pwc-archive/papers-with-abstracts huggingface dataset:
Superpixel Convolutional Networks using Bilateral Inceptions
Authors: Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, Peter V. Gehler
Date: 2015-11-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.
Interpolated Discretized Embedding of Single Vectors and Vector Pairs for Classification, Metric Learning and Distance Approximation
Authors: Ofir Pele, Yakir Ben-Aliz
Date: 2016-08-08 00:00:00
Tasks: General Classification, Metric Learning, regression

We propose a new embedding method for a single vector and for a pair of
vectors. This embedding method enables: a) efficient classification and
regression of functions of single vectors; b) efficient approximation of
distance functions; and c) non-Euclidean, semimetric learning. To the best of
our knowledge, this is the first work that enables learning any general,
non-Euclidean, semimetrics. That is, our method is a universal semimetric
learning and approximation method that can approximate any distance function
with as high accuracy as needed with or without semimetric constraints. The
project homepage including code is at: http://www.ariel.ac.il/sites/ofirpele/ID
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Authors: Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao
Date: 2016-06-09 00:00:00
Tasks: Document Classification, Variational Inference

Word embedding maps words into a low-dimensional continuous embedding space
by exploiting the local word collocation patterns in a small context window. On
the other hand, topic modeling maps documents onto a low-dimensional topic
space, by utilizing the global word collocation patterns in the same document.
These two types of patterns are complementary. In this paper, we propose a
generative topic embedding model to combine the two types of patterns. In our
model, topics are represented by embedding vectors, and are shared across
documents. The probability of each word is influenced by both its local context
and its topic. A variational inference method yields the topic embeddings as
well as the topic mixing proportions for each document. Jointly they represent
the document in a low-dimensional continuous space. In two document
classification tasks, our method performs better than eight existing methods,
with fewer features. In addition, we illustrate with an example that our method
can generate coherent topics even based on only one document.
ASP for Minimal Entailment in a Rational Extension of SROEL
Authors: Laura Giordano, Daniele Theseider Dupré
Date: 2016-08-08 00:00:00

In this paper we exploit Answer Set Programming (ASP) for reasoning in a
rational extension SROEL-R-T of the low complexity description logic SROEL,
which underlies the OWL EL ontology language. In the extended language, a
typicality operator T is allowed to define concepts T(C) (typical C's) under a
rational semantics. It has been proven that instance checking under rational
entailment has a polynomial complexity. To strengthen rational entailment, in
this paper we consider a minimal model semantics. We show that, for arbitrary
SROEL-R-T knowledge bases, instance checking under minimal entailment is
\Pi^P_2-complete. Relying on a Small Model result, where models correspond to
answer sets of a suitable ASP encoding, we exploit Answer Set Preferences (and,
in particular, the asprin framework) for reasoning under minimal entailment.
The paper is under consideration for acceptance in Theory and Practice of Logic
Programming.
A MultiPath Network for Object Detection
Authors: Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár
Date: 2016-04-07 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection

The recent COCO object detection dataset presents several new challenges for
object detection. In particular, it contains objects at a broad range of
scales, less prototypical images, and requires more precise localization. To
address these challenges, we test three modifications to the standard Fast
R-CNN object detector: (1) skip connections that give the detector access to
features at multiple network layers, (2) a foveal structure to exploit object
context at multiple object resolutions, and (3) an integral loss function and
corresponding network adjustment that improve localization. The result of these
modifications is that information can flow along multiple paths in our network,
including through features from multiple network layers and from multiple
object views. We refer to our modified classifier as a "MultiPath" network. We
couple our MultiPath network with DeepMask object proposals, which are well
suited for localization and small objects, and adapt our pipeline to predict
segmentation masks in addition to bounding boxes. The combined system improves
results over the baseline Fast R-CNN detector with Selective Search by 66%
overall and by 4x on small objects. It placed second in both the COCO 2015
detection and segmentation challenges.
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Authors: Malte Probst, Franz Rothlauf
Date: 2015-09-22 00:00:00
Tasks: Bayesian Optimization, Combinatorial Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Deep Boltzmann Machines
(DBMs) are generative neural networks with these desired properties. We
integrate a DBM into an EDA and evaluate the performance of this system in
solving combinatorial optimization problems with a single objective. We compare
the results to the Bayesian Optimization Algorithm. The performance of DBM-EDA
was superior to BOA for difficult additively decomposable functions, i.e.,
concatenated deceptive traps of higher order. For most other benchmark
problems, DBM-EDA cannot clearly outperform BOA, or other neural network-based
EDAs. In particular, it often yields optimal solutions for a subset of the runs
(with fewer evaluations than BOA), but is unable to provide reliable
convergence to the global optimum competitively. At the same time, the model
building process is computationally more expensive than that of other EDAs
using probabilistic models from the neural network family, such as DAE-EDA.
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
Authors: Malte Probst
Date: 2015-09-30 00:00:00
Tasks: Combinatorial Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Generative Adversarial
Networks (GAN) are generative neural networks which can be trained to
implicitly model the probability distribution of given data, and it is possible
to sample this distribution. We integrate a GAN into an EDA and evaluate the
performance of this system when solving combinatorial optimization problems
with a single objective. We use several standard benchmark problems and compare
the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield
competitive results - the GAN lacks the ability to quickly learn a good
approximation of the probability distribution. A key reason seems to be the
large amount of noise present in the first EDA generations.
QPass: a Merit-based Evaluation of Soccer Passes
Authors: Laszlo Gyarmati, Rade Stanojevic
Date: 2016-08-08 00:00:00
Tasks: Descriptive

