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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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31#
發(fā)表于 2025-3-26 21:13:04 | 只看該作者
32#
發(fā)表于 2025-3-27 01:33:29 | 只看該作者
A Measurement-Based Quantum-Like Language Model for?Text Matching mostly generated by randomly initialized parameter matrices, which cannot well explain the role of measurement operators in quantum theory. In this paper, we propose a Measurement-Based Quantum-like Language Model (MBQLM). Specifically, each word is considered a fundamental event in quantum probabi
33#
發(fā)表于 2025-3-27 09:10:44 | 只看該作者
34#
發(fā)表于 2025-3-27 12:57:14 | 只看該作者
WINMLP: Quantum & Involution Inspire False Positive Reduction in?Lung Nodule Detectionave made significant contributions to improving the accuracy. However, it remains a challenge to reduce the False Positive rate while maintaining high sensitivity. In this paper, we propose a novel MLP-based False Positive Reduction network, Wave-Involution MLP. We design a progressive multi-scale f
35#
發(fā)表于 2025-3-27 14:38:40 | 只看該作者
Incorporating Generation Method and?Discourse Structure to?Event Coreference Resolutionthe structure between paragraphs which is also important to event coreference resolution. Moreover, almost all previous work modeled event coreference resolution as a classification task. In this paper, we introduce macro discourse structure to help event coreference resolution through a Relational
36#
發(fā)表于 2025-3-27 18:00:46 | 只看該作者
37#
發(fā)表于 2025-3-27 22:11:03 | 只看該作者
Spatial and?Temporal Guidance for?Semi-supervised Video Object Segmentationmemory-based methods have attracted increasing attention with significant performance improvements. However, these methods employ pixel-level matching according to the similarity without considering the trajectory and the feature of the object, which may result in mismatching between the object and
38#
發(fā)表于 2025-3-28 02:10:17 | 只看該作者
39#
發(fā)表于 2025-3-28 07:19:07 | 只看該作者
40#
發(fā)表于 2025-3-28 10:49:33 | 只看該作者
Unsupervised Anomaly Segmentation for?Brain Lesions Using Dual Semantic-Manifold Reconstructiontecting the anomalies (lesions) by only using the normal samples (healthy anatomies) in the training phase. Existing methods utilize the reconstruction process to model the normative distribution but inevitably lead to the impairment of localization information, which is critical for the pixel-level
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