural Architecture SearchPut 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 OptimizationWhen Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger
We present Noise-to-Meaning Recursive Self-Improvement (N2M-RSI), a minimal formal model showing that once an AI agent feeds its own outputs back as inputs and crosses an explicit information-integration threshold, its i…
AI AgentAutoMLA-DARTS: Stable Model Selection for Data Repair in Time Series
Time series often present gaps in the data. This phenomenon, also called missing values, is so prevalent that a cottage industry of missing-value imputation algorithms exists, each with different capabilities and efficac…
AutoMLImputationMissing ValuesModel Selection+1United States Road Accident Prediction using Random Forest Predictor
Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive …
AutoMLPredictionTime Series AnalysisCAPO: Cost-Aware Prompt Optimization
Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive to prompt formulation. While automated pr…
Arithmetic ReasoningAutoMLSentiment AnalysisSubjectivity Analysis+1Learning to Be A Doctor: Searching for Effective Medical Agent Architectures
Large Language Model (LLM)-based agents have demonstrated strong capabilities across a wide range of tasks, and their application in the medical domain holds particular promise due to the demand for high generalizability…
AutoMLDiagnosticLarge Language ModelLEMUR 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+3