24,762 research outputs found
Temporal difference learning with interpolated table value functions
This paper introduces a novel function approximation architecture especially well suited to temporal difference learning. The architecture is based on using sets of interpolated table look-up functions. These offer rapid and stable learning, and are efficient when the number of inputs is small. An empirical investigation is conducted to test their performance on a supervised learning task, and on themountain car problem, a standard reinforcement learning benchmark. In each case, the interpolated table functions offer competitive performance. ©2009 IEEE
Video Frame Interpolation via Adaptive Separable Convolution
Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-sampling simultaneously. These methods require large kernels
to handle large motion, which limits the number of pixels whose kernels can be
estimated at once due to the large memory demand. To address this problem, this
paper formulates frame interpolation as local separable convolution over input
frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D
kernels require significantly fewer parameters to be estimated. Our method
develops a deep fully convolutional neural network that takes two input frames
and estimates pairs of 1D kernels for all pixels simultaneously. Since our
method is able to estimate kernels and synthesizes the whole video frame at
once, it allows for the incorporation of perceptual loss to train the neural
network to produce visually pleasing frames. This deep neural network is
trained end-to-end using widely available video data without any human
annotation. Both qualitative and quantitative experiments show that our method
provides a practical solution to high-quality video frame interpolation.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
Neural network interpolation of the magnetic field for the LISA Pathfinder Diagnostics Subsystem
LISA Pathfinder is a science and technology demonstrator of the European
Space Agency within the framework of its LISA mission, which aims to be the
first space-borne gravitational wave observatory. The payload of LISA
Pathfinder is the so-called LISA Technology Package, which is designed to
measure relative accelerations between two test masses in nominal free fall.
Its disturbances are monitored and dealt by the diagnostics subsystem. This
subsystem consists of several modules, and one of these is the magnetic
diagnostics system, which includes a set of four tri-axial fluxgate
magnetometers, intended to measure with high precision the magnetic field at
the positions of the test masses. However, since the magnetometers are located
far from the positions of the test masses, the magnetic field at their
positions must be interpolated. It has been recently shown that because there
are not enough magnetic channels, classical interpolation methods fail to
derive reliable measurements at the positions of the test masses, while neural
network interpolation can provide the required measurements at the desired
accuracy. In this paper we expand these studies and we assess the reliability
and robustness of the neural network interpolation scheme for variations of the
locations and possible offsets of the magnetometers, as well as for changes in
environmental conditions. We find that neural networks are robust enough to
derive accurate measurements of the magnetic field at the positions of the test
masses in most circumstances
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