pwc-archive/papers-with-abstracts huggingface dataset:
Audio Source Separation Using a Deep Autoencoder
Authors: Giljin Jang, Han-Gyu Kim, Yung-Hwan Oh
Date: 2014-12-22 00:00:00
Tasks: Audio Source Separation, Clustering

This paper proposes a novel framework for unsupervised audio source
separation using a deep autoencoder. The characteristics of unknown source
signals mixed in the mixed input is automatically by properly configured
autoencoders implemented by a network with many layers, and separated by
clustering the coefficient vectors in the code layer. By investigating the
weight vectors to the final target, representation layer, the primitive
components of the audio signals in the frequency domain are observed. By
clustering the activation coefficients in the code layer, the previously
unknown source signals are segregated. The original source sounds are then
separated and reconstructed by using code vectors which belong to different
clusters. The restored sounds are not perfect but yield promising results for
the possibility in the success of many practical applications.
Bayesian Optimisation for Machine Translation
Authors: Yishu Miao, Ziyu Wang, Phil Blunsom
Date: 2014-12-22 00:00:00
Tasks: Bayesian Optimisation, Machine Translation, Translation

This paper presents novel Bayesian optimisation algorithms for minimum error
rate training of statistical machine translation systems. We explore two
classes of algorithms for efficiently exploring the translation space, with the
first based on N-best lists and the second based on a hypergraph representation
that compactly represents an exponential number of translation options. Our
algorithms exhibit faster convergence and are capable of obtaining lower error
rates than the existing translation model specific approaches, all within a
generic Bayesian optimisation framework. Further more, we also introduce a
random embedding algorithm to scale our approach to sparse high dimensional
feature sets.
Recurrent-Neural-Network for Language Detection on Twitter Code-Switching Corpus
Authors: Joseph Chee Chang, Chu-Cheng Lin
Date: 2014-12-14 00:00:00
Tasks: Machine Translation, Part-Of-Speech Tagging, Sentiment Analysis, Translation

Mixed language data is one of the difficult yet less explored domains of
natural language processing. Most research in fields like machine translation
or sentiment analysis assume monolingual input. However, people who are capable
of using more than one language often communicate using multiple languages at
the same time. Sociolinguists believe this "code-switching" phenomenon to be
socially motivated. For example, to express solidarity or to establish
authority. Most past work depend on external tools or resources, such as
part-of-speech tagging, dictionary look-up, or named-entity recognizers to
extract rich features for training machine learning models. In this paper, we
train recurrent neural networks with only raw features, and use word embedding
to automatically learn meaningful representations. Using the same
mixed-language Twitter corpus, our system is able to outperform the best
SVM-based systems reported in the EMNLP'14 Code-Switching Workshop by 1% in
accuracy, or by 17% in error rate reduction.
Decision-theoretic rough sets-based three-way approximations of interval-valued fuzzy sets
Authors: Guangming Lang
Date: 2014-12-22 00:00:00

In practical situations, interval-valued fuzzy sets are frequently
encountered. In this paper, firstly, we present shadowed sets for interpreting
and understanding interval fuzzy sets. We also provide an analytic solution to
computing the pair of thresholds by searching for a balance of uncertainty in
the framework of shadowed sets. Secondly, we construct errors-based three-way
approximations of interval-valued fuzzy sets. We also provide an alternative
decision-theoretic formulation for calculating the pair of thresholds by
transforming interval-valued loss functions into single-valued loss functions,
in which the required thresholds are computed by minimizing decision costs.
Thirdly, we compute errors-based three-way approximations of interval-valued
fuzzy sets by using interval-valued loss functions. Finally, we employ several
examples to illustrate that how to take an action for an object with
interval-valued membership grade by using interval-valued loss functions.
Half-CNN: A General Framework for Whole-Image Regression
Authors: Jun Yuan, Bingbing Ni, Ashraf A. Kassim
Date: 2014-12-22 00:00:00
Tasks: Classification, Face Detection, General Classification, image-classification, Image Classification, object-detection, Object Detection, regression, Saliency Prediction, Segmentation

