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Found 8 papers, 2 papers with code
Demystifying Chains, Trees, and Graphs of Thoughts
Among these, prompt engineering coupled with structures has emerged as a promising paradigm, with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, in which the overall LLM reasoning is guided by a structure such as a graph.
DiffDA: a Diffusion model for weather-scale Data Assimilation
The experiments also show that the initial conditions assimilated from sparse observations (less than 0. 77% of gridded data) and 48-hour forecast can be used for forecast models with a loss of lead time of at most 24 hours compared to initial conditions from state-of-the-art data assimilation in ERA5.
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
A fundamental workload in this setting is dynamic link prediction: using a history of graph updates to predict whether a given pair of vertices will become connected.
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT).
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
We propose the ENS-10 prediction correction task for improving the forecast quality at a 48-hour lead time through ensemble post-processing.
Learning Combinatorial Node Labeling Algorithms
We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph coloring.
Motif Prediction with Graph Neural Networks
We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis.
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms.
