8,612 research outputs found
Semantic Graph Convolutional Networks for 3D Human Pose Regression
In this paper, we study the problem of learning Graph Convolutional Networks
(GCNs) for regression. Current architectures of GCNs are limited to the small
receptive field of convolution filters and shared transformation matrix for
each node. To address these limitations, we propose Semantic Graph
Convolutional Networks (SemGCN), a novel neural network architecture that
operates on regression tasks with graph-structured data. SemGCN learns to
capture semantic information such as local and global node relationships, which
is not explicitly represented in the graph. These semantic relationships can be
learned through end-to-end training from the ground truth without additional
supervision or hand-crafted rules. We further investigate applying SemGCN to 3D
human pose regression. Our formulation is intuitive and sufficient since both
2D and 3D human poses can be represented as a structured graph encoding the
relationships between joints in the skeleton of a human body. We carry out
comprehensive studies to validate our method. The results prove that SemGCN
outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code
can be found at https://github.com/garyzhao/SemGC
Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models
This paper studies the impact of bootstrap procedure on the eigenvalue
distributions of the sample covariance matrix under the high-dimensional factor
structure. We provide asymptotic distributions for the top eigenvalues of
bootstrapped sample covariance matrix under mild conditions. After bootstrap,
the spiked eigenvalues which are driven by common factors will converge weakly
to Gaussian limits via proper scaling and centralization. However, the largest
non-spiked eigenvalue is mainly determined by order statistics of bootstrap
resampling weights, and follows extreme value distribution. Based on the
disparate behavior of the spiked and non-spiked eigenvalues, we propose
innovative methods to test the number of common factors. According to the
simulations and a real data example, the proposed methods are the only ones
performing reliably and convincingly under the existence of both weak factors
and cross-sectionally correlated errors. Our technical details contribute to
random matrix theory on spiked covariance model with convexly decaying density
and unbounded support, or with general elliptical distributions.Comment: 95 pages, 9 figures, 4 table
Optimal Power Flow in Hybrid AC and Multi-terminal HVDC Networks with Offshore Wind Farm Integration Based on Semidefinite Programming
Multi-terminal high voltage direct current (MTHVDC) technology is a promising
technology for the offshore wind farm integration, which requires the new
control and operation scheme. Therefore, the optimal power flow problem for
this system is important to achieve the optimal economic operation. In this
paper, convex relaxation model based on semidefinite programming for the
MT-HVDC system considering DC/DC converters is proposed to solve the optimal
power flow problem. A hybrid AC and MT-HVDC system for offshore wind farm
integration is used for the test. The simulation results validate the
effectiveness of the proposed model and guarantee that the global optimum
solution is achieved.Comment: Accepted in IEEE/PES ISGT Asia 2019 conference (May, 2019), Chengdu,
Chin
Learning to Learn Single Domain Generalization
We are concerned with a worst-case scenario in model generalization, in the
sense that a model aims to perform well on many unseen domains while there is
only one single domain available for training. We propose a new method named
adversarial domain augmentation to solve this Out-of-Distribution (OOD)
generalization problem. The key idea is to leverage adversarial training to
create "fictitious" yet "challenging" populations, from which a model can learn
to generalize with theoretical guarantees. To facilitate fast and desirable
domain augmentation, we cast the model training in a meta-learning scheme and
use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case
constraint. Detailed theoretical analysis is provided to testify our
formulation, while extensive experiments on multiple benchmark datasets
indicate its superior performance in tackling single domain generalization.Comment: In CVPR 2020 (13 pages including supplementary material). The source
code and pre-trained models are publicly available at:
https://github.com/joffery/M-AD
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