[Ml-stat-talks] Fwd: [talks] Colloquium Speaker: Andrej Karpathy Monday April 11, 12:30pm

Barbara Engelhardt bee at princeton.edu
Thu Apr 7 10:33:52 EDT 2016

Talk of interest.

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Colloquium Speaker
Andrej Karpathy, Stanford University
Monday, April 11- 12:30pm
Computer Science 105

Connecting Images and Natural Language

Intelligent agents require the ability to perceive their environments,
understand their high-level semantics, and communicate with humans. While
computer vision has recently made great strides on visual recognition
tasks, the predominant paradigm is to predict one or more fixed visual
categories for each image. I will describe a line of work that
significantly expands the vocabulary of our computer vision systems and
allows them to express visual concepts in natural language, such as “a
picture of a girl playing with a stack of legos”, or “a couple holding
hands and walking on a beach”. In particular, the final model can take an
image and both detect and describe in natural language all of its salient
regions. My modeling techniques draw on recent advances in Deep Learning
that allow us to construct and train neural networks with hundreds of
millions of neurons that take raw images and map them directly to natural
language sentences. I will show that the model generates qualitatively
compelling results and quantitative evaluation and control experiments
demonstrate the strength of this approach with respect to simpler baselines
and previous methods.

Andrej Karpathy is a Computer Science Ph.D. candidate at Stanford
University working with Prof. Fei-Fei Li. He received his M.S. from
University of British Columbia in Computer Science and his B.S from
University of Toronto in Computer Science and Physics. He is interested in
the intersection of computer vision, natural language processing and
reinforcement learning with the aim of developing agents who can
intelligently interact with humans in dynamic environments. His work was
featured in the New York Times and MIT Technology Review. He helped develop
and instruct a new Computer Science class at Stanford on Convolutional
Neural Networks for Visual Recognition. In his spare time he develops Deep
Learning libraries in Javascript and maintains websites that support more
efficient meta-research.
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