2,162 research outputs found
Hypergraph Neural Networks
In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.Comment: Accepted in AAAI'201
Nested Elimination: A Simple Algorithm for Best-Item Identification from Choice-Based Feedback
We study the problem of best-item identification from choice-based feedback.
In this problem, a company sequentially and adaptively shows display sets to a
population of customers and collects their choices. The objective is to
identify the most preferred item with the least number of samples and at a high
confidence level. We propose an elimination-based algorithm, namely Nested
Elimination (NE), which is inspired by the nested structure implied by the
information-theoretic lower bound. NE is simple in structure, easy to
implement, and has a strong theoretical guarantee for sample complexity.
Specifically, NE utilizes an innovative elimination criterion and circumvents
the need to solve any complex combinatorial optimization problem. We provide an
instance-specific and non-asymptotic bound on the expected sample complexity of
NE. We also show NE achieves high-order worst-case asymptotic optimality.
Finally, numerical experiments from both synthetic and real data corroborate
our theoretical findings.Comment: Accepted to ICML 202
Noise-Resilient Quantum Power Flow
Quantum power flow (QPF) provides inspiring directions for tackling power
flow's computational burdens leveraging quantum computing. However, existing
QPF methods are mainly based on noise-sensitive quantum algorithms, whose
practical utilization is significantly hindered by the limited capability of
today's noisy-intermediate-scale quantum (NISQ) devices. This paper devises a
NISQ-QPF algorithm, which enables power flow calculation on noisy quantum
computers. The main contributions include: (1) a variational quantum circuit
(VQC)-based AC power flow formulation, which enables QPF using short-depth
quantum circuits; (2) noise-resilient QPF solvers based on the variational
quantum linear solver (VQLS) and modified fast decoupled power flow; (3) a
practical NISQ-QPF framework for implementable and reliable power flow analysis
on noisy quantum machines. Promising case studies validate the effectiveness
and accuracy of NISQ-QPF on IBM's real, noisy quantum devices.Comment: 6 pages, 6 figure
MeshNet: Mesh Neural Network for 3D Shape Representation
Mesh is an important and powerful type of data for 3D shapes and widely
studied in the field of computer vision and computer graphics. Regarding the
task of 3D shape representation, there have been extensive research efforts
concentrating on how to represent 3D shapes well using volumetric grid,
multi-view and point cloud. However, there is little effort on using mesh data
in recent years, due to the complexity and irregularity of mesh data. In this
paper, we propose a mesh neural network, named MeshNet, to learn 3D shape
representation from mesh data. In this method, face-unit and feature splitting
are introduced, and a general architecture with available and effective blocks
are proposed. In this way, MeshNet is able to solve the complexity and
irregularity problem of mesh and conduct 3D shape representation well. We have
applied the proposed MeshNet method in the applications of 3D shape
classification and retrieval. Experimental results and comparisons with the
state-of-the-art methods demonstrate that the proposed MeshNet can achieve
satisfying 3D shape classification and retrieval performance, which indicates
the effectiveness of the proposed method on 3D shape representation
DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation
Distributed statistical learning has become a popular technique for
large-scale data analysis. Most existing work in this area focuses on dividing
the observations, but we propose a new algorithm, DDAC-SpAM, which divides the
features under a high-dimensional sparse additive model. Our approach involves
three steps: divide, decorrelate, and conquer. The decorrelation operation
enables each local estimator to recover the sparsity pattern for each additive
component without imposing strict constraints on the correlation structure
among variables. The effectiveness and efficiency of the proposed algorithm are
demonstrated through theoretical analysis and empirical results on both
synthetic and real data. The theoretical results include both the consistent
sparsity pattern recovery as well as statistical inference for each additive
functional component. Our approach provides a practical solution for fitting
sparse additive models, with promising applications in a wide range of domains.Comment: 52 pages, 3 figure
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