12,062 research outputs found
A Perturbative Density Matrix Renormalization Group Algorithm for Large Active Spaces
We describe a low cost alternative to the standard variational DMRG (density
matrix renormalization group) algorithm that is analogous to the combination of
selected configuration interaction plus perturbation theory (SCI+PT). We denote
the resulting method p-DMRG (perturbative DMRG) to distinguish it from the
standard variational DMRG. p-DMRG is expected to be useful for systems with
very large active spaces, for which variational DMRG becomes too expensive.
Similar to SCI+PT, in p-DMRG a zeroth-order wavefunction is first obtained by a
standard DMRG calculation, but with a small bond dimension. Then, the residual
correlation is recovered by a second-order perturbative treatment. We discuss
the choice of partitioning for the perturbation theory, which is crucial for
its accuracy and robustness. To circumvent the problem of a large bond
dimension in the first-order wavefunction, we use a sum of matrix product
states (MPS) to expand the first-order wavefunction, yielding substantial
savings in computational cost and memory. We also propose extrapolation schemes
to reduce the errors in the zeroth- and first-order wavefunctions. Numerical
results for Cr 2 with a (28e,76o) active space and 1,3-butadiene with a
(22e,82o) active space reveal that p-DMRG provides ground state energies of a
similar quality to variational DMRG with very large bond dimensions, but at a
significantly lower computational cost. This suggests that p-DMRG will be an
efficient tool for benchmark studies in the future
Wavelet Integrated CNNs for Noise-Robust Image Classification
Convolutional Neural Networks (CNNs) are generally prone to noise
interruptions, i.e., small image noise can cause drastic changes in the output.
To suppress the noise effect to the final predication, we enhance CNNs by
replacing max-pooling, strided-convolution, and average-pooling with Discrete
Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers
applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and
design wavelet integrated CNNs (WaveCNets) using these layers for image
classification. In WaveCNets, feature maps are decomposed into the
low-frequency and high-frequency components during the down-sampling. The
low-frequency component stores main information including the basic object
structures, which is transmitted into the subsequent layers to extract robust
high-level features. The high-frequency components, containing most of the data
noise, are dropped during inference to improve the noise-robustness of the
WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy
version of ImageNet) show that WaveCNets, the wavelet integrated versions of
VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness
than their vanilla versions.Comment: CVPR accepted pape
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized
to accomplish similarity search in large-scale multimedia retrieval
applications. Particularly supervised hashing has recently received
considerable research attention by leveraging the label information to preserve
the pairwise similarities of data points in the Hamming space. However, there
still remain two crucial bottlenecks: 1) the learning process of the full
pairwise similarity preservation is computationally unaffordable and unscalable
to deal with big data; 2) the available category information of data are not
well-explored to learn discriminative hash functions. To overcome these
challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH)
framework, which aims to directly embed the transformed semantic information
into the asymmetric similarity approximation and discriminative hashing
function learning. Specifically, a semantic-aware latent embedding is
introduced to asymmetrically preserve the full pairwise similarities while
skillfully handle the cumbersome n times n pairwise similarity matrix.
Meanwhile, a semantic-aware autoencoder is developed to jointly preserve the
data structures in the discriminative latent semantic space and perform data
reconstruction. Moreover, an efficient alternating optimization algorithm is
proposed to solve the resulting discrete optimization problem. Extensive
experimental results on multiple large-scale datasets demonstrate that our
SADIH can clearly outperform the state-of-the-art baselines with the additional
benefit of lower computational costs.Comment: Accepted by The Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
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