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Call for papers (second round)
NIPS’18 Workshop on Modeling and Decision-Making in the Spatiotemporal Domain
at the Thirty-second Conference on Neural Information Processing Systems
Friday December 07, 2018
at the Palais des Congrès de Montréal, Montréal, Canada
Website: https://nips.cc/Conferences/2018/Schedule?showEvent=10930
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1. Call for contributions
We welcome short papers (max 4 pages, excluding references) with theory, direct
applications, or attempts to improve efficiency in existing spatiotemporal
modeling techniques. Submission instructions can be found on
https://sites.google.com/site/nips18spatiotemporal/<https://openreview.net/group?id=NIPS.cc/2018/Workshop/Spatiotemporal>.
Accepted papers will be will be available online on
OpenReview<https://openreview.net/> and presented as contributed talks or
posters.
2. Important dates (second round)
Paper submission deadline: October 19, 2018 11:59 pm AOE
Acceptance notification: November 02, 2018
Camera ready deadline: November 30, 2018 11:59 pm AOE
Workshop: December 07, 2018 8.00-6:30
3. Workshop overview
Understanding the evolution of a process over space and time is fundamental to
a variety of disciplines. Such phenomena that exhibit dynamics in both space
and time include propagation of diseases, evolution of agro-ecosystems
variations in air pollution, dynamics in fluid flows, and patterns in neural
activity. In addition to these fields in which modeling the nonlinear evolution
of a process is the focus, there is also an emerging interest in
decision-making and controlling of autonomous agents in the spatiotemporal
domain. That is, in addition to learning what actions to take, when and where
to take actions is becoming crucial for an agent to successfully interact with
dynamic environments. Although various modeling techniques and conventions are
used in different application domains, the fundamental principles remain
unchanged. Automatically capturing the dependencies between spatial and
temporal components, making accurate predictions into the future, quantifying
the uncertainty associated with predictions, real-time performance, and working
in both big data and data scarce regimes are some of the key aspects that
deserve our attention. Establishing connections between Machine Learning and
Statistics, this workshop aims at:
(1) Raising open questions on challenges of spatiotemporal modeling and
decision-making,
(2) Establishing connections among diverse applications of spatiotemporal
modeling and control, and
(3) Encouraging conversation between theoreticians and practitioners to develop
robust predictive models and decision making engines.
Keywords
Theory: stochastic processes, deep learning/convolutional LSTM, control theory,
Bayesian filtering, kernel methods, evolving Gaussian Processes, chaos theory,
time-frequency analysis, reinforcement learning for dynamic environments,
dynamic policy learning, biostatistics, epidemiology, geostatistics,
climatology, neuroscience, etc.
Applications:
Natural phenomena: disease propagation and outbreaks, agricultural
monitoring and control, environmental monitoring, climate modeling, etc.
Social sciences and economics: predictive policing, population mapping,
poverty mapping, food resources, agriculture, etc.
Engineering/robotics: active data collection, traffic modeling, motion
prediction, fluid dynamics, soft robotics, etc.
Machine Learning/Modeling: Music representation and generation, analysis of
video data, multi-sensor fusion in spatiotemporal domains
Organizers
Ransalu Senanayake, The University of Sydney ([email protected])
Neal Jean, Stanford University ([email protected])
Fabio Ramos, The University of Sydney
Girish Chowdhary, University of Illinois Urbana-Champaign
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