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Found 5 papers, 3 papers with code
Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions
We generate LDF using a novel two-stage autoencoder model that learns from clusters of similar source and target domain data.
Towards Effective Paraphrasing for Information Disguise
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet.
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
ISTRBoost: Importance Sampling Transfer Regression using Boosting
Our methodology,~\us{}, is a boosting and random-forest based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset.
Diagnosing Data from ICTs to Provide Focused Assistance in Agricultural Adoptions
Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations.
