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Found 7 papers, 7 papers with code
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
BioImageLoader: Easy Handling of Bioimage Datasets for Machine Learning
BioImageLoader (BIL) is a python library that handles bioimage datasets for machine learning applications, easing simple workflows and enabling complex ones.
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
First, GLI is designed to incentivize \emph{dataset contributors}.
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
By updating only 0. 1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated.
Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling
The problem of learning mixture of MNL models from partial rankings naturally arises in such applications.
Interpretable exact linear reductions via positivity
We design and implement an algorithm that, given an exact linear reduction, re-parametrizes it by performing an invertible transformation of the new coordinates to improve the interpretability of the new variables.
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.
