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Found 10 papers, 5 papers with code
How Expressive are Transformers in Spectral Domain for Graphs?
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
Sparsity-based Feature Selection for Anomalous Subgroup Discovery
However, there is a common lack of a principled and scalable feature selection method for efficient discovery.
Post-discovery Analysis of Anomalous Subsets
Analyzing the behaviour of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require specialized attention, but also to assist in designing future interventions.
Automated Supervised Feature Selection for Differentiated Patterns of Care
The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features.
Ranking Facts for Explaining Answers to Elementary Science Questions
In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice.
SpecTRA: Spectral Transformer for Graph Representation Learning
Transformers have recently been applied in the more generic domain of graphs.
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on
Entity Linking
on MSNBC
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
