pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Identification of Translationese
Authors: Ella Rabinovich, Shuly Wintner
Date: 2016-09-11 00:00:00
Proceeding: TACL 2015 1
Tasks: Clustering, General Classification, Machine Translation, text-classification, Text Classification, Translation

Translated texts are distinctively different from original ones, to the
extent that supervised text classification methods can distinguish between them
with high accuracy. These differences were proven useful for statistical
machine translation. However, it has been suggested that the accuracy of
translation detection deteriorates when the classifier is evaluated outside the
domain it was trained on. We show that this is indeed the case, in a variety of
evaluation scenarios. We then show that unsupervised classification is highly
accurate on this task. We suggest a method for determining the correct labels
of the clustering outcomes, and then use the labels for voting, improving the
accuracy even further. Moreover, we suggest a simple method for clustering in
the challenging case of mixed-domain datasets, in spite of the dominance of
domain-related features over translation-related ones. The result is an
effective, fully-unsupervised method for distinguishing between original and
translated texts that can be applied to new domains with reasonable accuracy.
On the Similarities Between Native, Non-native and Translated Texts
Authors: Ella Rabinovich, Sergiu Nisioi, Noam Ordan, Shuly Wintner
Date: 2016-09-11 00:00:00
Proceeding: ACL 2016 8
Tasks: Translation

We present a computational analysis of three language varieties: native,
advanced non-native, and translation. Our goal is to investigate the
similarities and differences between non-native language productions and
translations, contrasting both with native language. Using a collection of
computational methods we establish three main results: (1) the three types of
texts are easily distinguishable; (2) non-native language and translations are
closer to each other than each of them is to native language; and (3) some of
these characteristics depend on the source or native language, while others do
not, reflecting, perhaps, unified principles that similarly affect translations
and non-native language.
EventNet Version 1.1 Technical Report
Authors: Dongang Wang, Zheng Shou, Hongyi Liu, Shih-Fu Chang
Date: 2016-05-24 00:00:00

EventNet is a large-scale video corpus and event ontology consisting of 500
events associated with event-specific concepts. In order to improve the quality
of the current EventNet, we conduct the following steps and introduce EventNet
version 1.1: (1) manually verify the correctness of event labels for all
videos; (2) remove the YouTube user bias by limiting the maximum number of
videos in each event from the same YouTube user as 3; (3) remove the videos
which are currently not accessible online; (4) remove the video belonging to
multiple event categories. After the above procedure, some events may contain
only a small number of videos, and therefore we crawl more videos for those
events to ensure every event will contain more than 50 videos. Finally,
EventNet version 1.1 contains 67,641 videos, 500 events, and 5,028
event-specific concepts. In addition, we train a Convolutional Neural Network
(CNN) model for event classification via fine-tuning AlexNet using EventNet
version 1.1. Then we use the trained CNN model to extract FC7 layer feature and
train binary classifiers using linear SVM for each event-specific concept. We
believe this new version of EventNet will significantly facilitate research in
computer vision and multimedia, and will put it online for public downloading
in the future.
A Simple Approach to Sparse Clustering
Authors: Ery Arias-Castro, Xiao Pu
Date: 2016-02-23 00:00:00
Tasks: Clustering

Consider the problem of sparse clustering, where it is assumed that only a
subset of the features are useful for clustering purposes. In the framework of
the COSA method of Friedman and Meulman, subsequently improved in the form of
the Sparse K-means method of Witten and Tibshirani, a natural and simpler
hill-climbing approach is introduced. The new method is shown to be competitive
with these two methods and others.
Fast K-Means with Accurate Bounds
Authors: James Newling, François Fleuret
Date: 2016-02-08 00:00:00
Tasks: Clustering, Vector Quantization (k-means problem)

