pwc-archive/papers-with-abstracts huggingface dataset:
Text Flow: A Unified Text Detection System in Natural Scene Images
Authors: Shangxuan Tian, Yifeng Pan, Chang Huang, Shijian Lu, Kai Yu, Chew Lim Tan
Date: 2016-04-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Scene Text Detection, Text Detection, Text-Line Extraction

The prevalent scene text detection approach follows four sequential steps
comprising character candidate detection, false character candidate removal,
text line extraction, and text line verification. However, errors occur and
accumulate throughout each of these sequential steps which often lead to low
detection performance. To address these issues, we propose a unified scene text
detection system, namely Text Flow, by utilizing the minimum cost (min-cost)
flow network model. With character candidates detected by cascade boosting, the
min-cost flow network model integrates the last three sequential steps into a
single process which solves the error accumulation problem at both character
level and text line level effectively. The proposed technique has been tested
on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a
multilingual dataset and it outperforms the state-of-the-art methods on all
three datasets with much higher recall and F-score. The good performance on the
multilingual dataset shows that the proposed technique can be used for the
detection of texts in different languages.
Contextual object categorization with energy-based model
Authors: Changyong Ri, Duho Pak, Cholryong Choe, Suhyang Kim, Yonghak Sin
Date: 2016-04-23 00:00:00
Tasks: model, Object, Object Categorization

Object categorization is a hot issue of an image mining. Contextual
information between objects is one of the important semantic knowledge of an
image. However, the previous researches for an object categorization have not
made full use of the contextual information, especially the spatial relations
between objects. In addition, the object categorization methods, which
generally use the probabilistic graphical models to implement the incorporation
of contextual information with appearance of objects, are almost inevitable to
evaluate the intractable partition function for normalization. In this work, we
introduced fully-connected fuzzy spatial relations including directional,
distance and topological relations between object regions, so the spatial
relational information could be fully utilized. Then, the spatial relations
were considered as well as co-occurrence and appearance of objects by using
energy-based model, where the energy function was defined as the region-object
association potential and the configuration potential of objects. Minimizing
the energy function of whole image arrangement, we obtained the optimal label
set about the image regions and addressed the evaluation of intractable
partition function in conditional random fields. Experimental results show the
validity and reliability of this proposed method.
A Computational Model for Situated Task Learning with Interactive Instruction
Authors: Shiwali Mohan, James Kirk, John Laird
Date: 2016-04-23 00:00:00

Learning novel tasks is a complex cognitive activity requiring the learner to
acquire diverse declarative and procedural knowledge. Prior ACT-R models of
acquiring task knowledge from instruction focused on learning procedural
knowledge from declarative instructions encoded in semantic memory. In this
paper, we identify the requirements for designing compu- tational models that
learn task knowledge from situated task- oriented interactions with an expert
and then describe and evaluate a model of learning from situated interactive
instruc- tion that is implemented in the Soar cognitive architecture.
When Are Nonconvex Problems Not Scary?
Authors: Ju Sun, Qing Qu, John Wright
Date: 2015-10-21 00:00:00
Tasks: Dictionary Learning, Retrieval, Tensor Decomposition

In this note, we focus on smooth nonconvex optimization problems that obey:
(1) all local minimizers are also global; and (2) around any saddle point or
local maximizer, the objective has a negative directional curvature. Concrete
applications such as dictionary learning, generalized phase retrieval, and
orthogonal tensor decomposition are known to induce such structures. We
describe a second-order trust-region algorithm that provably converges to a
global minimizer efficiently, without special initializations. Finally we
highlight alternatives, and open problems in this direction.
Finding Optimal Combination of Kernels using Genetic Programming
Authors: Jyothi Korra
Date: 2016-04-08 00:00:00
Tasks: Object, Object Categorization

In Computer Vision, problem of identifying or classifying the objects present
in an image is called Object Categorization. It is a challenging problem,
especially when the images have clutter background, occlusions or different
lighting conditions. Many vision features have been proposed which aid object
categorization even in such adverse conditions. Past research has shown that,
employing multiple features rather than any single features leads to better
recognition. Multiple Kernel Learning (MKL) framework has been developed for
learning an optimal combination of features for object categorization. Existing
MKL methods use linear combination of base kernels which may not be optimal for
object categorization. Real-world object categorization may need to consider
complex combination of kernels(non-linear) and not only linear combination.
Evolving non-linear functions of base kernels using Genetic Programming is
proposed in this report. Experiment results show that non-kernel generated
using genetic programming gives good accuracy as compared to linear combination
of kernels.
Can Boosting with SVM as Week Learners Help?
Authors: Dinesh Govindaraj
Date: 2016-04-18 00:00:00
Tasks: Object, Object Categorization, Object Recognition