Quantitative analysis of soccer players' passing ability focuses on
descriptive statistics without considering the players' real contribution to
the passing and ball possession strategy of their team. Which player is able to
help the build-up of an attack, or to maintain the possession of the ball? We
introduce a novel methodology called QPass to answer questions like these
quantitatively. Based on the analysis of an entire season, we rank the players
based on the intrinsic value of their passes using QPass. We derive an album of
pass trajectories for different gaming styles. Our methodology reveals a quite
counterintuitive paradigm: losing the ball possession could lead to better
chances to win a game.
Complexity Results for Manipulation, Bribery and Control of the Kemeny Procedure in Judgment Aggregation
Authors: Ronald de Haan
Date: 2016-08-08 00:00:00

We study the computational complexity of several scenarios of strategic
behavior for the Kemeny procedure in the setting of judgment aggregation. In
particular, we investigate (1) manipulation, where an individual aims to
achieve a better group outcome by reporting an insincere individual opinion,
(2) bribery, where an external agent aims to achieve an outcome with certain
properties by bribing a number of individuals, and (3) control (by adding or
deleting issues), where an external agent aims to achieve an outcome with
certain properties by influencing the set of issues in the judgment aggregation
situation. We show that determining whether these types of strategic behavior
are possible (and if so, computing a policy for successful strategic behavior)
is complete for the second level of the Polynomial Hierarchy. That is, we show
that these problems are $\Sigma^p_2$-complete.
Database of handwritten Arabic mathematical formulas images
Authors: Ibtissem Hadj Ali, Mohammed Ali Mahjoub
Date: 2016-08-08 00:00:00

Although publicly available, ground-truthed database have proven useful for
training, evaluating, and comparing recognition systems in many domains, the
availability of such database for handwritten Arabic mathematical formula
recognition in particular, is currently quite poor. In this paper, we present a
new public database that contains mathematical expressions available in their
off-line handwritten form. Here, we describe the different steps that allowed
us to acquire this database, from the creation of the mathematical expression
corpora to the transcription of the collected data. Currently, the dataset
contains 4 238 off-line handwritten mathematical expressions written by 66
writers and 20 300 handwritten isolated symbol images. The ground truth is also
provided for the handwritten expressions as XML files with the number of
symbols, and the MATHML structure.
Comparative study and enhancement of Camera Tampering Detection algorithms
Authors: Mabrouka Hagui, Mohamed Ali Mahjoub, Ahmed Boukhris
Date: 2016-08-08 00:00:00

Recently the use of video surveillance systems is widely increasing.
Different places are equipped by camera surveillances such as hospitals,
schools, airports, museums and military places in order to ensure the safety
and security of the persons and their property. Therefore it becomes
significant to guarantee the proper working of these systems. Intelligent video
surveillance systems equipped by sophisticated digital camera can analyze video
information s and automatically detect doubtful actions. The camera tampering
detection algorithms may indicate that accidental or suspicious activities have
occurred and that causes the abnormality works of the video surveillance.
  Camera Tampering Detection uses several techniques based on image processing
and computer vision. In this paper, comparative study of performance of three
algorithms that can detect abnormal disturbance for video surveillance is
presented.
A combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation
Authors: Mahaman Sani Chaibou, Karim Kalti, Bassel Soulaiman, Mohamed Ali Mahjoub
Date: 2016-08-08 00:00:00
Tasks: General Classification, Image Segmentation, Segmentation, Semantic Segmentation

