pwc-archive/papers-with-abstracts huggingface dataset:
Non-distributional Word Vector Representations
Authors: Manaal Faruqui, Chris Dyer
Date: 2015-06-17 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Representation Learning

Data-driven representation learning for words is a technique of central
importance in NLP. While indisputably useful as a source of features in
downstream tasks, such vectors tend to consist of uninterpretable components
whose relationship to the categories of traditional lexical semantic theories
is tenuous at best. We present a method for constructing interpretable word
vectors from hand-crafted linguistic resources like WordNet, FrameNet etc.
These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We
analyze their performance on state-of-the-art evaluation methods for
distributional models of word vectors and find they are competitive to standard
distributional approaches.
Discovering Hidden Factors of Variation in Deep Networks
Authors: Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen
Date: 2014-12-20 00:00:00
Tasks: General Classification

Deep learning has enjoyed a great deal of success because of its ability to
learn useful features for tasks such as classification. But there has been less
exploration in learning the factors of variation apart from the classification
signal. By augmenting autoencoders with simple regularization terms during
training, we demonstrate that standard deep architectures can discover and
explicitly represent factors of variation beyond those relevant for
categorization. We introduce a cross-covariance penalty (XCov) as a method to
disentangle factors like handwriting style for digits and subject identity in
faces. We demonstrate this on the MNIST handwritten digit database, the Toronto
Faces Database (TFD) and the Multi-PIE dataset by generating manipulated
instances of the data. Furthermore, we demonstrate these deep networks can
extrapolate `hidden' variation in the supervised signal.
Gated Feedback Recurrent Neural Networks
Authors: Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio
Date: 2015-02-09 00:00:00
Tasks: Language Modeling, Language Modelling

In this work, we propose a novel recurrent neural network (RNN) architecture.
The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of
stacking multiple recurrent layers by allowing and controlling signals flowing
from upper recurrent layers to lower layers using a global gating unit for each
pair of layers. The recurrent signals exchanged between layers are gated
adaptively based on the previous hidden states and the current input. We
evaluated the proposed GF-RNN with different types of recurrent units, such as
tanh, long short-term memory and gated recurrent units, on the tasks of
character-level language modeling and Python program evaluation. Our empirical
evaluation of different RNN units, revealed that in both tasks, the GF-RNN
outperforms the conventional approaches to build deep stacked RNNs. We suggest
that the improvement arises because the GF-RNN can adaptively assign different
layers to different timescales and layer-to-layer interactions (including the
top-down ones which are not usually present in a stacked RNN) by learning to
gate these interactions.
Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions
Authors: Ying Sun, Prabhu Babu, Daniel P. Palomar
Date: 2015-06-17 00:00:00

This paper considers the problem of robustly estimating a structured
covariance matrix with an elliptical underlying distribution with known mean.
In applications where the covariance matrix naturally possesses a certain
structure, taking the prior structure information into account in the
estimation procedure is beneficial to improve the estimation accuracy. We
propose incorporating the prior structure information into Tyler's M-estimator
and formulate the problem as minimizing the cost function of Tyler's estimator
under the prior structural constraint. First, the estimation under a general
convex structural constraint is introduced with an efficient algorithm for
finding the estimator derived based on the majorization minimization (MM)
algorithm framework. Then, the algorithm is tailored to several special
structures that enjoy a wide range of applications in signal processing related
fields, namely, sum of rank-one matrices, Toeplitz, and banded Toeplitz
structure. In addition, two types of non-convex structures, i.e., the Kronecker
structure and the spiked covariance structure, are also discussed, where it is
shown that simple algorithms can be derived under the guidelines of MM.
Numerical results show that the proposed estimator achieves a smaller
estimation error than the benchmark estimators at a lower computational cost.
Learning Spike time codes through Morphological Learning with Binary Synapses
Authors: Subhrajit Roy, Phyo Phyo San, Shaista Hussain, Lee Wang Wei, Arindam Basu
Date: 2015-06-17 00:00:00

In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses
that is able to learn temporal features of spike input patterns is considered.
Since binary synapses are considered, learning happens through formation and
elimination of connections between the inputs and the dendritic branches to
modify the structure or "morphology" of the NNLD. A morphological learning
algorithm inspired by the 'Tempotron', i.e., a recently proposed temporal
learning algorithm-is presented in this work. Unlike 'Tempotron', the proposed
learning rule uses a technique to automatically adapt the NNLD threshold during
training. Experimental results indicate that our NNLD with 1-bit synapses can
obtain similar accuracy as a traditional Tempotron with 4-bit synapses in
classifying single spike random latency and pair-wise synchrony patterns.
Hence, the proposed method is better suited for robust hardware implementation
in the presence of statistical variations. We also present results of applying
this rule to real life spike classification problems from the field of tactile
sensing.
Fair assignment of indivisible objects under ordinal preferences
Authors: Haris Aziz, Serge Gaspers, Simon Mackenzie, Toby Walsh
Date: 2013-12-23 00:00:00
Tasks: Fairness, Open-Ended Question Answering

