239 citations · 434 across the 32 of their papers we have counts for
5 papers · 1 filter
DiffSharp: Automatic Differentiation Library
Atilim Gunes Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
In this paper we introduce DiffSharp, an automatic differentiation (AD) library designed with machine learning in mind. AD is a family of techniques that evaluate derivatives at ma…
Collecting and Annotating the Large Continuous Action Dataset
Daniel Paul Barrett, Ran Xu, Haonan Yu +1
We make available to the community a new dataset to support action-recognition research. This dataset is different from prior datasets in several key ways. It is significantly larg…
Robot Language Learning, Generation, and Comprehension
Daniel Paul Barrett, Scott Alan Bronikowski, Haonan Yu +1
We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and…
The Compositional Nature of Event Representations in the Human Brain
Andrei Barbu, N. Siddharth, Caiming Xiong +10
How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? Subjects viewed videos during neuroimaging (fMRI) session…
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul +1
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "au…