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.
Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.
Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized co…
Efficient autoregressive entity linking with correction for faster, more accurate results.
Efficiently estimates uncertainty for LLM-based entity linking in tabular data.
mGENRE improves multilingual entity linking with autoregressive sequence prediction.
In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testi…
GENRE retrieves entities autoregressively, improving efficiency and accuracy.
Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph const…
Improves medication name inference for telemedicine and conversational agents.
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where t…
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However,…
VB-Score evaluates AI systems without ground truth, revealing robustness.
More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like to object to the assumption that having an address is a luxury, by proposing a generative address design that maps the world in accordance w…
Paper proposes a deep learning model for understanding e-commerce addresses.
Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learn…
ETHGamDet detects crypto gambling contracts and addresses.
Chaos and nonlinear economic dynamics are addressed for a quantum coupled map lattice model of an artificial economy, with quantized supply and demand equilibrium conditions. The measure theoretic properties and the patterns that emerge in both the economic business volume dynamics' diagrams as well as in the quantum m…
Classifies Bitcoin addresses based on their balance functions.
Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payment…
In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previous MANNs suffer from complex memory addressing mechanism, making them relatively hard to train and causing computational overheads. Moreove…
1. Translated by Thomas E. Cecil, Department of Mathematics and Computer Science, College of the Holy Cross, Worcester, MA 01610, USA; E-mail address: cecil@mathcs.holycross.edu 2. Typed by Wenjiao Yan, School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing 10…
We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones whe…
Corrects errors in Hans' pseudocovering spaces paper.
Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intrigui…
In this paper, we address the issue of quaternionic Toledo invariant to study the character variety of two dimensional complex hyperbolic uniform lattices into . We construct four distinct representations to prove that the character variety contains at least four distinct components. We also address the existe…
New federated conformal prediction method addresses label shift for uncertainty quantification.
Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address it…
New method improves blockchain analysis by handling temporal changes and scalability.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.
New method improves causal effect estimation by addressing imbalance in training data.
GRAND treats GNNs as PDE discretizations, addressing graph learning issues.
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from…
Study addresses RTB model performance drops due to distribution shifts.
The study explores maximal symmetry in Ricci solitons on Lie groups.
Paper optimizes hyperspherical prototypes for better class separation.
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…
FedFaiREE addresses fairness in decentralized learning with small samples.
IntelligentPooling improves treatment decisions in mHealth.
A new approach improves numerical tabular data imputation by addressing diffusion models' limitations.
Sensorimotor contingency theory offers a promising account of the nature of perception, a topic rarely addressed in the robotics community. We propose a developmental framework to address the problem of the autonomous acquisition of sensorimotor contingencies by a naive robot. While exploring the world, the robot inter…
DoubleGen addresses bias in generative modeling of counterfactuals.
Researchers improve NCE by addressing its flat loss landscape issues.
The paper addresses optimal execution for multi-asset portfolios using Ornstein-Uhlenbeck dynamics.
Bayesian optimization improves with transfer learning for aircraft design.
This paper analyzes tokenized U.S. Treasuries, revealing patterns and roles in blockchain transactions.
Addresses theoretical and practical aspects of Gaussian differential privacy.
This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normality of the proposed estimator (which has the form of an Aalen-Johansen i…
This paper uses GANs to generate synthetic Bitcoin address data.