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Artificial Intelligence and Robotics Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Artificial Intelligence and Robotics
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …
Going Big: A Large-Scale Study On What Big Data Developers Ask, Mehdi Bagherzadeh, Raffi T. Khatchadourian
Going Big: A Large-Scale Study On What Big Data Developers Ask, Mehdi Bagherzadeh, Raffi T. Khatchadourian
Publications and Research
Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into …
Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo
Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo
Dissertations, Theses, and Capstone Projects
We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the …
Comparing Tensorflow Deep Learning Performance Using Cpus, Gpus, Local Pcs And Cloud, John Lawrence, Jonas Malmsten, Andrey Rybka, Daniel A. Sabol, Ken Triplin
Comparing Tensorflow Deep Learning Performance Using Cpus, Gpus, Local Pcs And Cloud, John Lawrence, Jonas Malmsten, Andrey Rybka, Daniel A. Sabol, Ken Triplin
Publications and Research
Deep learning is a very computational intensive task. Traditionally GPUs have been used to speed-up computations by several orders of magnitude. TensorFlow is a deep learning framework designed to improve performance further by running on multiple nodes in a distributed system. While TensorFlow has only been available for a little over a year, it has quickly become the most popular open source machine learning project on GitHub. The open source version of TensorFlow was originally only capable of running on a single node while Google’s proprietary version only was capable of leveraging distributed systems. This has now changed. In this …