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Found 4 papers, 0 papers with code
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text
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.
Locally-Adaptive Quantization for Streaming Vector Search
In this work, we study LVQ in streaming similarity search.
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review
We provide a comprehensive review of the existing literature on memory-augmented GNNs.
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning
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.
