18 citations · 41 across the 9 of their papers we have counts for
17 papers
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF
Haoming Guo, Tianyi Huang, Huixuan Huang +2
The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have mad…
Multi-modal Ensemble Models for Predicting Video Memorability
Tony Zhao, Irving Fang, Jeffrey Kim +1
Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among…
OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
Daniel Ma, Gerald Friedland, Mario Michael Krell
Origami is becoming more and more relevant to research. However, there is no public dataset yet available and there hasn't been any research on this topic in machine learning. We c…
From Tinkering to Engineering: Measurements in Tensorflow Playground
Henrik Hoeiness, Axel Harstad, Gerald Friedland
In this article, we present an extension of the Tensorflow Playground, called Tensorflow Meter (short TFMeter). TFMeter is an interactive neural network architecting tool that allo…
DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models
Tony Zhao, Jaeyoung Choi, Gerald Friedland
Cross-modal retrieval relies on accurate models to retrieve relevant results for queries across modalities such as image, text, and video. In this paper, we build upon previous wor…
Efficient Saliency Maps for Explainable AI
T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. I…