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Found 7 papers, 3 papers with code
Can we Soft Prompt LLMs for Graph Learning Tasks?
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs.
Ranked #2 on
Node Property Prediction
on ogbn-arxiv
(using extra training data)
A Generalization of ViT/MLP-Mixer to Graphs
First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.
Ranked #1 on
Graph Regression
on Peptides-struct
A Study on Transformer Configuration and Training Objective
In this paper, we revisit these conventional configurations.
Ranked #101 on
Image Classification
on ImageNet
One Student Knows All Experts Know: From Sparse to Dense
Mixture-of-experts (MoE) is a powerful sparse architecture including multiple experts.
Large-Scale Deep Learning Optimizations: A Comprehensive Survey
Deep learning have achieved promising results on a wide spectrum of AI applications.
