pwc-archive/papers-with-abstracts huggingface dataset:
Probability Distinguishes Different Types of Conditional Statements
Authors: Joseph W. Norman
Date: 2013-07-15 00:00:00

The language of probability is used to define several different types of
conditional statements. There are four principal types: subjunctive, material,
existential, and feasibility. Two further types of conditionals are defined
using the propositional calculus and Boole's mathematical logic:
truth-functional and Boolean feasibility (which turn out to be special cases of
probabilistic conditionals). Each probabilistic conditional is quantified by a
fractional parameter between zero and one that says whether it is purely
affirmative, purely negative, or intermediate in its sense. Conditionals can be
specialized further by their content to express factuality and
counterfactuality, and revised or reformulated to account for exceptions and
confounding factors. The various conditionals have distinct mathematical
representations: through intermediate probability expressions and logical
formulas, each conditional is eventually translated into a set of polynomial
equations and inequalities (with real coefficients). The polynomial systems
from different types of conditionals exhibit different patterns of behavior,
concerning for example opposing conditionals or false antecedents. Interesting
results can be computed from the relevant polynomial systems using well-known
methods from algebra and computer science. Among other benefits, the proposed
framework of analysis offers paraconsistent procedures for logical deduction
that produce such familiar results as modus ponens, transitivity, disjunction
introduction, and disjunctive syllogism; all while avoiding any explosion of
consequences from inconsistent premises. Several example problems from Goodman
and Adams are analyzed. A new perspective called polylogicism is presented:
mathematical logic that respects the diversity among conditionals in particular
and logic problems in general.
Autoencoder Trees
Authors: Ozan İrsoy, Ethem Alpaydin
Date: 2014-09-26 00:00:00
Tasks: Decoder

We discuss an autoencoder model in which the encoding and decoding functions
are implemented by decision trees. We use the soft decision tree where internal
nodes realize soft multivariate splits given by a gating function and the
overall output is the average of all leaves weighted by the gating values on
their path. The encoder tree takes the input and generates a lower dimensional
representation in the leaves and the decoder tree takes this and reconstructs
the original input. Exploiting the continuity of the trees, autoencoder trees
are trained with stochastic gradient descent. On handwritten digit and news
data, we see that the autoencoder trees yield good reconstruction error
compared to traditional autoencoder perceptrons. We also see that the
autoencoder tree captures hierarchical representations at different
granularities of the data on its different levels and the leaves capture the
localities in the input space.
Beyond Maximum Likelihood: from Theory to Practice
Authors: Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman
Date: 2014-09-26 00:00:00

Maximum likelihood is the most widely used statistical estimation technique.
Recent work by the authors introduced a general methodology for the
construction of estimators for functionals in parametric models, and
demonstrated improvements - both in theory and in practice - over the maximum
likelihood estimator (MLE), particularly in high dimensional scenarios
involving parameter dimension comparable to or larger than the number of
samples. This approach to estimation, building on results from approximation
theory, is shown to yield minimax rate-optimal estimators for a wide class of
functionals, implementable with modest computational requirements. In a
nutshell, a message of this recent work is that, for a wide class of
functionals, the performance of these essentially optimal estimators with $n$
samples is comparable to that of the MLE with $n \ln n$ samples.
  In the present paper, we highlight the applicability of the aforementioned
methodology to statistical problems beyond functional estimation, and show that
it can yield substantial gains. For example, we demonstrate that for learning
tree-structured graphical models, our approach achieves a significant reduction
of the required data size compared with the classical Chow--Liu algorithm,
which is an implementation of the MLE, to achieve the same accuracy. The key
step in improving the Chow--Liu algorithm is to replace the empirical mutual
information with the estimator for mutual information proposed by the authors.
Further, applying the same replacement approach to classical Bayesian network
classification, the resulting classifiers uniformly outperform the previous
classifiers on 26 widely used datasets.
Two-stage Geometric Information Guided Image Reconstruction
Authors: Jing Qin, Weihong Guo
Date: 2014-09-26 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Vocal Bursts Valence Prediction

In compressive sensing, it is challenging to reconstruct image of high quality from very few noisy linear projections. Existing methods mostly work well on piecewise constant images but not so well on piecewise smooth images such as natural images, medical images that contain a lot of details. We propose a two-stage method called GeoCS to recover images with rich geometric information from very limited amount of noisy measurements. The method adopts the shearlet transform that is mathematically proven to be optimal in sparsely representing images containing anisotropic features such as edges, corners, spikes etc. It also uses the weighted total variation (TV) sparsity with spatially variant weights to preserve sharp edges but to reduce the staircase effects of TV. Geometric information extracted from the results of stage I serves as an initial prior for stage II which alternates image reconstruction and geometric information update in a mutually beneficial way. GeoCS has been tested on incomplete spectral Fourier samples. It is applicable to other types of measurements as well. Experimental results on various complicated images show that GeoCS is efficient and generates high-quality images.
Community Detection in Sparse Random Networks
Authors: Ery Arias-Castro, Nicolas Verzelen
Date: 2013-08-13 00:00:00
Tasks: Community Detection

