Search Results for author:
Found 7 papers, 5 papers with code
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism.
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time.
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers.
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance.
Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets.
Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis
To solve this problem, we propose intuitive objective functions, and develop an an efficient iterative algorithm which we call the NEO-CC algorithm.
Fast Multiplier Methods to Optimize Non-exhaustive, Overlapping Clustering
In this paper, we consider two fast multiplier methods to accelerate the convergence of an augmented Lagrangian scheme: a proximal method of multipliers and an alternating direction method of multipliers (ADMM).
