165,545 research outputs found
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of the proposed model are jointly trained to maximize the
conditional probability of a target sequence given a source sequence. The
performance of a statistical machine translation system is empirically found to
improve by using the conditional probabilities of phrase pairs computed by the
RNN Encoder-Decoder as an additional feature in the existing log-linear model.
Qualitatively, we show that the proposed model learns a semantically and
syntactically meaningful representation of linguistic phrases.Comment: EMNLP 201
A statistical physics perspective on criticality in financial markets
Stock markets are complex systems exhibiting collective phenomena and
particular features such as synchronization, fluctuations distributed as
power-laws, non-random structures and similarity to neural networks. Such
specific properties suggest that markets operate at a very special point.
Financial markets are believed to be critical by analogy to physical systems
but few statistically founded evidence have been given. Through a data-based
methodology and comparison to simulations inspired by statistical physics of
complex systems, we show that the Dow Jones and indices sets are not rigorously
critical. However, financial systems are closer to the criticality in the crash
neighborhood.Comment: 23 pages, 19 figure
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Marchenko-Pastur law with relaxed independence conditions
We prove the Marchenko-Pastur law for the eigenvalues of sample
covariance matrices in two new situations where the data does not have
independent coordinates. In the first scenario - the block-independent model -
the coordinates of the data are partitioned into blocks in such a way that
the entries in different blocks are independent, but the entries from the same
block may be dependent. In the second scenario - the random tensor model - the
data is the homogeneous random tensor of order , i.e. the coordinates of the
data are all different products of variables chosen from a
set of independent random variables. We show that Marchenko-Pastur law
holds for the block-independent model as long as the size of the largest block
is and for the random tensor model as long as . Our main
technical tools are new concentration inequalities for quadratic forms in
random variables with block-independent coordinates, and for random tensors
A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users
The packet loss characteristics of Internet paths that include residential broadband links are not well understood, and there are no good models for their behaviour. This compli- cates the design of real-time video applications targeting home users, since it is difficult to choose appropriate error correction and concealment algorithms without a good model for the types of loss observed. Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment. We present a new two-level Markov model for packet loss that can more accurately describe the characteristics of these links, and quantify the effectiveness of this model. We demonstrate that our new packet loss model allows for improved application design, by using it to model the performance of forward error correction on such links
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Bayesian nonparametric sparse VAR models
High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a
new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional
VAR models that can improve estimation efficiency and prediction accuracy. Our
hierarchical prior overcomes overparametrization and overfitting issues by
clustering the VAR coefficients into groups and by shrinking the coefficients
of each group toward a common location. Clustering and shrinking effects
induced by the BNP-Lasso prior are well suited for the extraction of causal
networks from time series, since they account for some stylized facts in
real-world networks, which are sparsity, communities structures and
heterogeneity in the edges intensity. In order to fully capture the richness of
the data and to achieve a better understanding of financial and macroeconomic
risk, it is therefore crucial that the model used to extract network accounts
for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the
paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ----
Supplementary Material available on reques
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