no code implementations • 20 Feb 2024 • Kewei Cheng, Nesreen K. Ahmed, Theodore Willke, Yizhou Sun
Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
no code implementations • 3 Feb 2024 • Cecilia Aguerrebere, Mark Hildebrand, Ishwar Singh Bhati, Theodore Willke, Mariano Tepper
In this work, we study LVQ in streaming similarity search.
no code implementations • 22 Sep 2022 • Guixiang Ma, Vy A. Vo, Theodore Willke, Nesreen K. Ahmed
We provide a comprehensive review of the existing literature on memory-augmented GNNs.
no code implementations • 25 Apr 2022 • Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capota, Theodore Willke, Shahin Nazarian, Paul Bogdan
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core in heterogeneous hardware platforms.