197,376 research outputs found
Interaction-tuned Anderson versus Mott localization
Disorder or sufficiently strong interactions can render a metallic state
unstable causing it to turn into an insulating one. Despite the fact that the
interplay of these two routes to a vanishing conductivity has been a central
research topic, a unifying picture has not emerged so far. Here, we establish
that the two-dimensional Falicov-Kimball model, one of the simplest lattice
models of strong electron correlation does allow for the study of this
interplay. In particular, we show that this model at particle-hole symmetry
possesses three distinct thermodynamic insulating phases and exhibits Anderson
localization. The previously reported metallic phase is identified as a
finite-size feature due to the presence of weak localization. We characterize
these phases by their electronic density of states, staggered occupation,
conductivity, and the generalized inverse participation ratio. The implications
of our findings for other strongly correlated systems are discussed.Comment: 5 pages, 4 figure
Efficient Correlated Topic Modeling with Topic Embedding
Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval.Comment: KDD 2017 oral. The first two authors contributed equall
Exactly Solvable Models of Strongly Correlated Electrons
This is a reprint volume devoted to exact solutions of models of strongly
correlated electrons in one spatial dimension by means of the Bethe Ansatz.Comment: Editors 490 pages, World Scientific, 1994, ISBN 981-02-1534-
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.Comment: CIKM 2018 Full Pape
The Discrete Infinite Logistic Normal Distribution
We present the discrete infinite logistic normal distribution (DILN), a
Bayesian nonparametric prior for mixed membership models. DILN is a
generalization of the hierarchical Dirichlet process (HDP) that models
correlation structure between the weights of the atoms at the group level. We
derive a representation of DILN as a normalized collection of gamma-distributed
random variables, and study its statistical properties. We consider
applications to topic modeling and derive a variational inference algorithm for
approximate posterior inference. We study the empirical performance of the DILN
topic model on four corpora, comparing performance with the HDP and the
correlated topic model (CTM). To deal with large-scale data sets, we also
develop an online inference algorithm for DILN and compare with online HDP and
online LDA on the Nature magazine, which contains approximately 350,000
articles.Comment: This paper will appear in Bayesian Analysis. A shorter version of
this paper appeared at AISTATS 2011, Fort Lauderdale, FL, US
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