Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality

1 code implementation • NAACL 2021 • Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula, Huy Vu, H. Andrew Schwartz

In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers.

Dimensionality Reduction

Predicting Responses to Psychological Questionnaires from Participants' Social Media Posts and Question Text Embeddings

no code implementations • Findings of the Association for Computational Linguistics 2020 • Huy Vu, Suhaib Abdurahman, Sudeep Bhatia, Lyle Ungar

Finally, as a side contribution, the success of our model also suggests a new approach to study survey questions using NLP tools such as text embeddings rather than response data used in traditional methods.

Unrolling of Deep Graph Total Variation for Image Denoising

1 code implementation • 21 Oct 2020 • Huy Vu, Gene Cheung, Yonina C. Eldar

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set.

Image Denoising Rolling Shutter Correction

Suicide Risk Assessment with Multi-level Dual-Context Language and BERT

no code implementations • WS 2019 • Matthew Matero, Akash Idnani, Youngseo Son, Salvatore Giorgi, Huy Vu, Mohammad Zamani, Parth Limbachiya, Sharath Ch Guntuku, ra, H. Andrew Schwartz

Mental health predictive systems typically model language as if from a single context (e. g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e. g. either the message-level or user-level).