Search Results for author: Gerald Friedland

Found 13 papers, 7 papers with code

Multi-modal Ensemble Models for Predicting Video Memorability

1 code implementation1 Feb 2021 Tony Zhao, Irving Fang, Jeffrey Kim, Gerald Friedland

Modeling media memorability has been a consistent challenge in the field of machine learning.

BIG-bench Machine Learning

From Tinkering to Engineering: Measurements in Tensorflow Playground

no code implementations11 Jan 2021 Henrik Hoeiness, Axel Harstad, Gerald Friedland

In this article, we present an extension of the Tensorflow Playground, called Tensorflow Meter (short TFMeter).

Experimental Design

DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models

1 code implementation19 Oct 2020 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.

Cross-Modal Retrieval Retrieval

Efficient Saliency Maps for Explainable AI

1 code implementation26 Nov 2019 T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland

This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods.

Astronomy

One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy

no code implementations23 Oct 2018 Jingkang Wang, Ruoxi Jia, Gerald Friedland, Bo Li, Costas Spanos

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 in an arbitrary failure of an otherwise seemingly well-trained ML model.

Decision Making

A Practical Approach to Sizing Neural Networks

no code implementations4 Oct 2018 Gerald Friedland, Alfredo Metere, Mario Krell

This allows an estimate of the required size of a neural network for a given problem.

Memorization

The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity

1 code implementation10 Jul 2018 Gerald Friedland, Jingkang Wang, Ruoxi Jia, Bo Li

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 in the machine learning process and if so, how much?

BIG-bench Machine Learning

A Capacity Scaling Law for Artificial Neural Networks

1 code implementation20 Aug 2017 Gerald Friedland, Mario Krell

First, we derive the calculation of what we call the lossless memory (LM) dimension.

Experimental Design

The YLI-MED Corpus: Characteristics, Procedures, and Plans

no code implementations13 Mar 2015 Julia Bernd, Damian Borth, Benjamin Elizalde, Gerald Friedland, Heather Gallagher, Luke Gottlieb, Adam Janin, Sara Karabashlieva, Jocelyn Takahashi, Jennifer Won

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. e., automatically identifying what's happening in a video by analyzing the audio and visual content.

Descriptive Event Detection

YFCC100M: The New Data in Multimedia Research

2 code implementations5 Mar 2015 Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, Li-Jia Li

We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released.

Multimedia Computers and Society H.3.7

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