330,754 research outputs found
Non-Asymptotic Uniform Rates of Consistency for k-NN Regression
We derive high-probability finite-sample uniform rates of consistency for
-NN regression that are optimal up to logarithmic factors under mild
assumptions. We moreover show that -NN regression adapts to an unknown lower
intrinsic dimension automatically. We then apply the -NN regression rates to
establish new results about estimating the level sets and global maxima of a
function from noisy observations.Comment: In Proceedings of 33rd AAAI Conference on Artificial Intelligence
(AAAI 2019
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw
sensory data using only a single neural network for value optimization. There
are several challenges in the current state-of-the-art reinforcement learning
algorithms that prevent them from converging towards the global optima. It is
likely that the solution to these problems lies in short- and long-term
planning, exploration and memory management for reinforcement learning
algorithms. Games are often used to benchmark reinforcement learning algorithms
as they provide a flexible, reproducible, and easy to control environment.
Regardless, few games feature a state-space where results in exploration,
memory, and planning are easily perceived. This paper presents The Dreaming
Variational Autoencoder (DVAE), a neural network based generative modeling
architecture for exploration in environments with sparse feedback. We further
present Deep Maze, a novel and flexible maze engine that challenges DVAE in
partial and fully-observable state-spaces, long-horizon tasks, and
deterministic and stochastic problems. We show initial findings and encourage
further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International
Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial
Intelligence XXXV, 201
Providing location everywhere
Anacleto R., Figueiredo L., Novais P., Almeida A., Providing Location Everywhere, in Progress in Artificial Intelligence, Antunes L., Sofia Pinto H. (eds), Lecture Notes in Artificial Intelligence 7026, Springer-Verlag, ISBN 978-3-540-24768-2, (Proceedings of the 15th Portuguese conference on Artificial Intelligence - EPIA 2011, Lisboa, Portugal), pp 15-28, 2011.The ability to locate an individual is an essential part of many applications, specially the mobile ones. Obtaining this location
in an open environment is relatively simple through GPS (Global Positioning System), but indoors or even in dense environments this type of
location system doesn’t provide a good accuracy. There are already systems that try to suppress these limitations, but most of them need the
existence of a structured environment to work. Since Inertial Navigation Systems (INS) try to suppress the need of a structured environment we
propose an INS based on Micro Electrical Mechanical Systems (MEMS) that is capable of, in real time, compute the position of an individual everywhere
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