514 research outputs found
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
We propose a novel class of time-varying nonparanormal graphical models,
which allows us to model high dimensional heavy-tailed systems and the
evolution of their latent network structures. Under this model, we develop
statistical tests for presence of edges both locally at a fixed index value and
globally over a range of values. The tests are developed for a high-dimensional
regime, are robust to model selection mistakes and do not require commonly
assumed minimum signal strength. The testing procedures are based on a high
dimensional, debiasing-free moment estimator, which uses a novel kernel
smoothed Kendall's tau correlation matrix as an input statistic. The estimator
consistently estimates the latent inverse Pearson correlation matrix uniformly
in both the index variable and kernel bandwidth. Its rate of convergence is
shown to be minimax optimal. Our method is supported by thorough numerical
simulations and an application to a neural imaging data set
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods
Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor
Truncated Power (TTP) method, that incorporates variable selection into the
estimation of decomposition components. The sparsity is achieved via an
efficient truncation step embedded in the tensor power iteration. Our method
applies to a broad family of high dimensional latent variable models, including
high dimensional Gaussian mixture and mixtures of sparse regressions. A
thorough theoretical investigation is further conducted. In particular, we show
that the final decomposition estimator is guaranteed to achieve a local
statistical rate, and further strengthen it to the global statistical rate by
introducing a proper initialization procedure. In high dimensional regimes, the
obtained statistical rate significantly improves those shown in the existing
non-sparse decomposition methods. The empirical advantages of TTP are confirmed
in extensive simulated results and two real applications of click-through rate
prediction and high-dimensional gene clustering.Comment: To Appear in JRSS-
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