We present in this paper an image segmentation approach that combines a fuzzy
semantic region classification and a context based region-growing. Input image
is first over-segmented. Then, prior domain knowledge is used to perform a
fuzzy classification of these regions to provide a fuzzy semantic labeling.
This allows the proposed approach to operate at high level instead of using
low-level features and consequently to remedy to the problem of the semantic
gap. Each over-segmented region is represented by a vector giving its
corresponding membership degrees to the different thematic labels and the whole
image is therefore represented by a Regions Partition Matrix. The segmentation
is achieved on this matrix instead of the image pixels through two main phases:
focusing and propagation. The focusing aims at selecting seeds regions from
which information propagation will be performed. Thepropagation phase allows to
spread toward others regions and using fuzzy contextual information the needed
knowledge ensuring the semantic segmentation. An application of the proposed
approach on mammograms shows promising results
Learning Joint Representations of Videos and Sentences with Web Image Search
Authors: Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Naokazu Yokoya
Date: 2016-08-08 00:00:00
Tasks: Image Retrieval, Natural Language Queries, Retrieval, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Retrieval, Video Retrieval

Our objective is video retrieval based on natural language queries. In
addition, we consider the analogous problem of retrieving sentences or
generating descriptions given an input video. Recent work has addressed the
problem by embedding visual and textual inputs into a common space where
semantic similarities correlate to distances. We also adopt the embedding
approach, and make the following contributions: First, we utilize web image
search in sentence embedding process to disambiguate fine-grained visual
concepts. Second, we propose embedding models for sentence, image, and video
inputs whose parameters are learned simultaneously. Finally, we show how the
proposed model can be applied to description generation. Overall, we observe a
clear improvement over the state-of-the-art methods in the video and sentence
retrieval tasks. In description generation, the performance level is comparable
to the current state-of-the-art, although our embeddings were trained for the
retrieval tasks.
Convergence radius and sample complexity of ITKM algorithms for dictionary learning
Authors: Karin Schnass
Date: 2015-03-24 00:00:00
Tasks: Dictionary Learning, valid

In this work we show that iterative thresholding and K-means (ITKM)
algorithms can recover a generating dictionary with K atoms from noisy $S$
sparse signals up to an error $\tilde \varepsilon$ as long as the
initialisation is within a convergence radius, that is up to a $\log K$ factor
inversely proportional to the dynamic range of the signals, and the sample size
is proportional to $K \log K \tilde \varepsilon^{-2}$. The results are valid
for arbitrary target errors if the sparsity level is of the order of the square
root of the signal dimension $d$ and for target errors down to $K^{-\ell}$ if
$S$ scales as $S \leq d/(\ell \log K)$.
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling
Authors: Seppo Enarvi, Mikko Kurimo
Date: 2016-05-03 00:00:00
Tasks: CPU, English Conversational Speech Recognition, GPU, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present a new tool for training neural network language models (NNLMs),
scoring sentences, and generating text. The tool has been written using Python
library Theano, which allows researcher to easily extend it and tune any aspect
of the training process. Regardless of the flexibility, Theano is able to
generate extremely fast native code that can utilize a GPU or multiple CPU
cores in order to parallelize the heavy numerical computations. The tool has
been evaluated in difficult Finnish and English conversational speech
recognition tasks, and significant improvement was obtained over our best
back-off n-gram models. The results that we obtained in the Finnish task were
compared to those from existing RNNLM and RWTHLM toolkits, and found to be as
good or better, while training times were an order of magnitude shorter.
Towards Representation Learning with Tractable Probabilistic Models
Authors: Antonio Vergari, Nicola Di Mauro, Floriana Esposito
Date: 2016-08-08 00:00:00
Tasks: Representation Learning