Object recognition in images involves identifying objects with partial
occlusions, viewpoint changes, varying illumination, cluttered backgrounds.
Recent work in object recognition uses machine learning techniques SVM-KNN,
Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we
want to utilize SVM as week learners in AdaBoost. Experiments are done with
classifiers like near- est neighbor, k-nearest neighbor, Support vector
machines, Local learning(SVM- KNN) and AdaBoost. Models use Scale-Invariant
descriptors and Pyramid his- togram of gradient descriptors. AdaBoost is
trained with set of week classifier as SVMs, each with kernel distance function
on different descriptors. Results shows AdaBoost with SVM outperform other
methods for Object Categorization dataset.
Refining Architectures of Deep Convolutional Neural Networks
Authors: Sukrit Shankar, Duncan Robertson, Yani Ioannou, Antonio Criminisi, Roberto Cipolla
Date: 2016-04-22 00:00:00
Proceeding: CVPR 2016 6

Deep Convolutional Neural Networks (CNNs) have recently evinced immense
success for various image recognition tasks. However, a question of paramount
importance is somewhat unanswered in deep learning research - is the selected
CNN optimal for the dataset in terms of accuracy and model size? In this paper,
we intend to answer this question and introduce a novel strategy that alters
the architecture of a given CNN for a specified dataset, to potentially enhance
the original accuracy while possibly reducing the model size. We use two
operations for architecture refinement, viz. stretching and symmetrical
splitting. Our procedure starts with a pre-trained CNN for a given dataset, and
optimally decides the stretch and split factors across the network to refine
the architecture. We empirically demonstrate the necessity of the two
operations. We evaluate our approach on two natural scenes attributes datasets,
SUN Attributes and CAMIT-NSAD, with architectures of GoogleNet and VGG-11, that
are quite contrasting in their construction. We justify our choice of datasets,
and show that they are interestingly distinct from each other, and together
pose a challenge to our architectural refinement algorithm. Our results
substantiate the usefulness of the proposed method.
A Probabilistic $\ell_1$ Method for Clustering High Dimensional Data
Authors: Tsvetan Asamov, Adi Ben-Israel
Date: 2015-04-06 00:00:00
Tasks: Clustering, General Classification, Vocal Bursts Intensity Prediction

In general, the clustering problem is NP-hard, and global optimality cannot
be established for non-trivial instances. For high-dimensional data,
distance-based methods for clustering or classification face an additional
difficulty, the unreliability of distances in very high-dimensional spaces. We
propose a distance-based iterative method for clustering data in very
high-dimensional space, using the $\ell_1$-metric that is less sensitive to
high dimensionality than the Euclidean distance. For $K$ clusters in
$\mathbb{R}^n$, the problem decomposes to $K$ problems coupled by
probabilities, and an iteration reduces to finding $Kn$ weighted medians of
points on a line. The complexity of the algorithm is linear in the dimension of
the data space, and its performance was observed to improve significantly as
the dimension increases.
Practical Algorithms for Learning Near-Isometric Linear Embeddings
Authors: Jerry Luo, Kayla Shapiro, Hao-Jun Michael Shi, Qi Yang, Kan Zhu
Date: 2016-01-01 00:00:00
Tasks: Dimensionality Reduction

We propose two practical non-convex approaches for learning near-isometric,
linear embeddings of finite sets of data points. Given a set of training points
$\mathcal{X}$, we consider the secant set $S(\mathcal{X})$ that consists of all
pairwise difference vectors of $\mathcal{X}$, normalized to lie on the unit
sphere. The problem can be formulated as finding a symmetric and positive
semi-definite matrix $\boldsymbol{\Psi}$ that preserves the norms of all the
vectors in $S(\mathcal{X})$ up to a distortion parameter $\delta$. Motivated by
non-negative matrix factorization, we reformulate our problem into a Frobenius
norm minimization problem, which is solved by the Alternating Direction Method
of Multipliers (ADMM) and develop an algorithm, FroMax. Another method solves
for a projection matrix $\boldsymbol{\Psi}$ by minimizing the restricted
isometry property (RIP) directly over the set of symmetric, postive
semi-definite matrices. Applying ADMM and a Moreau decomposition on a proximal
mapping, we develop another algorithm, NILE-Pro, for dimensionality reduction.
FroMax is shown to converge faster for smaller $\delta$ while NILE-Pro
converges faster for larger $\delta$. Both non-convex approaches are then
empirically demonstrated to be more computationally efficient than prior convex
approaches for a number of applications in machine learning and signal
processing.
DisCSPs with Privacy Recast as Planning Problems for Utility-based Agents
Authors: Julien Savaux, Julien Vion, Sylvain Piechowiak, René Mandiau, Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius Silaghi
Date: 2016-04-22 00:00:00

