A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
The paper proposes a method to find interpretable subspaces in node embeddings using a knowledge base.
Deep learning improves anomaly detection across various fields.
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate class…
Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and v…
Taxonomies are semantic hierarchies of concepts. One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method t…
Bayesian algorithm improves word representations using semantic taxonomy.
Unified taxonomy for ML uncertainty in physics, validated.
Taxonomy for ML in simulations, covering patterns and algorithms.
We investigate sets of financial non-redundant and nonsynchronously recorded time series. The sets are composed by a number of stock market indices located all over the world in five continents. By properly selecting the time horizon of returns and by using a reference currency we find a meaningful taxonomy. The detect…
In this paper, a taxonomy for memory networks is proposed based on their memory organization. The taxonomy includes all the popular memory networks: vanilla recurrent neural network (RNN), long short term memory (LSTM ), neural stack and neural Turing machine and their variants. The taxonomy puts all these networks und…
Unified taxonomy categorizes DL-based MTSAD methods.
TXtract extracts structured knowledge from thousands of product categories.
Proposes a taxonomy for economic policies.
New system constructs cell-type taxonomy across multiple samples.
The paper discusses building ETF risk models using a multilevel classification taxonomy.
Taxonomies of cryptocurrencies and comparisons with fiat money and databases.
This paper reviews LLMs for credit risk assessment, creating a taxonomy.
New taxonomy divides defense methods for neural networks.
Owners of a web-site are often interested in analysis of groups of users of their site. Information on these groups can help optimizing the structure and contents of the site. In this paper we use an approach based on formal concepts for constructing taxonomies of user groups. For decreasing the huge amount of concepts…
In E-commerce, it is a common practice to organize the product catalog using product taxonomy. This enables the buyer to easily locate the item they are looking for and also to explore various items available under a category. Product taxonomy is a tree structure with 3 or more levels of depth and several leaf nodes. P…
This research categorizes AMM designs for secure token exchanges.
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various…
New taxonomy reveals different detection limits for various types of fraud.
Electronic health record (EHR) systems are used extensively throughout the healthcare domain. However, data interchangeability between EHR systems is limited due to the use of different coding standards across systems. Existing methods of mapping coding standards based on manual human experts mapping, dictionary mappin…
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their…
Paper proposes a comprehensive taxonomy for crypto assets.
Research creates a taxonomy to bridge AI security and regulatory gaps.
New taxonomy and evaluation of neural network compression methods.
As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data source…
We introduce an algorithm able to reconstruct the relevant network structure on which the time evolution of country-product bipartite networks takes place. The significant links are obtained by selecting the largest values of the projected matrix. We first perform a number of tests of this filtering procedure on synthe…
Unifies 18 definitions of surprise, classifies them into four categories.
Proposes a method to predict stock movements using fine-grained events from finance news.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
The appeal of metric evaluation of research impact has attracted considerable interest in recent times. Although the public at large and administrative bodies are much interested in the idea, scientists and other researchers are much more cautious, insisting that metrics are but an auxiliary instrument to the qualitati…
Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.
Fine-grained gap-dependent regret bounds for reinforcement learning.
This work tackles manifold regression onto hyperbolic space for tree classification and taxonomy extension.
Taxonomy of knowledge modalities in RL for better transfer.
FIGARO generates symbolic music with fine-grained control.
DistPre predicts traffic speeds efficiently for large networks.
Framework for assessing explainable AI systems.
The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakt…
Fine-grained pretraining improves neural network's ability to learn rare features.
Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes have been developed for this purpose. However, in contrast to how humans categorize objects, these approaches have not yet made use of any tax…
The standard taxonomy of predictive uncertainty is inconsistent with standard measures.
ECN framework improves training on noisy structured labels.