We consider the problem of detecting a tight community in a sparse random
network. This is formalized as testing for the existence of a dense random
subgraph in a random graph. Under the null hypothesis, the graph is a
realization of an Erd\"os-R\'enyi graph on $N$ vertices and with connection
probability $p_0$; under the alternative, there is an unknown subgraph on $n$
vertices where the connection probability is p1 > p0. In Arias-Castro and
Verzelen (2012), we focused on the asymptotically dense regime where p0 is
large enough that np0>(n/N)^{o(1)}. We consider here the asymptotically sparse
regime where p0 is small enough that np0<(n/N)^{c0} for some c0>0. As before,
we derive information theoretic lower bounds, and also establish the
performance of various tests. Compared to our previous work, the arguments for
the lower bounds are based on the same technology, but are substantially more
technical in the details; also, the methods we study are different: besides a
variant of the scan statistic, we study other statistics such as the size of
the largest connected component, the number of triangles, the eigengap of the
adjacency matrix, etc. Our detection bounds are sharp, except in the Poisson
regime where we were not able to fully characterize the constant arising in the
bound.
A Deep Graph Embedding Network Model for Face Recognition
Authors: Yufei Gan, Teng Yang, Chu He
Date: 2014-09-25 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, General Classification, Graph Embedding

In this paper, we propose a new deep learning network "GENet", it combines the multi-layer network architec- ture and graph embedding framework. Firstly, we use simplest unsupervised learning PCA/LDA as first layer to generate the low- level feature. Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. The experiments indicate that higher classification accuracy can be obtained by this algorithm on the CMU-PIE, ORL, Extended Yale B dataset.
Image Classification with A Deep Network Model based on Compressive Sensing
Authors: Yufei Gan, Tong Zhuo, Chu He
Date: 2014-09-25 00:00:00
Tasks: Classification, Compressive Sensing, General Classification, image-classification, Image Classification

To simplify the parameter of the deep learning network, a cascaded
compressive sensing model "CSNet" is implemented for image classification.
Firstly, we use cascaded compressive sensing network to learn feature from the
data. Secondly, CSNet generates the feature by binary hashing and block-wise
histograms. Finally, a linear SVM classifier is used to classify these
features. The experiments on the MNIST dataset indicate that higher
classification accuracy can be obtained by this algorithm.
Performance of Stanford and Minipar Parser on Biomedical Texts
Authors: Rushdi Shams
Date: 2014-09-25 00:00:00

In this paper, the performance of two dependency parsers, namely Stanford and
Minipar, on biomedical texts has been reported. The performance of te parsers
to assignm dependencies between two biomedical concepts that are already proved
to be connected is not satisfying. Both Stanford and Minipar, being statistical
parsers, fail to assign dependency relation between two connected concepts if
they are distant by at least one clause. Minipar's performance, in terms of
precision, recall and the F-score of the attachment score (e.g., correctly
identified head in a dependency), to parse biomedical text is also measured
taking the Stanford's as a gold standard. The results suggest that Minipar is
not suitable yet to parse biomedical texts. In addition, a qualitative
investigation reveals that the difference between working principles of the
parsers also play a vital role for Minipar's degraded performance.
Semi-supervised Classification for Natural Language Processing
Authors: Rushdi Shams
Date: 2014-09-25 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Semi-supervised classification is an interesting idea where classification
models are learned from both labeled and unlabeled data. It has several
advantages over supervised classification in natural language processing
domain. For instance, supervised classification exploits only labeled data that
are expensive, often difficult to get, inadequate in quantity, and require
human experts for annotation. On the other hand, unlabeled data are inexpensive
and abundant. Despite the fact that many factors limit the wide-spread use of
semi-supervised classification, it has become popular since its level of
performance is empirically as good as supervised classification. This study
explores the possibilities and achievements as well as complexity and
limitations of semi-supervised classification for several natural langue
processing tasks like parsing, biomedical information processing, text
classification, and summarization.
Identification of jump Markov linear models using particle filters
Authors: Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Date: 2014-09-25 00:00:00
Tasks: State Space Models