We consider the discrete assignment problem in which agents express ordinal
preferences over objects and these objects are allocated to the agents in a
fair manner. We use the stochastic dominance relation between fractional or
randomized allocations to systematically define varying notions of
proportionality and envy-freeness for discrete assignments. The computational
complexity of checking whether a fair assignment exists is studied for these
fairness notions. We also characterize the conditions under which a fair
assignment is guaranteed to exist. For a number of fairness concepts,
polynomial-time algorithms are presented to check whether a fair assignment
exists. Our algorithmic results also extend to the case of unequal entitlements
of agents. Our NP-hardness result, which holds for several variants of
envy-freeness, answers an open question posed by Bouveret, Endriss, and Lang
(ECAI 2010). We also propose fairness concepts that always suggest a non-empty
set of assignments with meaningful fairness properties. Among these concepts,
optimal proportionality and optimal weak proportionality appear to be desirable
fairness concepts.
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Authors: Salman H. Khan, Munawar Hayat, Mohammed Bennamoun, Roberto Togneri, Ferdous Sohel
Date: 2015-06-17 00:00:00
Tasks: Object, Object Recognition, Scene Classification, Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to
the diverse intra-class variations and the confusing inter-class similarities.
This paper presents a novel approach which exploits rich mid-level
convolutional features to categorize indoor scenes. Traditionally used
convolutional features preserve the global spatial structure, which is a
desirable property for general object recognition. However, we argue that this
structuredness is not much helpful when we have large variations in scene
layouts, e.g., in indoor scenes. We propose to transform the structured
convolutional activations to another highly discriminative feature space. The
representation in the transformed space not only incorporates the
discriminative aspects of the target dataset, but it also encodes the features
in terms of the general object categories that are present in indoor scenes. To
this end, we introduce a new large-scale dataset of 1300 object categories
which are commonly present in indoor scenes. Our proposed approach achieves a
significant performance boost over previous state of the art approaches on five
major scene classification datasets.
Robust High Quality Image Guided Depth Upsampling
Authors: Wei Liu, Yijun Li, Xiaogang Chen, Jie Yang, Qiang Wu, Jingyi Yu
Date: 2015-06-17 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Time-of-Flight (ToF) depth sensing camera is able to obtain depth maps at a
high frame rate. However, its low resolution and sensitivity to the noise are
always a concern. A popular solution is upsampling the obtained noisy low
resolution depth map with the guidance of the companion high resolution color
image. However, due to the constrains in the existing upsampling models, the
high resolution depth map obtained in such way may suffer from either texture
copy artifacts or blur of depth discontinuity. In this paper, a novel
optimization framework is proposed with the brand new data term and smoothness
term. The comprehensive experiments using both synthetic data and real data
show that the proposed method well tackles the problem of texture copy
artifacts and blur of depth discontinuity. It also demonstrates sufficient
robustness to the noise. Moreover, a data driven scheme is proposed to
adaptively estimate the parameter in the upsampling optimization framework. The
encouraging performance is maintained even in the case of large upsampling e.g.
$8\times$ and $16\times$.
Generalized Additive Model Selection
Authors: Alexandra Chouldechova, Trevor Hastie
Date: 2015-06-11 00:00:00
Tasks: Additive models, model, Model Selection

We introduce GAMSEL (Generalized Additive Model Selection), a penalized
likelihood approach for fitting sparse generalized additive models in high
dimension. Our method interpolates between null, linear and additive models by
allowing the effect of each variable to be estimated as being either zero,
linear, or a low-complexity curve, as determined by the data. We present a
blockwise coordinate descent procedure for efficiently optimizing the penalized
likelihood objective over a dense grid of the tuning parameter, producing a
regularization path of additive models. We demonstrate the performance of our
method on both real and simulated data examples, and compare it with existing
techniques for additive model selection.
Geometric Inference for General High-Dimensional Linear Inverse Problems
Authors: T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
Date: 2014-04-17 00:00:00
Tasks: compressed sensing, Matrix Completion, regression, Two-sample testing, Vocal Bursts Intensity Prediction