Privacy has traditionally been a major motivation for decentralized problem
solving. However, even though several metrics have been proposed to quantify
it, none of them is easily integrated with common solvers. Constraint
programming is a fundamental paradigm used to approach various families of
problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems
(UDisCSP) where the utility of each state is estimated as the difference
between the the expected rewards for agreements on assignments for shared
variables, and the expected cost of privacy loss. Therefore, a traditional
DisCSP with privacy requirements is viewed as a planning problem. The actions
available to agents are: communication and local inference. Common
decentralized solvers are evaluated here from the point of view of their
interpretation as greedy planners. Further, we investigate some simple
extensions where these solvers start taking into account the utility function.
In these extensions we assume that the planning problem is further restricting
the set of communication actions to only the communication primitives present
in the corresponding solver protocols. The solvers obtained for the new type of
problems propose the action (communication/inference) to be performed in each
situation, defining thereby the policy.
Utilitarian Distributed Constraint Optimization Problems
Authors: Julien Savaux, Julien Vion, Sylvain Piechowiak, René Mandiau, Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius Silaghi
Date: 2016-04-22 00:00:00

Privacy has been a major motivation for distributed problem optimization.
However, even though several methods have been proposed to evaluate it, none of
them is widely used. The Distributed Constraint Optimization Problem (DCOP) is
a fundamental model used to approach various families of distributed problems.
As privacy loss does not occur when a solution is accepted, but when it is
proposed, privacy requirements cannot be interpreted as a criteria of the
objective function of the DCOP. Here we approach the problem by letting both
the optimized costs found in DCOPs and the privacy requirements guide the
agents' exploration of the search space. We introduce Utilitarian Distributed
Constraint Optimization Problem (UDCOP) where the costs and the privacy
requirements are used as parameters to a heuristic modifying the search
process. Common stochastic algorithms for decentralized constraint optimization
problems are evaluated here according to how well they preserve privacy.
Further, we propose some extensions where these solvers modify their search
process to take into account their privacy requirements, succeeding in
significantly reducing their privacy loss without significant degradation of
the solution quality.
Training Deep Nets with Sublinear Memory Cost
Authors: Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin
Date: 2016-04-21 00:00:00
Tasks: GPU

We propose a systematic approach to reduce the memory consumption of deep
neural network training. Specifically, we design an algorithm that costs
O(sqrt(n)) memory to train a n layer network, with only the computational cost
of an extra forward pass per mini-batch. As many of the state-of-the-art models
hit the upper bound of the GPU memory, our algorithm allows deeper and more
complex models to be explored, and helps advance the innovations in deep
learning research. We focus on reducing the memory cost to store the
intermediate feature maps and gradients during training. Computation graph
analysis is used for automatic in-place operation and memory sharing
optimizations. We show that it is possible to trade computation for memory -
giving a more memory efficient training algorithm with a little extra
computation cost. In the extreme case, our analysis also shows that the memory
consumption can be reduced to O(log n) with as little as O(n log n) extra cost
for forward computation. Our experiments show that we can reduce the memory
cost of a 1,000-layer deep residual network from 48G to 7G with only 30 percent
additional running time cost on ImageNet problems. Similarly, significant
memory cost reduction is observed in training complex recurrent neural networks
on very long sequences.
Handwritten Digits Recognition using Deep Convolutional Neural Network: An Experimental Study using EBlearn
Authors: Karim M. Mahmoud
Date: 2013-07-14 00:00:00

In this paper, results of an experimental study of a deep convolution neural
network architecture which can classify different handwritten digits using
EBLearn library are reported. The purpose of this neural network is to classify
input images into 10 different classes or digits (0-9) and to explore new
findings. The input dataset used consists of digits images of size 32X32 in
grayscale (MNIST dataset).
evt_MNIST: A spike based version of traditional MNIST
Authors: Mazdak Fatahi, Mahmood Ahmadi, Mahyar Shahsavari, Arash Ahmadi, Philippe Devienne
Date: 2016-04-22 00:00:00

Benchmarks and datasets have important role in evaluation of machine learning
algorithms and neural network implementations. Traditional dataset for images
such as MNIST is applied to evaluate efficiency of different training
algorithms in neural networks. This demand is different in Spiking Neural
Networks (SNN) as they require spiking inputs. It is widely believed, in the
biological cortex the timing of spikes is irregular. Poisson distributions
provide adequate descriptions of the irregularity in generating appropriate
spikes. Here, we introduce a spike-based version of MNSIT (handwritten digits
dataset),using Poisson distribution and show the Poissonian property of the
generated streams. We introduce a new version of evt_MNIST which can be used
for neural network evaluation.
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
Authors: Li Zhou, Emma Brunskill
Date: 2016-04-22 00:00:00
Tasks: Multi-Armed Bandits

