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.
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
ADNN uses prior knowledge to construct financial features.
Unsupervised relation discovery aims to discover new relations from a given text corpus without annotated data. However, it does not consider existing human annotated knowledge bases even when they are relevant to the relations to be discovered. In this paper, we study the problem of how to use out-of-relation knowledg…
Bayesian approach for solving systems of linear PDEs with boundary conditions.
A user-friendly interface constructs effective background knowledge from ER diagrams.
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
Transformer-based method for causal discovery with prior knowledge integration.
Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-sca…
A new tensor-based method for predicting temporal relationships in knowledge bases.
Method uses network biology to construct gene expression models for cancer.
System constructs public competitor graph from financial reports.
KG-WDRO optimizes transfer learning with external knowledge.
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…
Given a geodesic space (E, d), we show that full ordinal knowledge on the metric d-i.e. knowledge of the function D d : (w, x, y, z) 1 d(w,x)d(y,z) , determines uniquely-up to a constant factor-the metric d. For a subspace En of n points of E, converging in Hausdorff distance to E, we construct a met…
Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs …
This text aims to explain general relativity to geometers who have no knowledge about physics. Using handwritten notes by Michel Vaugon, we construct the bases of the theory.
Machine learning constructs problem-based medical records from electronic health records.
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges…
New SMC algorithm reduces memory usage for online clustering.
Paper proposes AI for stock market forecasting using external knowledge.
Paper integrates ML with physics models for engineering and environmental challenges.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
System helps engineers with concept recognition for SEVA.
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we furt…
We obtain an algorithmic construction of the isotropy lattice for a lifted action of a Lie group on and based only on the knowledge of and its action on . Some applications to symplectic geometry are also shown.
A new system recommends specific knowledge concepts in MOOCs based on student interests.
ARML learns task relations to improve meta-learning efficiency.
Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and …
In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previous tasks, the accumulated knowledge should be learned in a way which captures the common structure across learned tasks, while allowing the…
MoleculeSTM learns from molecule structures and texts for better drug design.
PKB framework boosts genomic data analysis by integrating pathway knowledge.
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance ratin…
Crowdsourced wisdom improves causal learning.
Interactive steering improves hierarchical clustering for diverse user needs.
Paper proposes NNAFC for automatic financial factor construction.
In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction of such models. In this work, we argue that dynamically bound f…
Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfer the model learned from seen classes to unseen classes. Existing works in GZSL usually assume that some prior information about unseen clas…
Proposes statistical inference for dependency knowledge graphs from EHR data.
Survey of methods to incorporate external knowledge into stock price prediction.
This work integrates domain knowledge into A*-based causal discovery methods.
Many efforts have been made to use various forms of domain knowledge in malware detection. Currently there exist two common approaches to malware detection without domain knowledge, namely byte n-grams and strings. In this work we explore the feasibility of applying neural networks to malware detection and feature lear…
New method simplifies causal inference with tiered background knowledge.
Knowledge bases contribute to many web search and mining tasks, yet they are often incomplete. To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature. Perhaps surprisingly, relatively simple models with limited expressiveness often performed remarkably well …
Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization …
We add prior knowledge to deep networks to make them invariant to transformations.
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…
This paper improves Gaussian process predictions by integrating prior knowledge.
The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning me…