This paper presents a unified geometric framework for the statistical
analysis of a general ill-posed linear inverse model which includes as special
cases noisy compressed sensing, sign vector recovery, trace regression,
orthogonal matrix estimation, and noisy matrix completion. We propose
computationally feasible convex programs for statistical inference including
estimation, confidence intervals and hypothesis testing. A theoretical
framework is developed to characterize the local estimation rate of convergence
and to provide statistical inference guarantees. Our results are built based on
the local conic geometry and duality. The difficulty of statistical inference
is captured by the geometric characterization of the local tangent cone through
the Gaussian width and Sudakov minoration estimate.
Deep Convolutional Networks on Graph-Structured Data
Authors: Mikael Henaff, Joan Bruna, Yann Lecun
Date: 2015-06-16 00:00:00
Tasks: General Classification

Deep Learning's recent successes have mostly relied on Convolutional
Networks, which exploit fundamental statistical properties of images, sounds
and video data: the local stationarity and multi-scale compositional structure,
that allows expressing long range interactions in terms of shorter, localized
interactions. However, there exist other important examples, such as text
documents or bioinformatic data, that may lack some or all of these strong
statistical regularities.
  In this paper we consider the general question of how to construct deep
architectures with small learning complexity on general non-Euclidean domains,
which are typically unknown and need to be estimated from the data. In
particular, we develop an extension of Spectral Networks which incorporates a
Graph Estimation procedure, that we test on large-scale classification
problems, matching or improving over Dropout Networks with far less parameters
to estimate.
SNA-based reasoning for multiagent team composition
Authors: Andre Filipe de Moraes Batista, Maria das Graças Bruno Marietto
Date: 2015-06-16 00:00:00

The social network analysis (SNA), branch of complex systems can be used in
the construction of multiagent systems. This paper proposes a study of how
social network analysis can assist in modeling multiagent systems, while
addressing similarities and differences between the two theories. We built a
prototype of multi-agent systems for resolution of tasks through the formation
of teams of agents that are formed on the basis of the social network
established between agents. Agents make use of performance indicators to assess
when should change their social network to maximize the participation in teams
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
Authors: José Ramón Padilla-López, Alexandros André Chaaraoui, Francisco Flórez-Revuelta
Date: 2014-07-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
Authors: Shiliang Zhang, Hui Jiang, MingBin Xu, JunFeng Hou, Li-Rong Dai
Date: 2015-05-06 00:00:00

In this paper, we propose the new fixed-size ordinally-forgetting encoding
(FOFE) method, which can almost uniquely encode any variable-length sequence of
words into a fixed-size representation. FOFE can model the word order in a
sequence using a simple ordinally-forgetting mechanism according to the
positions of words. In this work, we have applied FOFE to feedforward neural
network language models (FNN-LMs). Experimental results have shown that without
using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform
not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis
Authors: Jiuchuan Jiang, Manfred Jaeger
Date: 2015-06-16 00:00:00
Tasks: Relational Reasoning

Most work in the area of statistical relational learning (SRL) is focussed on
discrete data, even though a few approaches for hybrid SRL models have been
proposed that combine numerical and discrete variables. In this paper we
distinguish numerical random variables for which a probability distribution is
defined by the model from numerical input variables that are only used for
conditioning the distribution of discrete response variables. We show how
numerical input relations can very easily be used in the Relational Bayesian
Network framework, and that existing inference and learning methods need only
minor adjustments to be applied in this generalized setting. The resulting
framework provides natural relational extensions of classical probabilistic
models for categorical data. We demonstrate the usefulness of RBN models with
numeric input relations by several examples.
  In particular, we use the augmented RBN framework to define probabilistic
models for multi-relational (social) networks in which the probability of a
link between two nodes depends on numeric latent feature vectors associated
with the nodes. A generic learning procedure can be used to obtain a
maximum-likelihood fit of model parameters and latent feature values for a
variety of models that can be expressed in the high-level RBN representation.
Specifically, we propose a model that allows us to interpret learned latent
feature values as community centrality degrees by which we can identify nodes
that are central for one community, that are hubs between communities, or that
are isolated nodes. In a multi-relational setting, the model also provides a
characterization of how different relations are associated with each community.
Depth Perception in Autostereograms: 1/f-Noise is Best
Authors: Yael Yankelevsky, Ishai Shvartz, Tamar Avraham, Alfred M. Bruckstein
Date: 2015-06-16 00:00:00

An autostereogram is a single image that encodes depth information that pops
out when looking at it. The trick is achieved by replicating a vertical strip
that sets a basic two-dimensional pattern with disparity shifts that encode a
three-dimensional scene. It is of interest to explore the dependency between
the ease of perceiving depth in autostereograms and the choice of the basic
pattern used for generating them. In this work we confirm a theory proposed by
Bruckstein et al. to explain the process of autostereographic depth perception,
providing a measure for the ease of "locking into" the depth profile, based on
the spectral properties of the basic pattern used. We report the results of
three sets of psychophysical experiments using autostereograms generated from
two-dimensional random noise patterns having power spectra of the form
$1/f^\beta$. The experiments were designed to test the ability of human
subjects to identify smooth, low resolution surfaces, as well as detail, in the
form of higher resolution objects in the depth profile, and to determine limits
in identifying small objects as a function of their size. In accordance with
the theory, we discover a significant advantage of the $1/f$ noise pattern
(pink noise) for fast depth lock-in and fine detail detection, showing that
such patterns are optimal choices for autostereogram design. Validating the
theoretical model predictions strengthens its underlying assumptions, and
contributes to a better understanding of the visual system's binocular
disparity mechanisms.
Emotion Analysis of Songs Based on Lyrical and Audio Features
Authors: Adit Jamdar, Jessica Abraham, Karishma Khanna, Rahul Dubey
Date: 2015-06-16 00:00:00
Tasks: Emotion Recognition, General Classification

In this paper, a method is proposed to detect the emotion of a song based on
its lyrical and audio features. Lyrical features are generated by segmentation
of lyrics during the process of data extraction. ANEW and WordNet knowledge is
then incorporated to compute Valence and Arousal values. In addition to this,
linguistic association rules are applied to ensure that the issue of ambiguity
is properly addressed. Audio features are used to supplement the lyrical ones
and include attributes like energy, tempo, and danceability. These features are
extracted from The Echo Nest, a widely used music intelligence platform.
Construction of training and test sets is done on the basis of social tags
extracted from the last.fm website. The classification is done by applying
feature weighting and stepwise threshold reduction on the k-Nearest Neighbors
algorithm to provide fuzziness in the classification.
Fast Generation of Best Interval Patterns for Nonmonotonic Constraints
Authors: Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli
Date: 2015-06-02 00:00:00

In pattern mining, the main challenge is the exponential explosion of the set
of patterns. Typically, to solve this problem, a constraint for pattern
selection is introduced. One of the first constraints proposed in pattern
mining is support (frequency) of a pattern in a dataset. Frequency is an
anti-monotonic function, i.e., given an infrequent pattern, all its
superpatterns are not frequent. However, many other constraints for pattern
selection are not (anti-)monotonic, which makes it difficult to generate
patterns satisfying these constraints. In this paper we introduce the notion of
projection-antimonotonicity and $\theta$-$\Sigma\o\phi\iota\alpha$ algorithm
that allows efficient generation of the best patterns for some nonmonotonic
constraints. In this paper we consider stability and $\Delta$-measure, which
are nonmonotonic constraints, and apply them to interval tuple datasets. In the
experiments, we compute best interval tuple patterns w.r.t. these measures and
show the advantage of our approach over postfiltering approaches.
  KEYWORDS: Pattern mining, nonmonotonic constraints, interval tuple data
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection
Authors: Liliana Lo Presti, Marco La Cascia
Date: 2015-06-16 00:00:00
Tasks: Classification, Emotion Recognition, Facial Emotion Recognition, General Classification

This paper proposes a new approach to model the temporal dynamics of a
sequence of facial expressions. To this purpose, a sequence of Face Image
Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI)
system. The temporal dynamics of such sequence of descriptors are represented
by means of a Hankel matrix. The paper presents different strategies to compute
dynamics-based representation of a sequence of FID, and reports classification
accuracy values of the proposed representations within different standard
classification frameworks. The representations have been validated in two very
challenging application domains: emotion recognition and pain detection.
Experiments on two publicly available benchmarks and comparison with
state-of-the-art approaches demonstrate that the dynamics-based FID
representation attains competitive performance when off-the-shelf
classification tools are adopted.
The Scope and Limits of Simulation in Cognitive Models
Authors: Ernest Davis, Gary Marcus
Date: 2015-06-16 00:00:00

It has been proposed that human physical reasoning consists largely of
running "physics engines in the head" in which the future trajectory of the
physical system under consideration is computed precisely using accurate
scientific theories. In such models, uncertainty and incomplete knowledge is
dealt with by sampling probabilistically over the space of possible
trajectories ("Monte Carlo simulation"). We argue that such simulation-based
models are too weak, in that there are many important aspects of human physical
reasoning that cannot be carried out this way, or can only be carried out very
inefficiently; and too strong, in that humans make large systematic errors that
the models cannot account for. We conclude that simulation-based reasoning
makes up at most a small part of a larger system that encompasses a wide range
of additional cognitive processes.