1.6k citations · 1.7k across the 20 of their papers we have counts for
20 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…
Cybercasing 2.0: You Get What You Pay For
Jaeyoung Choi, Istemi Ekin Akkus, Serge Egelman +4
Under U.S. law, marketing databases exist under almost no legal restrictions concerning accuracy, access, or confidentiality. We explore the possible (mis)use of these databases in…
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Jingkang Wang, Ruoxi Jia, Gerald Friedland +2
Despite the great success achieved in machine learning (ML), adversarial examples have caused concerns with regards to its trustworthiness: A small perturbation of an input results…
A Practical Approach to Sizing Neural Networks
Gerald Friedland, Alfredo Metere, Mario Krell
Memorization is worst-case generalization. Based on MacKay's information theoretic model of supervised machine learning, this article discusses how to practically estimate the maxi…
The Accuracy of the Demographic Inferences Shown on Google's Ad Settings
Michael Carl Tschantz, Serge Egelman, Jaeyoung Choi +2
Google's Ad Settings shows the gender and age that Google has inferred about a web user. We compare the inferred values to the self-reported values of 501 survey participants. We f…
The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity
Gerald Friedland, Jingkang Wang, Ruoxi Jia +1
This paper proposes a fundamental answer to a frequently asked question in multimedia computing and machine learning: Do artifacts from perceptual compression contribute to error i…
Principle of Conservation of Computational Complexity
Gerald Friedland, Alfredo Metere
In this manuscript, we derive the principle of conservation of computational complexity. We measure computational complexity as the number of binary computations (decisions) requir…
Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)
Mario Michael Krell, Julia Bernd, Yifan Li +5
Social multimedia users are increasingly sharing all kinds of data about the world. They do this for their own reasons, not to provide data for field studies-but the trend presents…
Audio Concept Classification with Hierarchical Deep Neural Networks
Mirco Ravanelli, Benjamin Elizalde, Karl Ni +1
Audio-based multimedia retrieval tasks may identify semantic information in audio streams, i.e., audio concepts (such as music, laughter, or a revving engine). Conventional Gaussia…
A Capacity Scaling Law for Artificial Neural Networks
Gerald Friedland, Mario Krell
We derive the calculation of two critical numbers predicting the behavior of perceptron networks. First, we derive the calculation of what we call the lossless memory (LM) dimensio…
Isomorphism between Maximum Lyapunov Exponent and Shannon's Channel Capacity
Gerald Friedland, Alfredo Metere
We demonstrate that the Maximum Lyapunov Exponent for computable dynamical systems is isomorphic to the maximum capacity of a noiseless, memoryless channel in a Shannon communicati…
DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection
Liping Jing, Bo Liu, Jaeyoung Choi +4
This paper presents a novel two-phase method for audio representation, Discriminative and Compact Audio Representation (DCAR), and evaluates its performance at detecting events in…
Where to be wary: The impact of widespread photo-taking and image enhancement practices on users' geo-privacy
Jaeyoung Choi, Martha Larson, Xinchao Li +2
Today's geo-location estimation approaches are able to infer the location of a target image using its visual content alone. These approaches exploit visual matching techniques, app…
The YLI-MED Corpus: Characteristics, Procedures, and Plans
Julia Bernd, Damian Borth, Benjamin Elizalde +7
The YLI Multimedia Event Detection corpus is a public-domain index of videos with annotations and computed features, specialized for research in multimedia event detection (MED), i…
YFCC100M: The New Data in Multimedia Research
Bart Thomee, David A. Shamma, Gerald Friedland +5
We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 1…