5,177 research outputs found
Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres
Many computer vision challenges require continuous outputs, but tend to be
solved by discrete classification. The reason is classification's natural
containment within a probability -simplex, as defined by the popular softmax
activation function. Regular regression lacks such a closed geometry, leading
to unstable training and convergence to suboptimal local minima. Starting from
this insight we revisit regression in convolutional neural networks. We observe
many continuous output problems in computer vision are naturally contained in
closed geometrical manifolds, like the Euler angles in viewpoint estimation or
the normals in surface normal estimation. A natural framework for posing such
continuous output problems are -spheres, which are naturally closed
geometric manifolds defined in the space. By introducing a
spherical exponential mapping on -spheres at the regression output, we
obtain well-behaved gradients, leading to stable training. We show how our
spherical regression can be utilized for several computer vision challenges,
specifically viewpoint estimation, surface normal estimation and 3D rotation
estimation. For all these problems our experiments demonstrate the benefit of
spherical regression. All paper resources are available at
https://github.com/leoshine/Spherical_Regression.Comment: CVPR 2019 camera read
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Event sequence, asynchronously generated with random timestamp, is ubiquitous
among applications. The precise and arbitrary timestamp can carry important
clues about the underlying dynamics, and has lent the event data fundamentally
different from the time-series whereby series is indexed with fixed and equal
time interval. One expressive mathematical tool for modeling event is point
process. The intensity functions of many point processes involve two
components: the background and the effect by the history. Due to its inherent
spontaneousness, the background can be treated as a time series while the other
need to handle the history events. In this paper, we model the background by a
Recurrent Neural Network (RNN) with its units aligned with time series indexes
while the history effect is modeled by another RNN whose units are aligned with
asynchronous events to capture the long-range dynamics. The whole model with
event type and timestamp prediction output layers can be trained end-to-end.
Our approach takes an RNN perspective to point process, and models its
background and history effect. For utility, our method allows a black-box
treatment for modeling the intensity which is often a pre-defined parametric
form in point processes. Meanwhile end-to-end training opens the venue for
reusing existing rich techniques in deep network for point process modeling. We
apply our model to the predictive maintenance problem using a log dataset by
more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence
(AAAI17
Origin of the multiferroic spiral spin-order in the RMnO3 perovskites
The origin of the spiral spin-order in perovskite multiferroic manganites
MnO ( Tb or Dy) is here investigated using a two -orbitals double-exchange model. Our main result is that the experimentally
observed spiral phase can be stabilized by introducing a relatively weak
next-nearest-neighbor superexchange coupling ( of the nearest-neighbor
superexchange). Moreover, the Jahn-Teller lattice distortion is also shown to
be essential to obtain a realistic spiral period. Supporting our conclusions,
the generic phase diagram of undoped perovskite manganites is obtained using
Monte Carlo simulations, showing phase transitions from the A-type
antiferromagnet, to the spiral phase, and finally to the E-type
antiferromagnet, with decreasing size of the ions. These results are
qualitatively explained by the enhanced relative intensity of the
superexchanges.Comment: 6 pages, 4 figure
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