23 citations · 35 across the 4 of their papers we have counts for
7 papers
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman +2
Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the p…
The Future of Computing Research: Industry-Academic Collaborations
Nady Boules, Khari Douglas, Stuart Feldman +8
IT-driven innovation is an enormous factor in the worldwide economic leadership of the United States. It is larger than finance, construction, or transportation, and it employs nea…
An Uncertain Future: Forecasting from Static Images using Variational Autoencoders
Jacob Walker, Carl Doersch, Abhinav Gupta +1
In a given scene, humans can often easily predict a set of immediate future events that might happen. However, generalized pixel-level anticipation in computer vision systems is di…
Robust Monocular Flight in Cluttered Outdoor Environments
Shreyansh Daftry, Sam Zeng, Arbaaz Khan +4
Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straight-for…
Cross-stitch Networks for Multi-task Learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta +1
Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representation…
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
Ishan Misra, Abhinav Shrivastava, Martial Hebert
We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hund…