Personalized recommendations for new users, also known as the cold-start
problem, can be formulated as a contextual bandit problem. Existing contextual
bandit algorithms generally rely on features alone to capture user variability.
Such methods are inefficient in learning new users' interests. In this paper we
propose Latent Contextual Bandits. We consider both the benefit of leveraging a
set of learned latent user classes for new users, and how we can learn such
latent classes from prior users. We show that our approach achieves a better
regret bound than existing algorithms. We also demonstrate the benefit of our
approach using a large real world dataset and a preliminary user study.
Entity Embeddings of Categorical Variables
Authors: Cheng Guo, Felix Berkhahn
Date: 2016-04-22 00:00:00
Tasks: Clustering, Entity Embeddings

We map categorical variables in a function approximation problem into
Euclidean spaces, which are the entity embeddings of the categorical variables.
The mapping is learned by a neural network during the standard supervised
training process. Entity embedding not only reduces memory usage and speeds up
neural networks compared with one-hot encoding, but more importantly by mapping
similar values close to each other in the embedding space it reveals the
intrinsic properties of the categorical variables. We applied it successfully
in a recent Kaggle competition and were able to reach the third position with
relative simple features. We further demonstrate in this paper that entity
embedding helps the neural network to generalize better when the data is sparse
and statistics is unknown. Thus it is especially useful for datasets with lots
of high cardinality features, where other methods tend to overfit. We also
demonstrate that the embeddings obtained from the trained neural network boost
the performance of all tested machine learning methods considerably when used
as the input features instead. As entity embedding defines a distance measure
for categorical variables it can be used for visualizing categorical data and
for data clustering.
Developing an ICU scoring system with interaction terms using a genetic algorithm
Authors: Chee Chun Gan, Gerard Learmonth
Date: 2016-04-22 00:00:00
Tasks: ICU Mortality, Mortality Prediction, Variable Selection

ICU mortality scoring systems attempt to predict patient mortality using
predictive models with various clinical predictors. Examples of such systems
are APACHE, SAPS and MPM. However, most such scoring systems do not actively
look for and include interaction terms, despite physicians intuitively taking
such interactions into account when making a diagnosis. One barrier to
including such terms in predictive models is the difficulty of using most
variable selection methods in high-dimensional datasets. A genetic algorithm
framework for variable selection with logistic regression models is used to
search for two-way interaction terms in a clinical dataset of adult ICU
patients, with separate models being built for each category of diagnosis upon
admittance to the ICU. The models had good discrimination across all
categories, with a weighted average AUC of 0.84 (>0.90 for several categories)
and the genetic algorithm was able to find several significant interaction
terms, which may be able to provide greater insight into mortality prediction
for health practitioners. The GA selected models had improved performance
against stepwise selection and random forest models, and provides greater
flexibility in terms of variable selection by being able to optimize over any
modeler-defined model performance metric instead of a specific variable
importance metric.
An improved chromosome formulation for genetic algorithms applied to variable selection with the inclusion of interaction terms
Authors: Chee Chun Gan, Gerard Learmonth
Date: 2016-04-22 00:00:00
Tasks: Computational Efficiency, Variable Selection

Genetic algorithms are a well-known method for tackling the problem of
variable selection. As they are non-parametric and can use a large variety of
fitness functions, they are well-suited as a variable selection wrapper that
can be applied to many different models. In almost all cases, the chromosome
formulation used in these genetic algorithms consists of a binary vector of
length n for n potential variables indicating the presence or absence of the
corresponding variables. While the aforementioned chromosome formulation has
exhibited good performance for relatively small n, there are potential problems
when the size of n grows very large, especially when interaction terms are
considered. We introduce a modification to the standard chromosome formulation
that allows for better scalability and model sparsity when interaction terms
are included in the predictor search space. Experimental results show that the
indexed chromosome formulation demonstrates improved computational efficiency
and sparsity on high-dimensional datasets with interaction terms compared to
the standard chromosome formulation.
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
Authors: Manfred Eppe, Sean Trott, Jerome Feldman
Date: 2016-04-22 00:00:00

We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art.
Parameterized Compilation Lower Bounds for Restricted CNF-formulas
Authors: Stefan Mengel
Date: 2016-04-22 00:00:00
Tasks: Diversity, Negation

We show unconditional parameterized lower bounds in the area of knowledge
compilation, more specifically on the size of circuits in decomposable negation
normal form (DNNF) that encode CNF-formulas restricted by several graph width
measures. In particular, we show that
  - there are CNF formulas of size $n$ and modular incidence treewidth $k$
whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and
  - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$
whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$.
  These results complement recent upper bounds for compiling CNF into DNNF and
strengthen---quantitatively and qualitatively---known conditional low\-er
bounds for cliquewidth. Moreover, they show that, unlike for many graph
problems, the parameters considered here behave significantly differently from
treewidth.