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Found 7 papers, 3 papers with code
Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach
Bank crisis is challenging to define but can be manifested through bank contagion.
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semantic representations and correlations.
Ranked #1 on
TGIF-Frame
on TGIF-QA
(using extra training data)
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA).
Ranked #1 on
Image Captioning
on COCO Captions
(SPICE metric, using extra
training data)
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
In this paper, we propose a novel method called Joint QA and DC GEneration (JADE), which utilizes a pre-trained multimodal model and easily-crawled image-text pairs to automatically generate and filter large-scale VQA and dense captioning datasets.
A Method for Predicting VaR by Aggregating Generalized Distributions Driven by the Dynamic Conditional Score
Constructing a more effective value at risk (VaR) prediction model has long been a goal in financial risk management.
Value-at-Risk forecasting model based on normal inverse Gaussian distribution driven by dynamic conditional score
Under the framework of dynamic conditional score, we propose a parametric forecasting model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday information into daily VaR forecast.
Research on Portfolio Liquidation Strategy under Discrete Times
The permanent impact generated by an asset in the portfolio during the liquidation will affect all assets, and the temporary impact generated by one asset will only affect itself.