The Convolutional Neural Network (CNN) has achieved great success in image
classification. The classification model can also be utilized at image or patch
level for many other applications, such as object detection and segmentation.
In this paper, we propose a whole-image CNN regression model, by removing the
full connection layer and training the network with continuous feature maps.
This is a generic regression framework that fits many applications. We
demonstrate this method through two tasks: simultaneous face detection &
segmentation, and scene saliency prediction. The result is comparable with
other models in the respective fields, using only a small scale network. Since
the regression model is trained on corresponding image / feature map pairs,
there are no requirements on uniform input size as opposed to the
classification model. Our framework avoids classifier design, a process that
may introduce too much manual intervention in model development. Yet, it is
highly correlated to the classification network and offers some in-deep review
of CNN structures.
The Lovelace 2.0 Test of Artificial Creativity and Intelligence
Authors: Mark O. Riedl
Date: 2014-10-22 00:00:00

Observing that the creation of certain types of artistic artifacts
necessitate intelligence, we present the Lovelace 2.0 Test of creativity as an
alternative to the Turing Test as a means of determining whether an agent is
intelligent. The Lovelace 2.0 Test builds off prior tests of creativity and
additionally provides a means of directly comparing the relative intelligence
of different agents.
A New Way to Factorize Linear Cameras
Authors: Feng Lu, Ziqiang Chen
Date: 2014-12-22 00:00:00

The implementation details of factorizing the 3x4 projection matrices of
linear cameras into their left matrix factors and the 4x4 homogeneous
central(also parallel for infinite center cases) projection factors are
presented in this work. Any full row rank 3x4 real matrix can be factorized
into such basic matrices which will be called LC factors.
  A further extension to multiple view midpoint triangulation, for both pinhole
and affine camera cases, is also presented based on such camera factorizations.
A Stable Multi-Scale Kernel for Topological Machine Learning
Authors: Jan Reininghaus, Stefan Huber, Ulrich Bauer, Roland Kwitt
Date: 2014-12-21 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Shape Classification, BIG-bench Machine Learning, General Classification, Retrieval, Topological Data Analysis

Topological data analysis offers a rich source of valuable information to
study vision problems. Yet, so far we lack a theoretically sound connection to
popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In
this work, we establish such a connection by designing a multi-scale kernel for
persistence diagrams, a stable summary representation of topological features
in data. We show that this kernel is positive definite and prove its stability
with respect to the 1-Wasserstein distance. Experiments on two benchmark
datasets for 3D shape classification/retrieval and texture recognition show
considerable performance gains of the proposed method compared to an
alternative approach that is based on the recently introduced persistence
landscapes.
Parameter estimation in spherical symmetry groups
Authors: Yu-Hui Chen, Dennis Wei, Gregory Newstadt, Marc DeGraef, Jeffrey Simmons, Alfred Hero
Date: 2014-11-10 00:00:00
Tasks: parameter estimation

This paper considers statistical estimation problems where the probability
distribution of the observed random variable is invariant with respect to
actions of a finite topological group. It is shown that any such distribution
must satisfy a restricted finite mixture representation. When specialized to
the case of distributions over the sphere that are invariant to the actions of
a finite spherical symmetry group $\mathcal G$, a group-invariant extension of
the Von Mises Fisher (VMF) distribution is obtained. The $\mathcal G$-invariant
VMF is parameterized by location and scale parameters that specify the
distribution's mean orientation and its concentration about the mean,
respectively. Using the restricted finite mixture representation these
parameters can be estimated using an Expectation Maximization (EM) maximum
likelihood (ML) estimation algorithm. This is illustrated for the problem of
mean crystal orientation estimation under the spherically symmetric group
associated with the crystal form, e.g., cubic or octahedral or hexahedral.
Simulations and experiments establish the advantages of the extended VMF EM-ML
estimator for data acquired by Electron Backscatter Diffraction (EBSD)
microscopy of a polycrystalline Nickel alloy sample.
Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation
Authors: Anoop Katti, Anurag Mittal
Date: 2014-12-21 00:00:00
Tasks: Pose Estimation

