no code implementations • CVPR 2021 • Hamad Ahmed, Ronnie B Wilbur, Hari M Bharadwaj, Jeffrey Mark Siskind
A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments.
no code implementations • 18 Dec 2018 • Ren Li, Jared S. Johansen, Hamad Ahmed, Thomas V. Ilyevsky, Ronnie B Wilbur, Hari M Bharadwaj, Jeffrey Mark Siskind
A recent paper [arXiv:1609. 00344] claims to classify brain processing evoked in subjects watching ImageNet stimuli as measured with EEG and to use a representation derived from this processing to create a novel object classifier.
no code implementations • 10 Nov 2016 • Jeffrey Mark Siskind, Barak A. Pearlmutter
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade.
no code implementations • 10 Nov 2016 • Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical.
no code implementations • 10 Nov 2016 • Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the . NET ecosystem, which is targeted by the C# and F# languages, among others.
no code implementations • 18 Nov 2015 • Daniel Paul Barrett, ran Xu, Haonan Yu, Jeffrey Mark Siskind
We make available to the community a new dataset to support action-recognition research.
no code implementations • 25 Aug 2015 • Daniel Paul Barrett, Scott Alan Bronikowski, Haonan Yu, Jeffrey Mark Siskind
We present a unified framework which supports grounding natural-language semantics in robotic driving.
no code implementations • 5 Jun 2015 • Haonan Yu, Jeffrey Mark Siskind
We tackle the problem of video object codetection by leveraging the weak semantic constraint implied by sentences that describe the video content.
4 code implementations • 20 Feb 2015 • Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning.
no code implementations • 14 Nov 2014 • Haonan Yu, Daniel P. Barrett, Jeffrey Mark Siskind
Prior work presented the sentence tracker, a method for scoring how well a sentence describes a video clip or alternatively how well a video clip depicts a sentence.
no code implementations • 9 Aug 2014 • Andrei Barbu, Alexander Bridge, Zachary Burchill, Dan Coroian, Sven Dickinson, Sanja Fidler, Aaron Michaux, Sam Mussman, Siddharth Narayanaswamy, Dhaval Salvi, Lara Schmidt, Jiangnan Shangguan, Jeffrey Mark Siskind, Jarrell Waggoner, Song Wang, Jinlian Wei, Yifan Yin, Zhiqi Zhang
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it.
no code implementations • 20 Sep 2013 • Andrei Barbu, N. Siddharth, Jeffrey Mark Siskind
We present an approach to searching large video corpora for video clips which depict a natural-language query in the form of a sentence.
no code implementations • CVPR 2014 • N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind
We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, thereby providing a medium, not only for top-down and bottom-up integration, but also for multi-modal integration between vision and language.
no code implementations • 21 Jun 2013 • Haonan Yu, Jeffrey Mark Siskind
We present a method for learning word meanings from complex and realistic video clips by discriminatively training (DT) positive sentential labels against negative ones, and then use the trained word models to generate sentential descriptions for new video.
no code implementations • 20 Jun 2013 • Daniel Paul Barrett, Jeffrey Mark Siskind
This method makes it possible to detect events which are characterized not by motion, but by the changing state of the people or objects involved.
no code implementations • CVPR 2013 • Yu Cao, Daniel Barrett, Andrei Barbu, Siddharth Narayanaswamy, Haonan Yu, Aaron Michaux, Yuewei Lin, Sven Dickinson, Jeffrey Mark Siskind, Song Wang
In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case.
no code implementations • 20 Nov 2012 • Oleksandr Manzyuk, Barak A. Pearlmutter, Alexey Andreyevich Radul, David R. Rush, Jeffrey Mark Siskind
The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation.
Symbolic Computation Mathematical Software Differential Geometry