5 citations · 11 across the 4 of their papers we have counts for
4 papers
DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue
Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss +1
Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability. This…
Machine Learning for Temporal Data in Finance: Challenges and Opportunities
Jason Wittenbach, Brian d'Alessandro, C. Bayan Bruss
Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern da…
Towards Ground Truth Explainability on Tabular Data
Brian Barr, Ke Xu, Claudio Silva +4
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes fro…
On the Interpretability and Evaluation of Graph Representation Learning
Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have impro…