Part-based models with restrictive tree-structured interactions for the Human
Pose Estimation problem, leaves many part interactions unhandled. Two of the
most common and strong manifestations of such unhandled interactions are
self-occlusion among the parts and the confusion in the localization of the
non-adjacent symmetric parts. By handling the self-occlusion in a data
efficient manner, we improve the performance of the basic Mixture of Parts
model by a large margin, especially on uncommon poses. Through addressing the
confusion in the symmetric limb localization using a combination of two
complementing trees, we improve the performance on all the parts by atmost
doubling the running time. Finally, we show that the combination of the two
solutions improves the results. We report results that are equivalent to the
state-of-the-art on two standard datasets. Because of maintaining the
tree-structured interactions and only part-level modeling of the base Mixture
of Parts model, this is achieved in time that is much less than the best
performing part-based model.
A web-based tool to Analyze Semantic Similarity Networks
Authors: Mario Cannataro, Pietro Hiram Guzzi, Marianna Milano, Pierangelo Veltri
Date: 2014-12-21 00:00:00
Tasks: Community Detection, Semantic Similarity, Semantic Textual Similarity

In computational biology, biological entities such as genes or proteins are
usually annotated with terms extracted from Gene Ontology (GO). The functional
similarity among terms of an ontology is evaluated by using Semantic Similarity
Measures (SSM). More recently, the extensive application of SSMs yielded to the
Semantic Similarity Networks (SSNs). SSNs are edge-weighted graphs where the
nodes are concepts (e.g. proteins) and each edge has an associated weight that
represents the semantic similarity among related pairs of nodes. The analysis
of SSNs may reveal biologically meaningful knowledge. For these aims, the need
for the introduction of tool able to manage and analyze SSN arises.
Consequently we developed SSN-Analyzer a web based tool able to build and
preprocess SSN. As proof of concept we demonstrate that community detection
algorithms applied to filtered (thresholded) networks, have better performances
in terms of biological relevance of the results, with respect to the use of raw
unfiltered networks.
Bi-directional Shape Correspondences (BSC): A Novel Technique for 2-d Shape Warping in Quadratic Time?
Authors: Abdulrahman Oladipupo Ibraheem
Date: 2014-12-21 00:00:00
Tasks: Clustering, Heuristic Search

We propose Bidirectional Shape Correspondence (BSC) as a possible improvement
on the famous shape contexts (SC) framework. Our proposals derive from the
observation that the SC framework enforces a one-to-one correspondence between
sample points, and that this leads to two possible drawbacks. First, this
denies the framework the opportunity to effect advantageous many-to-many
matching between points on the two shapes being compared. Second, this calls
for the Hungarian algorithm which unfortunately usurps cubic time. While the
dynamic-space-warping dynamic programming algorithm has provided a standard
solution to the first problem above, it demands quintic time for general
multi-contour shapes, and w times quadratic time for the special case of
single-contour shapes, even after an heuristic search window of width w has
been chosen. Therefore, in this work, we propose a simple method for computing
"many-to-many" correspondences for the class of all 2-d shapes in quadratic
time. Our approach is to explicitly let each point on the first shape choose a
best match on the second shape, and vice versa. Along the way, we also propose
the use of data-clustering techniques for dealing with the outliers problem,
and, from another viewpoint, it turns out that this clustering can be seen as
an autonomous, rather than pre-computed, sampling of shape boundary.
Correlation of Data Reconstruction Error and Shrinkages in Pair-wise Distances under Principal Component Analysis (PCA)
Authors: Abdulrahman Oladipupo Ibraheem
Date: 2014-12-21 00:00:00
Tasks: General Classification

