Search Results for author:
Found 8 papers, 2 papers with code
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers
The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network.
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
We find that the ensemble model overfitted to the training set shows sub-par calibration performance and also observe that PLMs trained with confidence penalty loss have a trade-off between calibration and accuracy.
Deep learning-based citation recommendation system for patents
In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system.
Machine-Learning Approach to Analyze the Status of Forklift Vehicles with Irregular Movement in a Shipyard
We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing.
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research.
Deep Patent Landscaping Model Using Transformer and Graph Embedding
Patent landscaping is a method used for searching related patents during a research and development (R&D) project.
Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service
Our model uses metadata for contents for prediction, so we use categorical embedding techniques to solve the sparsity of categorical variables and make them learn efficiently for the deep neural net model.
Text Classification using Capsules
This paper presents an empirical exploration of the use of capsule networks for text classification.