We propose a novel accelerated exact k-means algorithm, which performs better
than the current state-of-the-art low-dimensional algorithm in 18 of 22
experiments, running up to 3 times faster. We also propose a general
improvement of existing state-of-the-art accelerated exact k-means algorithms
through better estimates of the distance bounds used to reduce the number of
distance calculations, and get a speedup in 36 of 44 experiments, up to 1.8
times faster.
  We have conducted experiments with our own implementations of existing
methods to ensure homogeneous evaluation of performance, and we show that our
implementations perform as well or better than existing available
implementations. Finally, we propose simplified variants of standard approaches
and show that they are faster than their fully-fledged counterparts in 59 of 62
experiments.
On the Relationship between Online Gaussian Process Regression and Kernel Least Mean Squares Algorithms
Authors: Steven Van Vaerenbergh, Jesus Fernandez-Bes, Víctor Elvira
Date: 2016-09-11 00:00:00
Tasks: regression

We study the relationship between online Gaussian process (GP) regression and
kernel least mean squares (KLMS) algorithms. While the latter have no capacity
of storing the entire posterior distribution during online learning, we
discover that their operation corresponds to the assumption of a fixed
posterior covariance that follows a simple parametric model. Interestingly,
several well-known KLMS algorithms correspond to specific cases of this model.
The probabilistic perspective allows us to understand how each of them handles
uncertainty, which could explain some of their performance differences.
Adaptive Affinity Matrix for Unsupervised Metric Learning
Authors: Yaoyi Li, Junxuan Chen, Hongtao Lu
Date: 2015-11-13 00:00:00
Tasks: Clustering, Metric Learning

Spectral clustering is one of the most popular clustering approaches with the
capability to handle some challenging clustering problems. Most spectral
clustering methods provide a nonlinear map from the data manifold to a
subspace. Only a little work focuses on the explicit linear map which can be
viewed as the unsupervised distance metric learning. In practice, the selection
of the affinity matrix exhibits a tremendous impact on the unsupervised
learning. While much success of affinity learning has been achieved in recent
years, some issues such as noise reduction remain to be addressed. In this
paper, we propose a novel method, dubbed Adaptive Affinity Matrix (AdaAM), to
learn an adaptive affinity matrix and derive a distance metric from the
affinity. We assume the affinity matrix to be positive semidefinite with
ability to quantify the pairwise dissimilarity. Our method is based on posing
the optimization of objective function as a spectral decomposition problem. We
yield the affinity from both the original data distribution and the widely-used
heat kernel. The provided matrix can be regarded as the optimal representation
of pairwise relationship on the manifold. Extensive experiments on a number of
real-world data sets show the effectiveness and efficiency of AdaAM.
A centralized reinforcement learning method for multi-agent job scheduling in Grid
Authors: Milad Moradi
Date: 2016-09-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

One of the main challenges in Grid systems is designing an adaptive,
scalable, and model-independent method for job scheduling to achieve a
desirable degree of load balancing and system efficiency. Centralized job
scheduling methods have some drawbacks, such as single point of failure and
lack of scalability. Moreover, decentralized methods require a coordination
mechanism with limited communications. In this paper, we propose a multi-agent
approach to job scheduling in Grid, named Centralized Learning Distributed
Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS
is a model free approach that uses the information of jobs and their completion
time to estimate the efficiency of resources. In this method, there are a
learner agent and several scheduler agents that perform the task of learning
and job scheduling with the use of a coordination strategy that maintains the
communication cost at a limited level. We evaluated the efficiency of the CLDS
method by designing and performing a set of experiments on a simulated Grid
system under different system scales and loads. The results show that the CLDS
can effectively balance the load of system even in large scale and heavy loaded
Grids, while maintains its adaptive performance and scalability.
Divide and...conquer? On the limits of algorithmic approaches to syntactic semantic structure
Authors: Diego Gabriel Krivochen
Date: 2016-09-11 00:00:00
Tasks: valid

