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 proposes AI for stock market forecasting using external knowledge.
KGRL uses reinforcement learning with knowledge graphs for better interactive recommendation.
FinDKG uses LLMs to detect financial trends from news articles.
We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (…
Graph Hawkes Neural Network forecasts evolving graph sequences.
DArtNet predicts time series data using graph structure and dynamic attributes.
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces import…
Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural networ…
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (…
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…
This paper reviews feature selection in KGs for improved ML model performance.
A new framework for knowledge graph embedding using sheaves.
Graph learning captures financial dynamics over time.
CauSTream forecasts streamflow by integrating causal graphs for better interpretability.
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, …
Framework predicts Navier-Stokes solutions on 2D domains using graph neural networks.
Method finds motifs in knowledge graphs, revealing their structure.
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in …
The regression of multiple inter-connected sequence data is a problem in various disciplines. Formally, we name the regression problem of multiple inter-connected data entities as the "dynamic network regression" in this paper. Within the problem of stock forecasting or traffic speed prediction, we need to consider bot…
Graph change-point detection method learns graph similarity from data.
Paper proposes KE-GCN for better graph embedding.
We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background know…
Paper tackles dynamic graph topology identification in time-varying graphs.
Proposes statistical inference for dependency knowledge graphs from EHR data.
Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Us…
Proposes a new method for predicting missing relations in knowledge graphs.
Paper interprets contrastive learning dynamics using message passing.
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its ef…
ZSL-KG learns class representations from common sense knowledge graphs.
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (\eg articles with citation link tend to be in the same class), graph neural networks could be more sensiti…
Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end…
This work proposes a hybrid method for error detection in noisy Knowledge Graphs.
This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connecte…
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.
Study on veering triangulations and their flow graphs, proving new applications.
A model for POI recommendation using relation embedding.
Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems. The domain ontology (i.e., specification of domains, slots, and values) of a conversational AI system is generally incomplete, making the capability for DST models to generalize to new slots, values, and domains during inf…
Survey of methods to incorporate external knowledge into stock price prediction.
This paper defines resilience in knowledge graph embeddings and surveys existing works.
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior…
Enhances knowledge graph completion with mixed geometry tensor factorization.
Study restricts causal graphs with expert knowledge.
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous…
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.