Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control
本文提出GTFD，一种图-变换器欺诈检测方法，通过自监督预训练和对抗增强学习结合结构和时间证据解决企业交易网络中的复杂金融欺诈问题。
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Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
研究通过上下文增强的大型语言模型整合非传统数据源和财务信息，以提高公司业绩预测的准确性。
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Adaptive Hierarchical Representation Alliance for Multimodal Learning
为解决多模态学习中的语义粒度不匹配问题，提出自适应层次表示联盟(AHRA)方法，通过分层共享-私有专家框架增强任务相关性。
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Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
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Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control
本文提出GTFD，一种图-变换器欺诈检测方法，通过自监督预训练和对抗增强学习结合结构和时间证据解决企业交易网络中的复杂金融欺诈问题。
0 citationsRead paper
Diffusion models for dynamic volatility surface generation and data-driven hedging
本文通过开发一种扩散模型框架来生成动态隐含波动率曲面，并通过数据驱动的对冲评估其经济实用性，有效减少了静态套利违规情况。
0 citationsRead paper
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
研究通过上下文增强的大型语言模型整合非传统数据源和财务信息，以提高公司业绩预测的准确性。
0 citationsRead paper
Adaptive Hierarchical Representation Alliance for Multimodal Learning
为解决多模态学习中的语义粒度不匹配问题，提出自适应层次表示联盟(AHRA)方法，通过分层共享-私有专家框架增强任务相关性。
0 citationsRead paper
Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
This work addresses the challenge of multimodal sentiment analysis in real-world scenarios, where performance is often hindered by missing observational data and the lack of explicit modeling of modality reliability in existing methods, leading to reliability mismatch and propagation bias. To overcome these limitations, the authors propose the Modality Reliability-aware Collaborative Fusion (MRCF) framework, which introduces, for the first time, a sample-level modality reliability assessment mechanism that integrates intra-modality quality cues with cross-modality semantic consistency. MRCF dynamically regulates multimodal information flow through a reliability-aware branch, a reliability-guided interaction mechanism, and a calibration-based fusion module. Extensive experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS demonstrate that the proposed approach significantly enhances model robustness and accuracy under incomplete observational conditions.
0 citationsRead paper