In this on-going work, I explore certain theoretical and empirical
implications of data transformations under the PCA. In particular, I state and
prove three theorems about PCA, which I paraphrase as follows: 1). PCA without
discarding eigenvector rows is injective, but looses this injectivity when
eigenvector rows are discarded 2). PCA without discarding eigen- vector rows
preserves pair-wise distances, but tends to cause pair-wise distances to shrink
when eigenvector rows are discarded. 3). For any pair of points, the shrinkage
in pair-wise distance is bounded above by an L1 norm reconstruction error
associated with the points. Clearly, 3). suggests that there might exist some
correlation between shrinkages in pair-wise distances and mean square
reconstruction error which is defined as the sum of those eigenvalues
associated with the discarded eigenvectors. I therefore decided to perform
numerical experiments to obtain the corre- lation between the sum of those
eigenvalues and shrinkages in pair-wise distances. In addition, I have also
performed some experiments to check respectively the effect of the sum of those
eigenvalues and the effect of the shrinkages on classification accuracies under
the PCA map. So far, I have obtained the following results on some publicly
available data from the UCI Machine Learning Repository: 1). There seems to be
a strong cor- relation between the sum of those eigenvalues associated with
discarded eigenvectors and shrinkages in pair-wise distances. 2). Neither the
sum of those eigenvalues nor pair-wise distances have any strong correlations
with classification accuracies. 1
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction
Authors: Abdulrahman Oladipupo Ibraheem
Date: 2014-12-21 00:00:00
Tasks: Attribute

By drawing on ideas from optimisation theory, artificial neural networks
(ANN), graph embeddings and sparse representations, I develop a novel
technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at
addressing the feature extraction problem. The proposed method uses (preferably
deep) ANNs for projecting input attribute vectors to an output space wherein
pairwise distances are maximized for vectors belonging to different classes,
but minimized for those belonging to the same class, while simultaneously
enforcing sparsity on the ANN outputs. The vectors that result from the
projection can then be used as features in any classifier of choice.
Mathematically, I formulate the proposed method as the minimisation of an
objective function which can be interpreted, in the ANN output space, as a
negative factor of the sum of the squares of the pair-wise distances between
output vectors belonging to different classes, added to a positive factor of
the sum of squares of the pair-wise distances between output vectors belonging
to the same classes, plus sparsity and weight decay terms. To derive an
algorithm for minimizing the objective function via gradient descent, I use the
multi-variate version of the chain rule to obtain the partial derivatives of
the function with respect to ANN weights and biases, and find that each of the
required partial derivatives can be expressed as a sum of six terms. As it
turns out, four of those six terms can be computed using the standard back
propagation algorithm; the fifth can be computed via a slight modification of
the standard backpropagation algorithm; while the sixth one can be computed via
simple arithmetic. Finally, I propose experiments on the ARABASE Arabic corpora
of digits and letters, the CMU PIE database of faces, the MNIST digits
database, and other standard machine learning databases.
Locally Weighted Learning for Naive Bayes Classifier
Authors: Kim-Hung Li, Cheuk Ting Li
Date: 2014-12-21 00:00:00

As a consequence of the strong and usually violated conditional independence
assumption (CIA) of naive Bayes (NB) classifier, the performance of NB becomes
less and less favorable compared to sophisticated classifiers when the sample
size increases. We learn from this phenomenon that when the size of the
training data is large, we should either relax the assumption or apply NB to a
"reduced" data set, say for example use NB as a local model. The latter
approach trades the ignored information for the robustness to the model
assumption. In this paper, we consider using NB as a model for locally weighted
data. A special weighting function is designed so that if CIA holds for the
unweighted data, it also holds for the weighted data. The new method is
intuitive and capable of handling class imbalance. It is theoretically more
sound than the locally weighted learners of naive Bayes that base
classification only on the $k$ nearest neighbors. Empirical study shows that
the new method with appropriate choice of parameter outperforms seven existing
classifiers of similar nature.
Implicit Temporal Differences
Authors: Aviv Tamar, Panos Toulis, Shie Mannor, Edoardo M. Airoldi
Date: 2014-12-21 00:00:00
Tasks: Reinforcement Learning

