Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Overtuning 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 OptimizationQuantum-Classical Hybrid Quantized Neural Network
Here in this work, we present a novel Quadratic Binary Optimization (QBO) model for quantized neural network training, enabling the use of arbitrary activation and loss functions through spline interpolation. We introduc…
Hyperparameter OptimizationBalancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
As DBS technology advances toward directional leads and optimization-based current steering, this study aims to improve the selection of electrode contact configurations using the recently developed L1-norm regularized L…
Hyperparameter OptimizationCBTOPE2: An improved method for predicting of conformational B-cell epitopes in an antigen from its primary sequence
In 2009, our group pioneered a novel method CBTOPE for predicting conformational B-cell epitopes in a protein from its amino acid sequence, which received extensive citations from the scientific community. In a recent st…
Hyperparameter OptimizationDifferentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Bilevel optimization, in which one optimization problem is nested inside another, underlies many machine learning applications with a hierarchical structure -- such as meta-learning and hyperparameter optimization. Such …
Bilevel OptimizationHyperparameter OptimizationMeta-LearningRethinking Losses for Diffusion Bridge Samplers
Diffusion bridges are a promising class of deep-learning methods for sampling from unnormalized distributions. Recent works show that the Log Variance (LV) loss consistently outperforms the reverse Kullback-Leibler (rKL)…
Hyperparameter OptimizationHyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum
A variety of pruning methods have been introduced for over-parameterized Recurrent Neural Networks to improve efficiency in terms of power consumption and storage utilization. These advances motivate a new paradigm, term…
Hyperparameter OptimizationNetwork PruningPeer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation tasks, is undergoing a paradigmatic shift in development methodologies. …
Hyperparameter OptimizationImage GenerationTemporal horizons in forecasting: a performance-learnability trade-off
When training autoregressive models to forecast dynamical systems, a critical question arises: how far into the future should the model be trained to predict? Too short a horizon may miss long-term trends, while too long…
Hyperparameter OptimizationSelecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice
As machine learning models are increasingly embedded into society through high-stakes decision-making, selecting the right algorithm for a given task, audience, and sector presents a critical challenge, particularly in t…
FairnessHyperparameter OptimizationModel SelectionOptimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning
Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) results in complex systems with numerous hyperparameters that directly affect performance. While such systems are increasingly common in retrieval-augm…
Chunkinggraph constructionHyperparameter OptimizationKnowledge Graphs+2BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
Bayesian optimization (BO) offers an efficient pipeline for optimizing black-box functions with the help of a Gaussian process prior and an acquisition function (AF). Recently, in the context of single-objective BO, lear…
Bayesian OptimizationHyperparameter OptimizationLanguage ModelingLanguage Modelling+1PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms
Determining the 3D pose of a patient from a limited set of 2D X-ray images is a critical task in interventional settings. While preoperative volumetric imaging (e.g., CT and MRI) provides precise 3D localization and visu…
AnatomyHyperparameter OptimizationInductive BiasOptiMindTune: 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 SelectionAuto-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+5BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech
Parkinson's disease (PD) poses a growing global health challenge, with Bangladesh experiencing a notable rise in PD-related mortality. Early detection of PD remains particularly challenging in resource-constrained settin…
feature selectionHyperparameter OptimizationMinimizing False-Positive Attributions in Explanations of Non-Linear Models
Suppressor variables can influence model predictions without being dependent on the target outcome and they pose a significant challenge for Explainable AI (XAI) methods. These variables may cause false-positive feature …
Hyperparameter OptimizationPOCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence
In this paper we propose for the first time the hyperparameter optimization (HPO) algorithm POCAII. POCAII differs from the Hyperband and Successive Halving literature by explicitly separating the search and evaluation p…
Hyperparameter OptimizationSMACSMAC+Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning
Specialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, rec…
Hyperparameter OptimizationMulti-Task LearningInterim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints
This case study applies a phased hyperparameter optimization process to compare multitask natural language model variants that utilize multiphase learning rate scheduling and optimizer parameter grouping. We employ short…
Bayesian OptimizationHyperparameter OptimizationJoint Entity and Relation ExtractionLanguage Modeling+3