8 citations · 28 across the 11 of their papers we have counts for
8 papers · 1 filter
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +2
Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inducti…
Quality Measures for Dynamic Graph Generative Models
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +2
Deep generative models have recently achieved significant success in modeling graph data, including dynamic graphs, where topology and features evolve over time. However, unlike in…
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
Ryien Hosseini, Filippo Simini, Venkatram Vishwanath +1
Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of s…
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives
Romain Egele, Tyler Chang, Yixuan Sun +2
Machine learning (ML) methods offer a wide range of configurable hyperparameters that have a significant influence on their performance. While accuracy is a commonly used performan…
A Survey of Techniques for Optimizing Transformer Inference
Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani +2
Recent years have seen a phenomenal rise in performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Represe…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…