Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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Improving Out-of-Distribution Detection with?Margin-Based Prototype Learningntly improved OOD detection performance by optimizing the representation space. However, practical scenarios present a challenge where OOD samples near class boundaries may overlap with in-distribution samples in the feature space, resulting in misclassification, and few methods have considered the

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Text-to-Image Synthesis with?Threshold-Equipped Matching-Aware GANtering inaccurate negative samples, the discriminator can more accurately determine whether the generator has generated the images correctly according to the descriptions. In addition, to enhance the discriminative model’s ability to discriminate and capture key semantic information, a word fine-gra

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Dual-Branch Contrastive Learning for?Network Representation Learning network representation learning. However, existing GCL-based network representation methods mostly use a single-branch contrastive approach, which makes it difficult to learn deeper semantic relationships and is easily affected by noisy connections during the process of obtaining global structural

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Multi-granularity Contrastive Siamese Networks for?Abstractive Text Summarizationformative summaries. Sequence-to-Sequence (Seq2 Seq) models have achieved good results in abstractive text summarization in recent years. However, such models are often sensitive to noise information in the training data and exhibit fragility in practical applications. To enhance the denoising abili

四目在模仿

Joint Entity and?Relation Extraction for?Legal Documents Based on?Table Fillingstructured triplets from rich unstructured legal texts. However, the existing methods for joint entity relation extraction in legal judgment documents often lack domain-specific knowledge, and are difficult to effectively solve the problem of entity overlap in legal texts. To address these issues, w

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