In computer science, divide and conquer (D&C) is an algorithm design paradigm
based on multi-branched recursion. A D&C algorithm works by recursively and
monotonically breaking down a problem into sub problems of the same (or a
related) type, until these become simple enough to be solved directly. The
solutions to the sub problems are then combined to give a solution to the
original problem. The present work identifies D&C algorithms assumed within
contemporary syntactic theory, and discusses the limits of their applicability
in the realms of the syntax semantics and syntax morphophonology interfaces. We
will propose that D&C algorithms, while valid for some processes, fall short on
flexibility given a mixed approach to the structure of linguistic phrase
markers. Arguments in favour of a computationally mixed approach to linguistic
structure will be presented as an alternative that offers advantages to uniform
D&C approaches.
Relational Models
Authors: Volker Tresp, Maximilian Nickel
Date: 2016-09-11 00:00:00
Tasks: BIG-bench Machine Learning, Knowledge Graphs, Recommendation Systems, Survey

We provide a survey on relational models. Relational models describe complete
networked {domains by taking into account global dependencies in the data}.
Relational models can lead to more accurate predictions if compared to
non-relational machine learning approaches. Relational models typically are
based on probabilistic graphical models, e.g., Bayesian networks, Markov
networks, or latent variable models. Relational models have applications in
social networks analysis, the modeling of knowledge graphs, bioinformatics,
recommendation systems, natural language processing, medical decision support,
and linked data.
On the Necessity of Mixed Models: Dynamical Frustrations in the Mind
Authors: Diego Gabriel Krivochen
Date: 2013-07-18 00:00:00

In the present work we will present and analyze some basic processes at the
local and global level in linguistic derivations that seem to go beyond the
limits of Markovian or Turing-like computation, and require, in our opinion, a
quantum processor. We will first present briefly the working hypothesis and
then focus on the empirical domain. At the same time, we will argue that a
model appealing to only one kind of computation (be it quantum or not) is
necessarily insufficient, and thus both linear and non-linear formal models are
to be invoked in order to pursue a fuller understanding of mental computations
within a unified framework.
Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex Composite optimization
Authors: Xiyu Yu, DaCheng Tao
Date: 2016-06-02 00:00:00

Here we study non-convex composite optimization: first, a finite-sum of
smooth but non-convex functions, and second, a general function that admits a
simple proximal mapping. Most research on stochastic methods for composite
optimization assumes convexity or strong convexity of each function. In this
paper, we extend this problem into the non-convex setting using variance
reduction techniques, such as prox-SVRG and prox-SAGA. We prove that, with a
constant step size, both prox-SVRG and prox-SAGA are suitable for non-convex
composite optimization, and help the problem converge to a stationary point
within $O(1/\epsilon)$ iterations. That is similar to the convergence rate seen
with the state-of-the-art RSAG method and faster than stochastic gradient
descent. Our analysis is also extended into the min-batch setting, which
linearly accelerates the convergence. To the best of our knowledge, this is the
first analysis of convergence rate of variance-reduced proximal stochastic
gradient for non-convex composite optimization.
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
Authors: Hengyue Pan, Hui Jiang
Date: 2016-06-20 00:00:00
Tasks: Image Augmentation, image-classification, Image Classification

Convolutional neural networks (CNNs) have yielded the excellent performance
in a variety of computer vision tasks, where CNNs typically adopt a similar
structure consisting of convolution layers, pooling layers and fully connected
layers. In this paper, we propose to apply a novel method, namely Hybrid
Orthogonal Projection and Estimation (HOPE), to CNNs in order to introduce
orthogonality into the CNN structure. The HOPE model can be viewed as a hybrid
model to combine feature extraction using orthogonal linear projection with
mixture models. It is an effective model to extract useful information from the
original high-dimension feature vectors and meanwhile filter out irrelevant
noises. In this work, we present three different ways to apply the HOPE models
to CNNs, i.e., {\em HOPE-Input}, {\em single-HOPE-Block} and {\em
multi-HOPE-Blocks}. For {\em HOPE-Input} CNNs, a HOPE layer is directly used
right after the input to de-correlate high-dimension input feature vectors.
Alternatively, in {\em single-HOPE-Block} and {\em multi-HOPE-Blocks} CNNs, we
consider to use HOPE layers to replace one or more blocks in the CNNs, where
one block may include several convolutional layers and one pooling layer. The
experimental results on both Cifar-10 and Cifar-100 data sets have shown that
the orthogonal constraints imposed by the HOPE layers can significantly improve
the performance of CNNs in these image classification tasks (we have achieved
one of the best performance when image augmentation has not been applied, and
top 5 performance with image augmentation).
Nonparametric risk bounds for time-series forecasting
Authors: Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish
Date: 2012-12-03 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis, Time Series Forecasting

