32 citations · 57 across the 6 of their papers we have counts for
6 papers
Translation between Molecules and Natural Language
Carl Edwards, Tuan Lai, Kevin Ros +3
We present a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5…
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
Ayse S. Cakmak, Nina Thigpen, Garrett Honke +9
Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non…
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
Garrett Honke, Irina Higgins, Nina Thigpen +6
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based ne…
Using Deep Networks and Transfer Learning to Address Disinformation
Numa Dhamani, Paul Azunre, Jeffrey L. Gleason +4
We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disin…
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks
Paul Azunre, Craig Corcoran, Numa Dhamani +6
A character-level convolutional neural network (CNN) motivated by applications in "automated machine learning" (AutoML) is proposed to semantically classify columns in tabular data…
Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings
Paul Azunre, Craig Corcoran, David Sullivan +4
This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary. Assuming the dataset contains d…