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Found 8 papers, 3 papers with code
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.
SQL-CRAFT: Text-to-SQL through Interactive Refinement and Enhanced Reasoning
Modern LLMs have become increasingly powerful, but they are still facing challenges in specialized tasks such as Text-to-SQL.
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.
Exploring Generative Neural Temporal Point Process
While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.
STONet: A Neural-Operator-Driven Spatio-temporal Network
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks.
