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Found 18 papers, 1 papers with code
Object classification from randomized EEG trials
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.
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]
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.
Binomial Checkpointing for Arbitrary Programs with No User Annotation
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade.
Tricks from Deep Learning
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.
DiffSharp: An AD Library for .NET Languages
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.
Collecting and Annotating the Large Continuous Action Dataset
We make available to the community a new dataset to support action-recognition research.
Robot Language Learning, Generation, and Comprehension
We present a unified framework which supports grounding natural-language semantics in robotic driving.
Sentence Directed Video Object Codetection
We tackle the problem of video object codetection by leveraging the weak semantic constraint implied by sentences that describe the video content.
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning.
Video In Sentences Out
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it.
Saying What You're Looking For: Linguistics Meets Video Search
We present an approach to searching large video corpora for video clips which depict a natural-language query in the form of a sentence.
Seeing What You're Told: Sentence-Guided Activity Recognition In Video
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.
Discriminative Training: Learning to Describe Video with Sentences, from Video Described with Sentences
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.
Felzenszwalb-Baum-Welch: Event Detection by Changing Appearance
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.
Recognize Human Activities from Partially Observed Videos
In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case.
Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions
The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation.
Symbolic Computation Mathematical Software Differential Geometry