Jump Markov linear models consists of a finite number of linear state space
models and a discrete variable encoding the jumps (or switches) between the
different linear models. Identifying jump Markov linear models makes for a
challenging problem lacking an analytical solution. We derive a new expectation
maximization (EM) type algorithm that produce maximum likelihood estimates of
the model parameters. Our development hinges upon recent progress in combining
particle filters with Markov chain Monte Carlo methods in solving the nonlinear
state smoothing problem inherent in the EM formulation. Key to our development
is that we exploit a conditionally linear Gaussian substructure in the model,
allowing for an efficient algorithm.
Causal Graph Justifications of Logic Programs
Authors: Pedro Cabalar, Jorge Fandinno, Michael Fink
Date: 2014-09-25 00:00:00
Tasks: Negation

In this work we propose a multi-valued extension of logic programs under the
stable models semantics where each true atom in a model is associated with a
set of justifications. These justifications are expressed in terms of causal
graphs formed by rule labels and edges that represent their application
ordering. For positive programs, we show that the causal justifications
obtained for a given atom have a direct correspon- dence to (relevant)
syntactic proofs of that atom using the program rules involved in the graphs.
The most interesting contribution is that this causal information is obtained
in a purely semantic way, by algebraic op- erations (product, sum and
application) on a lattice of causal values whose ordering relation expresses
when a justification is stronger than another. Finally, for programs with
negation, we define the concept of causal stable model by introducing an
analogous transformation to Gelfond and Lifschitz's program reduct. As a
result, default negation behaves as "absence of proof" and no justification is
derived from negative liter
Ctrax extensions for tracking in difficult lighting conditions
Authors: Ulrich Stern, Chung-Hui Yang
Date: 2014-09-25 00:00:00

The fly tracking software Ctrax by Branson et al. is popular for positional
tracking of animals both within and beyond the fly community. Ctrax was not
designed to handle tracking in difficult lighting conditions with strong
shadows or recurring "on"/"off" changes in lighting - a condition that will
likely become increasingly common due to the advent of red-shifted
channelrhodopsin. We describe Ctrax extensions we developed that address this
problem. The extensions enabled good tracking accuracy in three types of
difficult lighting conditions in our lab. Our technique handling shadows relies
on "single animal tracking"; the other techniques should be widely applicable.
Generating Conceptual Metaphors from Proposition Stores
Authors: Ekaterina Ovchinnikova, Vladimir Zaytsev, Suzanne Wertheim, Ross Israel
Date: 2014-09-25 00:00:00

Contemporary research on computational processing of linguistic metaphors is
divided into two main branches: metaphor recognition and metaphor
interpretation. We take a different line of research and present an automated
method for generating conceptual metaphors from linguistic data. Given the
generated conceptual metaphors, we find corresponding linguistic metaphors in
corpora. In this paper, we describe our approach and its evaluation using
English and Russian data.
The risks of mixing dependency lengths from sequences of different length
Authors: Ramon Ferrer-i-Cancho, Haitao Liu
Date: 2013-04-13 00:00:00
Tasks: Sentence

Mixing dependency lengths from sequences of different length is a common
practice in language research. However, the empirical distribution of
dependency lengths of sentences of the same length differs from that of
sentences of varying length and the distribution of dependency lengths depends
on sentence length for real sentences and also under the null hypothesis that
dependencies connect vertices located in random positions of the sequence. This
suggests that certain results, such as the distribution of syntactic dependency
lengths mixing dependencies from sentences of varying length, could be a mere
consequence of that mixing. Furthermore, differences in the global averages of
dependency length (mixing lengths from sentences of varying length) for two
different languages do not simply imply a priori that one language optimizes
dependency lengths better than the other because those differences could be due
to differences in the distribution of sentence lengths and other factors.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Authors: Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li
Date: 2014-09-25 00:00:00
Tasks: Information Retrieval, Metric Learning, Retrieval

The problem of software artifact retrieval has the goal to effectively locate
software artifacts, such as a piece of source code, in a large code repository.
This problem has been traditionally addressed through the textual query. In
other words, information retrieval techniques will be exploited based on the
textual similarity between queries and textual representation of software
artifacts, which is generated by collecting words from comments, identifiers,
and descriptions of programs. However, in addition to these semantic
information, there are rich information embedded in source codes themselves.
These source codes, if analyzed properly, can be a rich source for enhancing
the efforts of software artifact retrieval. To this end, in this paper, we
develop a feature extraction method on source codes. Specifically, this method
can capture both the inherent information in the source codes and the semantic
information hidden in the comments, descriptions, and identifiers of the source
codes. Moreover, we design a heterogeneous metric learning approach, which
allows to integrate code features and text features into the same latent
semantic space. This, in turn, can help to measure the artifact similarity by
exploiting the joint power of both code and text features. Finally, extensive
experiments on real-world data show that the proposed method can help to
improve the performances of software artifact retrieval with a significant
margin.
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
Authors: Zhuotun Zhu, Xinggang Wang, Song Bai, Cong Yao, Xiang Bai
Date: 2014-09-25 00:00:00
Tasks: 3D Shape Classification, 3D Shape Recognition, 3D Shape Retrieval, Deep Learning, image-classification, Image Classification, object-detection, Object Detection, Retrieval

