Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
论文指出标注不完整限制了目标检测基准质量，通过重新标注增加对象数量，并提出高召回率和不确定性感知的标注流程以提高数据质量。
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Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries
研究通过微调分子语言模型以适应特定虚拟库，提高其在药物发现、有机材料和催化等领域的分子发现效率。
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Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing
This work proposes an efficient method for solving linear elliptic partial differential equations with constant diffusion, drift, and killing terms by integrating an enhanced Walk-on-Spheres (WoS) Monte Carlo algorithm with deep neural networks. The approach constructs unbiased estimators through explicit stochastic time sampling and employs a tailored neural network architecture to approximate both the stochastic representation of the solution and the boundary data. It represents the first integration of stochastic representations for elliptic PDEs with drift and killing terms into a deep learning framework. The study establishes uniform error bounds for the Monte Carlo estimator and proves that the solution approximation achieves polynomial complexity in both accuracy and dimensionality, thereby significantly extending the theoretical foundations and practical applicability of numerical methods for high-dimensional PDEs.
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Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points
This work addresses the challenge of efficiently generating high-quality fixed-size representations from the typically vast set of nondominated solutions in multi-objective network optimization. The authors propose using supported nondominated points—particularly extreme points—as a compact candidate set to replace the full nondominated set for subset selection. For the first time, they systematically demonstrate that supported nondominated points in capacitated network problems offer both high representational quality and computational efficiency. Experimental results show that fixed-size solution sets selected solely from this reduced candidate set achieve solution quality nearly equivalent to those selected from the complete nondominated set, while substantially reducing computational overhead.
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Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
论文指出标注不完整限制了目标检测基准质量，通过重新标注增加对象数量，并提出高召回率和不确定性感知的标注流程以提高数据质量。
0 citationsRead paper
Efficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring
本文提出COMPASS框架，通过在线持续微调基础模型解决预测过程监控中的概念漂移问题，优于现有方法。
0 citationsRead paper
Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries
研究通过微调分子语言模型以适应特定虚拟库，提高其在药物发现、有机材料和催化等领域的分子发现效率。
0 citationsRead paper
Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing
This work proposes an efficient method for solving linear elliptic partial differential equations with constant diffusion, drift, and killing terms by integrating an enhanced Walk-on-Spheres (WoS) Monte Carlo algorithm with deep neural networks. The approach constructs unbiased estimators through explicit stochastic time sampling and employs a tailored neural network architecture to approximate both the stochastic representation of the solution and the boundary data. It represents the first integration of stochastic representations for elliptic PDEs with drift and killing terms into a deep learning framework. The study establishes uniform error bounds for the Monte Carlo estimator and proves that the solution approximation achieves polynomial complexity in both accuracy and dimensionality, thereby significantly extending the theoretical foundations and practical applicability of numerical methods for high-dimensional PDEs.
0 citationsRead paper
Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points
This work addresses the challenge of efficiently generating high-quality fixed-size representations from the typically vast set of nondominated solutions in multi-objective network optimization. The authors propose using supported nondominated points—particularly extreme points—as a compact candidate set to replace the full nondominated set for subset selection. For the first time, they systematically demonstrate that supported nondominated points in capacitated network problems offer both high representational quality and computational efficiency. Experimental results show that fixed-size solution sets selected solely from this reduced candidate set achieve solution quality nearly equivalent to those selected from the complete nondominated set, while substantially reducing computational overhead.
0 citationsRead paper