393 citations · 634 across the 19 of their papers we have counts for
9 papers · 1 filter
Understanding Transformer Reasoning Capabilities via Graph Algorithms
Clayton Sanford, Bahare Fatemi, Ethan Hall +5
Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based…
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin +5
Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, so…
Stay Positive: Knowledge Graph Embedding Without Negative Sampling
Ainaz Hajimoradlou, Mehran Kazemi
Knowledge graphs (KGs) are typically incomplete and we often wish to infer new facts given the existing ones. This can be thought of as a binary classification problem; we aim to p…
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this pr…
Out-of-Sample Representation Learning for Multi-Relational Graphs
Marjan Albooyeh, Rishab Goel, Seyed Mehran Kazemi
Many important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to…
Time2Vec: Learning a Vector Representation of Time
Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali +7
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focu…
Diachronic Embedding for Temporal Knowledge Graph Completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker +1
Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been propos…
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain +4
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learni…
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Nandini Ramanan, Gautam Kunapuli, Tushar Khot +5
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight…