pwc-archive/papers-with-abstracts huggingface dataset:
Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation
Authors: Catarina Runa Miranda, Pedro Mendes, Pedro Coelho, Xenxo Alvarez, João Freitas, Miguel Sales Dias, Verónica Costa Orvalho
Date: 2015-06-02 00:00:00
Tasks: Face Recognition

Each human face is unique. It has its own shape, topology, and distinguishing
features. As such, developing and testing facial tracking systems are
challenging tasks. The existing face recognition and tracking algorithms in
Computer Vision mainly specify concrete situations according to particular
goals and applications, requiring validation methodologies with data that fits
their purposes. However, a database that covers all possible variations of
external and factors does not exist, increasing researchers' work in acquiring
their own data or compiling groups of databases.
  To address this shortcoming, we propose a methodology for facial data
acquisition through definition of fundamental variables, such as subject
characteristics, acquisition hardware, and performance parameters. Following
this methodology, we also propose two protocols that allow the capturing of
facial behaviors under uncontrolled and real-life situations. As validation, we
executed both protocols which lead to creation of two sample databases: FdMiee
(Facial database with Multi input, expressions, and environments) and FACIA
(Facial Multimodal database driven by emotional induced acting).
  Using different types of hardware, FdMiee captures facial information under
environmental and facial behaviors variations. FACIA is an extension of FdMiee
introducing a pipeline to acquire additional facial behaviors and speech using
an emotion-acting method. Therefore, this work eases the creation of adaptable
database according to algorithm's requirements and applications, leading to
simplified validation and testing processes.
SkILL - a Stochastic Inductive Logic Learner
Authors: Joana Côrte-Real, Theofrastos Mantadelis, Inês Dutra, Ricardo Rocha
Date: 2015-06-02 00:00:00
Tasks: Inductive logic programming, Relational Reasoning

Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored
area of Statistical Relational Learning which extends classic Inductive Logic
Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic
Learner, which takes probabilistic annotated data and produces First Order
Logic theories. Data in several domains such as medicine and bioinformatics
have an inherent degree of uncer- tainty, that can be used to produce models
closer to reality. SkILL can not only use this type of probabilistic data to
extract non-trivial knowl- edge from databases, but it also addresses
efficiency issues by introducing a novel, efficient and effective search
strategy to guide the search in PILP environments. The capabilities of SkILL
are demonstrated in three dif- ferent datasets: (i) a synthetic toy example
used to validate the system, (ii) a probabilistic adaptation of a well-known
biological metabolism ap- plication, and (iii) a real world medical dataset in
the breast cancer domain. Results show that SkILL can perform as well as a
deterministic ILP learner, while also being able to incorporate probabilistic
knowledge that would otherwise not be considered.
Average Convergence Rate of Evolutionary Algorithms
Authors: Jun He, Guangming Lin
Date: 2015-04-30 00:00:00
Tasks: Evolutionary Algorithms

In evolutionary optimization, it is important to understand how fast
evolutionary algorithms converge to the optimum per generation, or their
convergence rate. This paper proposes a new measure of the convergence rate,
called average convergence rate. It is a normalised geometric mean of the
reduction ratio of the fitness difference per generation. The calculation of
the average convergence rate is very simple and it is applicable for most
evolutionary algorithms on both continuous and discrete optimization. A
theoretical study of the average convergence rate is conducted for discrete
optimization. Lower bounds on the average convergence rate are derived. The
limit of the average convergence rate is analysed and then the asymptotic
average convergence rate is proposed.
Learning Speech Rate in Speech Recognition
Authors: Xiangyu Zeng, Shi Yin, Dong Wang
Date: 2015-06-02 00:00:00
Tasks: speech-recognition, Speech Recognition

A significant performance reduction is often observed in speech recognition
when the rate of speech (ROS) is too low or too high. Most of present
approaches to addressing the ROS variation focus on the change of speech
signals in dynamic properties caused by ROS, and accordingly modify the dynamic
model, e.g., the transition probabilities of the hidden Markov model (HMM).
However, an abnormal ROS changes not only the dynamic but also the static
property of speech signals, and thus can not be compensated for purely by
modifying the dynamic model. This paper proposes an ROS learning approach based
on deep neural networks (DNN), which involves an ROS feature as the input of
the DNN model and so the spectrum distortion caused by ROS can be learned and
compensated for. The experimental results show that this approach can deliver
better performance for too slow and too fast utterances, demonstrating our
conjecture that ROS impacts both the dynamic and the static property of speech.
In addition, the proposed approach can be combined with the conventional HMM
transition adaptation method, offering additional performance gains.
Soft Computing Techniques for Change Detection in remotely sensed images : A Review
Authors: Madhu Khurana, Vikas Saxena
Date: 2015-06-02 00:00:00
Tasks: Change Detection, General Classification

With the advent of remote sensing satellites, a huge repository of remotely
sensed images is available. Change detection in remotely sensed images has been
an active research area as it helps us understand the transitions that are
taking place on the Earths surface. This paper discusses the methods and their
classifications proposed by various researchers for change detection. Since use
of soft computing based techniques are now very popular among research
community, this paper also presents a classification based on learning
techniques used in soft-computing methods for change detection.
Video (GIF) Sentiment Analysis using Large-Scale Mid-Level Ontology
Authors: Zheng Cai, Donglin Cao, Rongrong Ji
Date: 2015-06-02 00:00:00
Tasks: Sentiment Analysis

With faster connection speed, Internet users are now making social network a
huge reservoir of texts, images and video clips (GIF). Sentiment analysis for
such online platform can be used to predict political elections, evaluates
economic indicators and so on. However, GIF sentiment analysis is quite
challenging, not only because it hinges on spatio-temporal visual
contentabstraction, but also for the relationship between such abstraction and
final sentiment remains unknown.In this paper, we dedicated to find out such
relationship.We proposed a SentiPairSequence basedspatiotemporal visual
sentiment ontology, which forms the midlevel representations for GIFsentiment.
The establishment process of SentiPair contains two steps. First, we construct
the Synset Forest to define the semantic tree structure of visual sentiment
label elements. Then, through theSynset Forest, we organically select and
combine sentiment label elements to form a mid-level visual sentiment
representation. Our experiments indicate that SentiPair outperforms other
competing mid-level attributes. Using SentiPair, our analysis frameworkcan
achieve satisfying prediction accuracy (72.6%). We also opened ourdataset
(GSO-2015) to the research community. GSO-2015 contains more than 6,000
manually annotated GIFs out of more than 40,000 candidates. Each is labeled
with both sentiment and SentiPair Sequence.
Image Retrieval Based on Binary Signature ang S-kGraph
Authors: Thanh The Van, Thanh Manh Le
Date: 2015-06-02 00:00:00
Tasks: Image Retrieval, Retrieval

In this paper, we introduce an optimum approach for querying similar images
on large digital-image databases. Our work is based on RBIR (region-based image
retrieval) method which uses multiple regions as the key to retrieval images.
This method significantly improves the accuracy of queries. However, this also
increases the cost of computing. To reduce this expensive computational cost,
we implement binary signature encoder which maps an image to its identification
in binary. In order to fasten the lookup, binary signatures of images are
classified by the help of S-kGraph. Finally, our work is evaluated on COREL's
images.
Discriminative Neural Sentence Modeling by Tree-Based Convolution
Authors: Lili Mou, Hao Peng, Ge Li, Yan Xu, Lu Zhang, Zhi Jin
Date: 2015-04-05 00:00:00
Proceeding: EMNLP 2015 9
Tasks: General Classification, Sentence, Sentiment Analysis, Text Classification

This paper proposes a tree-based convolutional neural network (TBCNN) for
discriminative sentence modeling. Our models leverage either constituency trees
or dependency trees of sentences. The tree-based convolution process extracts
sentences' structural features, and these features are aggregated by max
pooling. Such architecture allows short propagation paths between the output
layer and underlying feature detectors, which enables effective structural
feature learning and extraction. We evaluate our models on two tasks: sentiment
analysis and question classification. In both experiments, TBCNN outperforms
previous state-of-the-art results, including existing neural networks and
dedicated feature/rule engineering. We also make efforts to visualize the
tree-based convolution process, shedding light on how our models work.
What Makes Kevin Spacey Look Like Kevin Spacey
Authors: Supasorn Suwajanakorn, Ira Kemelmacher-Shlizerman, Steve Seitz
Date: 2015-06-02 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

We reconstruct a controllable model of a person from a large photo collection
that captures his or her {\em persona}, i.e., physical appearance and behavior.
The ability to operate on unstructured photo collections enables modeling a
huge number of people, including celebrities and other well photographed people
without requiring them to be scanned. Moreover, we show the ability to drive or
{\em puppeteer} the captured person B using any other video of a different
person A. In this scenario, B acts out the role of person A, but retains
his/her own personality and character. Our system is based on a novel
combination of 3D face reconstruction, tracking, alignment, and multi-texture
modeling, applied to the puppeteering problem. We demonstrate convincing
results on a large variety of celebrities derived from Internet imagery and
video.
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
Authors: Xiaowei Zhou, Spyridon Leonardos, Xiaoyan Hu, Kostas Daniilidis
Date: 2014-11-11 00:00:00
Proceeding: CVPR 2015 6

We investigate the problem of estimating the 3D shape of an object, given a
set of 2D landmarks in a single image. To alleviate the reconstruction
ambiguity, a widely-used approach is to confine the unknown 3D shape within a
shape space built upon existing shapes. While this approach has proven to be
successful in various applications, a challenging issue remains, i.e., the
joint estimation of shape parameters and camera-pose parameters requires to
solve a nonconvex optimization problem. The existing methods often adopt an
alternating minimization scheme to locally update the parameters, and
consequently the solution is sensitive to initialization. In this paper, we
propose a convex formulation to address this problem and develop an efficient
algorithm to solve the proposed convex program. We demonstrate the exact
recovery property of the proposed method, its merits compared to alternative
methods, and the applicability in human pose and car shape estimation.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
Authors: Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen-Change Loy, Xiaoou Tang
Date: 2014-12-17 00:00:00
Proceeding: CVPR 2015 6
Tasks: Diversity, Object, object-detection, Object Detection

In this paper, we propose deformable deep convolutional neural networks for
generic object detection. This new deep learning object detection framework has
innovations in multiple aspects. In the proposed new deep architecture, a new
deformation constrained pooling (def-pooling) layer models the deformation of
object parts with geometric constraint and penalty. A new pre-training strategy
is proposed to learn feature representations more suitable for the object
detection task and with good generalization capability. By changing the net
structures, training strategies, adding and removing some key components in the
detection pipeline, a set of models with large diversity are obtained, which
significantly improves the effectiveness of model averaging. The proposed
approach improves the mean averaged precision obtained by RCNN
\cite{girshick2014rich}, which was the state-of-the-art, from 31\% to 50.3\% on
the ILSVRC2014 detection test set. It also outperforms the winner of
ILSVRC2014, GoogLeNet, by 6.1\%. Detailed component-wise analysis is also
provided through extensive experimental evaluation, which provide a global view
for people to understand the deep learning object detection pipeline.
Quantifying Creativity in Art Networks
Authors: Ahmed Elgammal, Babak Saleh
Date: 2015-06-02 00:00:00

Can we develop a computer algorithm that assesses the creativity of a
painting given its context within art history? This paper proposes a novel
computational framework for assessing the creativity of creative products, such
as paintings, sculptures, poetry, etc. We use the most common definition of
creativity, which emphasizes the originality of the product and its influential
value. The proposed computational framework is based on constructing a network
between creative products and using this network to infer about the originality
and influence of its nodes. Through a series of transformations, we construct a
Creativity Implication Network. We show that inference about creativity in this
network reduces to a variant of network centrality problems which can be solved
efficiently. We apply the proposed framework to the task of quantifying
creativity of paintings (and sculptures). We experimented on two datasets with
over 62K paintings to illustrate the behavior of the proposed framework. We
also propose a methodology for quantitatively validating the results of the
proposed algorithm, which we call the "time machine experiment".
Statistical Machine Translation Features with Multitask Tensor Networks
Authors: Hendra Setiawan, Zhongqiang Huang, Jacob Devlin, Thomas Lamar, Rabih Zbib, Richard Schwartz, John Makhoul
Date: 2015-06-01 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Machine Translation, Tensor Networks, Translation

We present a three-pronged approach to improving Statistical Machine
Translation (SMT), building on recent success in the application of neural
networks to SMT. First, we propose new features based on neural networks to
model various non-local translation phenomena. Second, we augment the
architecture of the neural network with tensor layers that capture important
higher-order interaction among the network units. Third, we apply multitask
learning to estimate the neural network parameters jointly. Each of our
proposed methods results in significant improvements that are complementary.
The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and
Chinese-English translation over a state-of-the-art system that already
includes neural network features.
Mutual Dependence: A Novel Method for Computing Dependencies Between Random Variables
Authors: Rahul Agarwal, Pierre Sacre, Sridevi V. Sarma
Date: 2015-06-01 00:00:00

In data science, it is often required to estimate dependencies between
different data sources. These dependencies are typically calculated using
Pearson's correlation, distance correlation, and/or mutual information.
However, none of these measures satisfy all the Granger's axioms for an "ideal
measure". One such ideal measure, proposed by Granger himself, calculates the
Bhattacharyya distance between the joint probability density function (pdf) and
the product of marginal pdfs. We call this measure the mutual dependence.
However, to date this measure has not been directly computable from data. In
this paper, we use our recently introduced maximum likelihood non-parametric
estimator for band-limited pdfs, to compute the mutual dependence directly from
the data. We construct the estimator of mutual dependence and compare its
performance to standard measures (Pearson's and distance correlation) for
different known pdfs by computing convergence rates, computational complexity,
and the ability to capture nonlinear dependencies. Our mutual dependence
estimator requires fewer samples to converge to theoretical values, is faster
to compute, and captures more complex dependencies than standard measures.
Sample-Optimal Density Estimation in Nearly-Linear Time
Authors: Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Date: 2015-06-01 00:00:00
Tasks: Density Estimation

We design a new, fast algorithm for agnostically learning univariate
probability distributions whose densities are well approximated by piecewise
polynomial functions. Let $f$ be the density function of an arbitrary
univariate distribution, and suppose that $f$ is $\mathrm{OPT}$-close in
$L_1$-distance to an unknown piecewise polynomial function with $t$ interval
pieces and degree $d$. Our algorithm draws $n = O(t(d+1)/\epsilon^2)$ samples
from $f$, runs in time $\tilde{O}(n \cdot \mathrm{poly}(d))$, and with
probability at least $9/10$ outputs an $O(t)$-piecewise degree-$d$ hypothesis
$h$ that is $4 \cdot \mathrm{OPT} +\epsilon$ close to $f$.
  Our general algorithm yields (nearly) sample-optimal and nearly-linear time
estimators for a wide range of structured distribution families over both
continuous and discrete domains in a unified way. For most of our applications,
these are the first sample-optimal and nearly-linear time estimators in the
literature. As a consequence, our work resolves the sample and computational
complexities of a broad class of inference tasks via a single "meta-algorithm".
Moreover, we experimentally demonstrate that our algorithm performs very well
in practice.
  Our algorithm consists of three "levels": (i) At the top level, we employ an
iterative greedy algorithm for finding a good partition of the real line into
the pieces of a piecewise polynomial. (ii) For each piece, we show that the
sub-problem of finding a good polynomial fit on the current interval can be
solved efficiently with a separation oracle method. (iii) We reduce the task of
finding a separating hyperplane to a combinatorial problem and give an
efficient algorithm for this problem. Combining these three procedures gives a
density estimation algorithm with the claimed guarantees.
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Authors: Armand Joulin, Tomas Mikolov
Date: 2015-03-03 00:00:00
Proceeding: NeurIPS 2015 12

