5 citations · 11 across the 7 of their papers we have counts for
7 papers
Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
Mohammad Reza Zarei, Michael Christensen, Sarah Everts +1
Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. As this hesitancy undermines vaccine campaigns, many researchers have s…
Toward Super-Resolution for Appearance-Based Gaze Estimation
Galen O'Shea, Majid Komeili
Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is esse…
Interpretable Few-shot Learning with Online Attribute Selection
Mohammad Reza Zarei, Majid Komeili
Few-shot learning (FSL) presents a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot clas…
Interpretable Concept-based Prototypical Networks for Few-Shot Learning
Mohammad Reza Zarei, Majid Komeili
Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. How…
Measuring Cognitive Status from Speech in a Smart Home Environment
Kathleen C. Fraser, Majid Komeili
The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and…
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models
Omid Davoodi, Majid Komeili
Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models' decisions to humans. Reinforcement Learning…
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks
Mohammad Nokhbeh Zaeem, Majid Komeili
In many scenarios, human decisions are explained based on some high-level concepts. In this work, we take a step in the interpretability of neural networks by examining their inter…