2,095 research outputs found
Towards responsive Sensitive Artificial Listeners
This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness
Learning to Infer Graphics Programs from Hand-Drawn Images
We introduce a model that learns to convert simple hand drawings into
graphics programs written in a subset of \LaTeX. The model combines techniques
from deep learning and program synthesis. We learn a convolutional neural
network that proposes plausible drawing primitives that explain an image. These
drawing primitives are like a trace of the set of primitive commands issued by
a graphics program. We learn a model that uses program synthesis techniques to
recover a graphics program from that trace. These programs have constructs like
variable bindings, iterative loops, or simple kinds of conditionals. With a
graphics program in hand, we can correct errors made by the deep network,
measure similarity between drawings by use of similar high-level geometric
structures, and extrapolate drawings. Taken together these results are a step
towards agents that induce useful, human-readable programs from perceptual
input
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