Despite the recent achievements in machine learning, we are still very far
from achieving real artificial intelligence. In this paper, we discuss the
limitations of standard deep learning approaches and show that some of these
limitations can be overcome by learning how to grow the complexity of a model
in a structured way. Specifically, we study the simplest sequence prediction
problems that are beyond the scope of what is learnable with standard recurrent
networks, algorithmically generated sequences which can only be learned by
models which have the capacity to count and to memorize sequences. We show that
some basic algorithms can be learned from sequential data using a recurrent
network associated with a trainable memory.
Practice in Synonym Extraction at Large Scale
Authors: Liangliang Cao, Chang Wang
Date: 2014-12-06 00:00:00
Tasks: Question Answering

Synonym extraction is an important task in natural language processing and
often used as a submodule in query expansion, question answering and other
applications. Automatic synonym extractor is highly preferred for large scale
applications. Previous studies in synonym extraction are most limited to small
scale datasets. In this paper, we build a large dataset with 3.4 million
synonym/non-synonym pairs to capture the challenges in real world scenarios. We
proposed (1) a new cost function to accommodate the unbalanced learning
problem, and (2) a feature learning based deep neural network to model the
complicated relationships in synonym pairs. We compare several different
approaches based on SVMs and neural networks, and find out a novel feature
learning based neural network outperforms the methods with hand-assigned
features. Specifically, the best performance of our model surpasses the SVM
baseline with a significant 97\% relative improvement.
Blocks and Fuel: Frameworks for deep learning
Authors: Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio
Date: 2015-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

We introduce two Python frameworks to train neural networks on large
datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler
with CUDA-support. It facilitates the training of complex neural network models
by providing parametrized Theano operations, attaching metadata to Theano's
symbolic computational graph, and providing an extensive set of utilities to
assist training the networks, e.g. training algorithms, logging, monitoring,
visualization, and serialization. Fuel provides a standard format for machine
learning datasets. It allows the user to easily iterate over large datasets,
performing many types of pre-processing on the fly.
On Quantum Generalizations of Information-Theoretic Measures and their Contribution to Distributional Semantics
Authors: William Blacoe
Date: 2015-06-01 00:00:00

Information-theoretic measures such as relative entropy and correlation are
extremely useful when modeling or analyzing the interaction of probabilistic
systems. We survey the quantum generalization of 5 such measures and point out
some of their commonalities and interpretations. In particular we find the
application of information theory to distributional semantics useful. By
modeling the distributional meaning of words as density operators rather than
vectors, more of their semantic structure may be exploited. Furthermore,
properties of and interactions between words such as ambiguity, similarity and
entailment can be simulated more richly and intuitively when using methods from
quantum information theory.
Bootstrap Bias Corrections for Ensemble Methods
Authors: Giles Hooker, Lucas Mentch
Date: 2015-06-01 00:00:00
Tasks: BIG-bench Machine Learning, regression

This paper examines the use of a residual bootstrap for bias correction in
machine learning regression methods. Accounting for bias is an important
obstacle in recent efforts to develop statistical inference for machine
learning methods. We demonstrate empirically that the proposed bootstrap bias
correction can lead to substantial improvements in both bias and predictive
accuracy. In the context of ensembles of trees, we show that this correction
can be approximated at only double the cost of training the original ensemble
without introducing additional variance. Our method is shown to improve
test-set accuracy over random forests by up to 70\% on example problems from
the UCI repository.