Probabilistic models learned as density estimators can be exploited in
representation learning beside being toolboxes used to answer inference queries
only. However, how to extract useful representations highly depends on the
particular model involved. We argue that tractable inference, i.e. inference
that can be computed in polynomial time, can enable general schemes to extract
features from black box models. We plan to investigate how Tractable
Probabilistic Models (TPMs) can be exploited to generate embeddings by random
query evaluations. We devise two experimental designs to assess and compare
different TPMs as feature extractors in an unsupervised representation learning
framework. We show some experimental results on standard image datasets by
applying such a method to Sum-Product Networks and Mixture of Trees as
tractable models generating embeddings.
SANTIAGO: Spine Association for Neuron Topology Improvement and Graph Optimization
Authors: William Gray Roncal, Colin Lea, Akira Baruah, Gregory D. Hager
Date: 2016-08-08 00:00:00
Tasks: valid

Developing automated and semi-automated solutions for reconstructing wiring
diagrams of the brain from electron micrographs is important for advancing the
field of connectomics. While the ultimate goal is to generate a graph of neuron
connectivity, most prior automated methods have focused on volume segmentation
rather than explicit graph estimation. In these approaches, one of the key,
commonly occurring error modes is dendritic shaft-spine fragmentation.
  We posit that directly addressing this problem of connection identification
may provide critical insight into estimating more accurate brain graphs. To
this end, we develop a network-centric approach motivated by biological priors
image grammars. We build a computer vision pipeline to reconnect fragmented
spines to their parent dendrites using both fully-automated and semi-automated
approaches. Our experiments show we can learn valid connections despite
uncertain segmentation paths. We curate the first known reference dataset for
analyzing the performance of various spine-shaft algorithms and demonstrate
promising results that recover many previously lost connections. Our automated
approach improves the local subgraph score by more than four times and the full
graph score by 60 percent. These data, results, and evaluation tools are all
available to the broader scientific community. This reframing of the
connectomics problem illustrates a semantic, biologically inspired solution to
remedy a major problem with neuron tracking.
Uncovering Voice Misuse Using Symbolic Mismatch
Authors: Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag
Date: 2016-08-08 00:00:00

Voice disorders affect an estimated 14 million working-aged Americans, and
many more worldwide. We present the first large scale study of vocal misuse
based on long-term ambulatory data collected by an accelerometer placed on the
neck. We investigate an unsupervised data mining approach to uncovering latent
information about voice misuse.
  We segment signals from over 253 days of data from 22 subjects into over a
hundred million single glottal pulses (closures of the vocal folds), cluster
segments into symbols, and use symbolic mismatch to uncover differences between
patients and matched controls, and between patients pre- and post-treatment.
Our results show significant behavioral differences between patients and
controls, as well as between some pre- and post-treatment patients. Our
proposed approach provides an objective basis for helping diagnose behavioral
voice disorders, and is a first step towards a more data-driven understanding
of the impact of voice therapy.
Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors
Authors: Yuqing Hou
Date: 2015-08-29 00:00:00
Tasks: Image Retrieval, Management, Retrieval, TAG

Tag-based image retrieval (TBIR) has drawn much attention in recent years due
to the explosive amount of digital images and crowdsourcing tags. However, TBIR
is still suffering from the incomplete and inaccurate tags provided by users,
posing a great challenge for tag-based image management applications. In this
work, we proposed a novel method for image annotation, incorporating several
priors: Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors.
Highly representative CNN feature vectors are adopt to model the tag-visual
correlation and narrow the semantic gap. And we extract word vectors for tags
to measure similarity between tags in the semantic level, which is more
accurate than traditional frequency-based or graph-based methods. We utilize
the accelerated proximal gradient (APG) method to solve our model efficiently.
Extensive experiments conducted on multiple benchmark datasets demonstrate the
effectiveness and robustness of the proposed method.
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
Authors: Yuqing Hou, Zhouchen Lin
Date: 2015-06-10 00:00:00
Tasks: Clustering, Image Retrieval, Matrix Completion, Retrieval, TAG

Tag-based image retrieval (TBIR) has drawn much attention in recent years due
to the explosive amount of digital images and crowdsourcing tags. However, the
TBIR applications still suffer from the deficient and inaccurate tags provided
by users. Inspired by the subspace clustering methods, we formulate the tag
completion problem in a subspace clustering model which assumes that images are
sampled from subspaces, and complete the tags using the state-of-the-art Low
Rank Representation (LRR) method. And we propose a matrix completion algorithm
to further refine the tags. Our empirical results on multiple benchmark
datasets for image annotation show that the proposed algorithm outperforms
state-of-the-art approaches when handling missing and noisy tags.