We study the problem of how to build a deep learning representation for 3D
shape. Deep learning has shown to be very effective in variety of visual
applications, such as image classification and object detection. However, it
has not been successfully applied to 3D shape recognition. This is because 3D
shape has complex structure in 3D space and there are limited number of 3D
shapes for feature learning. To address these problems, we project 3D shapes
into 2D space and use autoencoder for feature learning on the 2D images. High
accuracy 3D shape retrieval performance is obtained by aggregating the features
learned on 2D images. In addition, we show the proposed deep learning feature
is complementary to conventional local image descriptors. By combing the global
deep learning representation and the local descriptor representation, our
method can obtain the state-of-the-art performance on 3D shape retrieval
benchmarks.
Deeply-Supervised Nets
Authors: Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu
Date: 2014-09-18 00:00:00
Tasks: Classification, General Classification, Image Classification

Our proposed deeply-supervised nets (DSN) method simultaneously minimizes
classification error while making the learning process of hidden layers direct
and transparent. We make an attempt to boost the classification performance by
studying a new formulation in deep networks. Three aspects in convolutional
neural networks (CNN) style architectures are being looked at: (1) transparency
of the intermediate layers to the overall classification; (2)
discriminativeness and robustness of learned features, especially in the early
layers; (3) effectiveness in training due to the presence of the exploding and
vanishing gradients. We introduce "companion objective" to the individual
hidden layers, in addition to the overall objective at the output layer (a
different strategy to layer-wise pre-training). We extend techniques from
stochastic gradient methods to analyze our algorithm. The advantage of our
method is evident and our experimental result on benchmark datasets shows
significant performance gain over existing methods (e.g. all state-of-the-art
results on MNIST, CIFAR-10, CIFAR-100, and SVHN).
Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel
Authors: Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu
Date: 2013-12-19 00:00:00
Tasks: Learning Theory

Regularization is a well recognized powerful strategy to improve the
performance of a learning machine and $l^q$ regularization schemes with
$0<q<\infty$ are central in use. It is known that different $q$ leads to
different properties of the deduced estimators, say, $l^2$ regularization leads
to smooth estimators while $l^1$ regularization leads to sparse estimators.
Then, how does the generalization capabilities of $l^q$ regularization learning
vary with $q$? In this paper, we study this problem in the framework of
statistical learning theory and show that implementing $l^q$ coefficient
regularization schemes in the sample dependent hypothesis space associated with
Gaussian kernel can attain the same almost optimal learning rates for all
$0<q<\infty$. That is, the upper and lower bounds of learning rates for $l^q$
regularization learning are asymptotically identical for all $0<q<\infty$. Our
finding tentatively reveals that, in some modeling contexts, the choice of $q$
might not have a strong impact with respect to the generalization capability.
From this perspective, $q$ can be arbitrarily specified, or specified merely by
other no generalization criteria like smoothness, computational complexity,
sparsity, etc..
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
Authors: Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie J. Dorr, Nathaniel W. Filardo, Lori Levin, Scott Miller, Christine Piatko
Date: 2014-09-24 00:00:00
Tasks: Machine Translation, Translation

We describe a unified and coherent syntactic framework for supporting a
semantically-informed syntactic approach to statistical machine translation.
Semantically enriched syntactic tags assigned to the target-language training
texts improved translation quality. The resulting system significantly
outperformed a linguistically naive baseline model (Hiero), and reached the
highest scores yet reported on the NIST 2009 Urdu-English translation task.
This finding supports the hypothesis (posed by many researchers in the MT
community, e.g., in DARPA GALE) that both syntactic and semantic information
are critical for improving translation quality---and further demonstrates that
large gains can be achieved for low-resource languages with different word
order than English.
Variational Pseudolikelihood for Regularized Ising Inference
Authors: Charles K. Fisher
Date: 2014-09-24 00:00:00

I propose a variational approach to maximum pseudolikelihood inference of the
Ising model. The variational algorithm is more computationally efficient, and
does a better job predicting out-of-sample correlations than $L_2$ regularized
maximum pseudolikelihood inference as well as mean field and isolated spin pair
approximations with pseudocount regularization. The key to the approach is a
variational energy that regularizes the inference problem by shrinking the
couplings towards zero, while still allowing some large couplings to explain
strong correlations. The utility of the variational pseudolikelihood approach
is illustrated by training an Ising model to represent the letters A-J using
samples of letters from different computer fonts.