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Found 8 papers, 0 papers with code
Poro 34B and the Blessing of Multilinguality
The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages.
Deep learning bank distress from news and numerical financial data
Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space.
News-sentiment networks as a risk indicator
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk.
Risk Management Statistical Finance Trading and Market Microstructure
Bank distress in the news: Describing events through deep learning
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated.
Detect & Describe: Deep learning of bank stress in the news
We model bank distress with data on 243 events and 6. 6M news articles for 101 large European banks.
Interactive Visual Exploration of Topic Models using Graphs
Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents.
Automated and Weighted Self-Organizing Time Maps
The SOTM provides means for a visual approach to evolutionary clustering, which aims at producing a sequence of clustering solutions.
Cluster coloring of the Self-Organizing Map: An information visualization perspective
From the viewpoint of information visualization, this paper provides a general, yet simple, solution to projection-based coloring of the SOM that reveals structures.