We derive generalization error bounds for traditional time-series forecasting
models. Our results hold for many standard forecasting tools including
autoregressive models, moving average models, and, more generally, linear
state-space models. These non-asymptotic bounds need only weak assumptions on
the data-generating process, yet allow forecasters to select among competing
models and to guarantee, with high probability, that their chosen model will
perform well. We motivate our techniques with and apply them to standard
economic and financial forecasting tools---a GARCH model for predicting equity
volatility and a dynamic stochastic general equilibrium model (DSGE), the
standard tool in macroeconomic forecasting. We demonstrate in particular how
our techniques can aid forecasters and policy makers in choosing models which
behave well under uncertainty and mis-specification.
Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames
Authors: Saad Nadeem, Arie Kaufman
Date: 2016-09-05 00:00:00
Tasks: Specificity

We present a computer-aided detection algorithm for polyps in optical
colonoscopy images. Polyps are the precursors to colon cancer. In the US alone,
more than 14 million optical colonoscopies are performed every year, mostly to
screen for polyps. Optical colonoscopy has been shown to have an approximately
25% polyp miss rate due to the convoluted folds and bends present in the colon.
In this work, we present an automatic detection algorithm to detect these
polyps in the optical colonoscopy images. We use a machine learning algorithm
to infer a depth map for a given optical colonoscopy image and then use a
detailed pre-built polyp profile to detect and delineate the boundaries of
polyps in this given image. We have achieved the best recall of 84.0% and the
best specificity value of 83.4%.
Refined Error Bounds for Several Learning Algorithms
Authors: Steve Hanneke
Date: 2015-12-22 00:00:00
Tasks: Active Learning

This article studies the achievable guarantees on the error rates of certain
learning algorithms, with particular focus on refining logarithmic factors.
Many of the results are based on a general technique for obtaining bounds on
the error rates of sample-consistent classifiers with monotonic error regions,
in the realizable case. We prove bounds of this type expressed in terms of
either the VC dimension or the sample compression size. This general technique
also enables us to derive several new bounds on the error rates of general
sample-consistent learning algorithms, as well as refined bounds on the label
complexity of the CAL active learning algorithm. Additionally, we establish a
simple necessary and sufficient condition for the existence of a
distribution-free bound on the error rates of all sample-consistent learning
rules, converging at a rate inversely proportional to the sample size. We also
study learning in the presence of classification noise, deriving a new excess
error rate guarantee for general VC classes under Tsybakov's noise condition,
and establishing a simple and general necessary and sufficient condition for
the minimax excess risk under bounded noise to converge at a rate inversely
proportional to the sample size.
New Steps on the Exact Learning of CNF
Authors: Montserrat Hermo, Ana Ozaki
Date: 2016-09-10 00:00:00
Tasks: Learning Theory