In reinforcement learning, the TD($\lambda$) algorithm is a fundamental
policy evaluation method with an efficient online implementation that is
suitable for large-scale problems. One practical drawback of TD($\lambda$) is
its sensitivity to the choice of the step-size. It is an empirically well-known
fact that a large step-size leads to fast convergence, at the cost of higher
variance and risk of instability. In this work, we introduce the implicit
TD($\lambda$) algorithm which has the same function and computational cost as
TD($\lambda$), but is significantly more stable. We provide a theoretical
explanation of this stability and an empirical evaluation of implicit
TD($\lambda$) on typical benchmark tasks. Our results show that implicit
TD($\lambda$) outperforms standard TD($\lambda$) and a state-of-the-art method
that automatically tunes the step-size, and thus shows promise for wide
applicability.
Beyond Q-Resolution and Prenex Form: A Proof System for Quantified Constraint Satisfaction
Authors: Hubie Chen
Date: 2014-03-02 00:00:00
Tasks: Form, Sentence

We consider the quantified constraint satisfaction problem (QCSP) which is to
decide, given a structure and a first-order sentence (not assumed here to be in
prenex form) built from conjunction and quantification, whether or not the
sentence is true on the structure. We present a proof system for certifying the
falsity of QCSP instances and develop its basic theory; for instance, we
provide an algorithmic interpretation of its behavior. Our proof system places
the established Q-resolution proof system in a broader context, and also allows
us to derive QCSP tractability results.
Qualitative shape representation based on the qualitative relative direction and distance calculus eOPRAm
Authors: Christopher H. Dorr, Reinhard Moratz
Date: 2014-12-20 00:00:00

This document serves as a brief technical report, detailing the processes
used to represent and reconstruct simplified polygons using qualitative spatial
descriptions, as defined by the eOPRAm qualitative spatial calculus.
Active Learning for Crowd-Sourced Databases
Authors: Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael. I. Jordan, Samuel Madden
Date: 2012-09-17 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

Crowd-sourcing has become a popular means of acquiring labeled data for a
wide variety of tasks where humans are more accurate than computers, e.g.,
labeling images, matching objects, or analyzing sentiment. However, relying
solely on the crowd is often impractical even for data sets with thousands of
items, due to time and cost constraints of acquiring human input (which cost
pennies and minutes per label). In this paper, we propose algorithms for
integrating machine learning into crowd-sourced databases, with the goal of
allowing crowd-sourcing applications to scale, i.e., to handle larger datasets
at lower costs. The key observation is that, in many of the above tasks, humans
and machine learning algorithms can be complementary, as humans are often more
accurate but slow and expensive, while algorithms are usually less accurate,
but faster and cheaper.
  Based on this observation, we present two new active learning algorithms to
combine humans and algorithms together in a crowd-sourced database. Our
algorithms are based on the theory of non-parametric bootstrap, which makes our
results applicable to a broad class of machine learning models. Our results, on
three real-life datasets collected with Amazon's Mechanical Turk, and on 15
well-known UCI data sets, show that our methods on average ask humans to label
one to two orders of magnitude fewer items to achieve the same accuracy as a
baseline that labels random images, and two to eight times fewer questions than
previous active learning schemes.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
Authors: Chaochao Lu, Xiaoou Tang
Date: 2014-04-15 00:00:00
Tasks: Diversity, Face Recognition, Face Verification, Multi-Task Learning

Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variations. This paper proposes a principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model, named GaussianFace, to enrich the diversity of training data. In comparison to existing methods, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification in an unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. Extensive experiments demonstrate the effectiveness of the proposed model in learning from diverse data sources and generalize to unseen domain. Specifically, the accuracy of our algorithm achieves an impressive accuracy rate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.