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Found 6 papers, 3 papers with code
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation.
When Heterophily Meets Heterogeneity: New Graph Benchmarks and Effective Methods
However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graphs with heterophily, leaving a gap in understanding how methods perform on graphs that are both heterogeneous and heterophilic.
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neural networks (GNNs)?
Personalized Federated Learning under Mixture of Distributions
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Extracting temporal relations (e. g., before, after, and simultaneous) among events is crucial to natural language understanding.
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation
Recent studies have demonstrated a perceivable improvement on the performance of neural machine translation by applying cross-lingual language model pretraining (Lample and Conneau, 2019), especially the Translation Language Modeling (TLM).