A major problem in computational learning theory is whether the class of
formulas in conjunctive normal form (CNF) is efficiently learnable. Although it
is known that this class cannot be polynomially learned using either membership
or equivalence queries alone, it is open whether CNF can be polynomially
learned using both types of queries. One of the most important results
concerning a restriction of the class CNF is that propositional Horn formulas
are polynomial time learnable in Angluin's exact learning model with membership
and equivalence queries. In this work we push this boundary and show that the
class of multivalued dependency formulas (MVDF) is polynomially learnable from
interpretations. We then provide a notion of reduction between learning
problems in Angluin's model, showing that a transformation of the algorithm
suffices to efficiently learn multivalued database dependencies from data
relations. We also show via reductions that our main result extends well known
previous results and allows us to find alternative solutions for them.
Resolution- and throughput-enhanced spectroscopy using high-throughput computational slit
Authors: Farnoud Kazemzadeh, Alexander Wong
Date: 2016-06-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

There exists a fundamental tradeoff between spectral resolution and the
efficiency or throughput for all optical spectrometers. The primary factors
affecting the spectral resolution and throughput of an optical spectrometer are
the size of the entrance aperture and the optical power of the focusing
element. Thus far collective optimization of the above mentioned has proven
difficult. Here, we introduce the concept of high-throughput computational
slits (HTCS), a numerical technique for improving both the effective spectral
resolution and efficiency of a spectrometer. The proposed HTCS approach was
experimentally validated using an optical spectrometer configured with a 200 um
entrance aperture, test, and a 50 um entrance aperture, control, demonstrating
improvements in spectral resolution of the spectrum by ~ 50% over the control
spectral resolution and improvements in efficiency of > 2 times over the
efficiency of the largest entrance aperture used in the study while producing
highly accurate spectra.
Random projections of random manifolds
Authors: Subhaneil Lahiri, Peiran Gao, Surya Ganguli
Date: 2016-07-14 00:00:00
Tasks: Dimensionality Reduction

Interesting data often concentrate on low dimensional smooth manifolds inside
a high dimensional ambient space. Random projections are a simple, powerful
tool for dimensionality reduction of such data. Previous works have studied
bounds on how many projections are needed to accurately preserve the geometry
of these manifolds, given their intrinsic dimensionality, volume and curvature.
However, such works employ definitions of volume and curvature that are
inherently difficult to compute. Therefore such theory cannot be easily tested
against numerical simulations to understand the tightness of the proven bounds.
We instead study typical distortions arising in random projections of an
ensemble of smooth Gaussian random manifolds. We find explicitly computable,
approximate theoretical bounds on the number of projections required to
accurately preserve the geometry of these manifolds. Our bounds, while
approximate, can only be violated with a probability that is exponentially
small in the ambient dimension, and therefore they hold with high probability
in cases of practical interest. Moreover, unlike previous work, we test our
theoretical bounds against numerical experiments on the actual geometric
distortions that typically occur for random projections of random smooth
manifolds. We find our bounds are tighter than previous results by several
orders of magnitude.
Simultaneous independent image display technique on multiple 3D objects
Authors: Takuto Hirukawa, Marco Visentini-Scarzanella, Hiroshi Kawasaki, Ryo Furukawa, Shinsaku Hiura
Date: 2016-09-10 00:00:00
Tasks: Colorization, Object

We propose a new system to visualize depth-dependent patterns and images on
solid objects with complex geometry using multiple projectors. The system,
despite consisting of conventional passive LCD projectors, is able to project
different images and patterns depending on the spatial location of the object.
The technique is based on the simple principle that multiple patterns projected
from multiple projectors interfere constructively with each other when their
patterns are projected on the same object. Previous techniques based on the
same principle can only achieve 1) low resolution volume colorization or 2)
high resolution images but only on a limited number of flat planes. In this
paper, we discretize a 3D object into a number of 3D points so that high
resolution images can be projected onto the complex shapes. We also propose a
dynamic ranges expansion technique as well as an efficient optimization
procedure based on epipolar constraints.
  Such technique can be used to the extend projection mapping to have spatial
dependency, which is desirable for practical applications. We also demonstrate
the system potential as a visual instructor for object placement and
assembling. Experiments prove the effectiveness of our method.