pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Medical Image Processing: Overview, Challenges and Future
Authors: Muhammad Imran Razzak, Saeeda Naz, Ahmad Zaib
Date: 2017-04-22 00:00:00
Tasks: Deep Learning, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Healthcare sector is totally different from other industry. It is on high
priority sector and people expect highest level of care and services regardless
of cost. It did not achieve social expectation even though it consume huge
percentage of budget. Mostly the interpretations of medical data is being done
by medical expert. In terms of image interpretation by human expert, it is
quite limited due to its subjectivity, the complexity of the image, extensive
variations exist across different interpreters, and fatigue. After the success
of deep learning in other real world application, it is also providing exciting
solutions with good accuracy for medical imaging and is seen as a key method
for future applications in health secotr. In this chapter, we discussed state
of the art deep learning architecture and its optimization used for medical
image segmentation and classification. In the last section, we have discussed
the challenges deep learning based methods for medical imaging and open
research issue.
Deep Learning based Isolated Arabic Scene Character Recognition
Authors: Saad Bin Ahmed, Saeeda Naz, Muhammad Imran Razzak, Rubiyah Yousaf
Date: 2017-04-22 00:00:00
Tasks: Deep Learning, Scene Text Recognition, text-classification, Text Classification

The technological advancement and sophistication in cameras and gadgets
prompt researchers to have focus on image analysis and text understanding. The
deep learning techniques demonstrated well to assess the potential for
classifying text from natural scene images as reported in recent years. There
are variety of deep learning approaches that prospects the detection and
recognition of text, effectively from images. In this work, we presented Arabic
scene text recognition using Convolutional Neural Networks (ConvNets) as a deep
learning classifier. As the scene text data is slanted and skewed, thus to deal
with maximum variations, we employ five orientations with respect to single
occurrence of a character. The training is formulated by keeping filter size 3
x 3 and 5 x 5 with stride value as 1 and 2. During text classification phase,
we trained network with distinct learning rates. Our approach reported
encouraging results on recognition of Arabic characters from segmented Arabic
scene images.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Authors: Federico Monti, Michael M. Bronstein, Xavier Bresson
Date: 2017-04-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Collaborative Filtering, Matrix Completion, Recommendation Systems

Matrix completion models are among the most common formulations of
recommender systems. Recent works have showed a boost of performance of these
techniques when introducing the pairwise relationships between users/items in
the form of graphs, and imposing smoothness priors on these graphs. However,
such techniques do not fully exploit the local stationarity structures of
user/item graphs, and the number of parameters to learn is linear w.r.t. the
number of users and items. We propose a novel approach to overcome these
limitations by using geometric deep learning on graphs. Our matrix completion
architecture combines graph convolutional neural networks and recurrent neural
networks to learn meaningful statistical graph-structured patterns and the
non-linear diffusion process that generates the known ratings. This neural
network system requires a constant number of parameters independent of the
matrix size. We apply our method on both synthetic and real datasets, showing
that it outperforms state-of-the-art techniques.
Efficient variational Bayesian neural network ensembles for outlier detection
Authors: Nick Pawlowski, Miguel Jaques, Ben Glocker
Date: 2017-03-20 00:00:00
Tasks: Outlier Detection

In this work we perform outlier detection using ensembles of neural networks
obtained by variational approximation of the posterior in a Bayesian neural
network setting. The variational parameters are obtained by sampling from the
true posterior by gradient descent. We show our outlier detection results are
comparable to those obtained using other efficient ensembling methods.
Neural End-to-End Learning for Computational Argumentation Mining
Authors: Steffen Eger, Johannes Daxenberger, Iryna Gurevych
Date: 2017-04-20 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, General Classification, Multi-Task Learning

We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as dependency parsing leads to subpar performance results. In
contrast, less complex (local) tagging models based on BiLSTMs perform robustly
across classification scenarios, being able to catch long-range dependencies
inherent to the AM problem. Moreover, we find that jointly learning 'natural'
subtasks, in a multi-task learning setup, improves performance.
Cohomology of Cryo-Electron Microscopy
Authors: Ke Ye, Lek-Heng Lim
Date: 2016-04-05 00:00:00

The goal of cryo-electron microscopy (EM) is to reconstruct the 3-dimensional
structure of a molecule from a collection of its 2-dimensional projected
images. In this article, we show that the basic premise of cryo-EM --- patching
together 2-dimensional projections to reconstruct a 3-dimensional object --- is
naturally one of Cech cohomology with SO(2)-coefficients. We deduce that every
cryo-EM reconstruction problem corresponds to an oriented circle bundle on a
simplicial complex, allowing us to classify cryo-EM problems via principal
bundles. In practice, the 2-dimensional images are noisy and a main task in
cryo-EM is to denoise them. We will see how the aforementioned insights can be
used towards this end.
Lexical Features in Coreference Resolution: To be Used With Caution
Authors: Nafise Sadat Moosavi, Michael Strube
Date: 2017-04-22 00:00:00
Proceeding: ACL 2017 7
Tasks: coreference-resolution, Coreference Resolution

Lexical features are a major source of information in state-of-the-art
coreference resolvers. Lexical features implicitly model some of the linguistic
phenomena at a fine granularity level. They are especially useful for
representing the context of mentions. In this paper we investigate a drawback
of using many lexical features in state-of-the-art coreference resolvers. We
show that if coreference resolvers mainly rely on lexical features, they can
hardly generalize to unseen domains. Furthermore, we show that the current
coreference resolution evaluation is clearly flawed by only evaluating on a
specific split of a specific dataset in which there is a notable overlap
between the training, development and test sets.
Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing
Authors: Hamed Masnadi-Shirazi
Date: 2017-04-09 00:00:00
Tasks: One-class classifier, scoring rule, Two-sample testing

We study strictly proper scoring rules in the Reproducing Kernel Hilbert
Space. We propose a general Kernel Scoring rule and associated Kernel
Divergence. We consider conditions under which the Kernel Score is strictly
proper. We then demonstrate that the Kernel Score includes the Maximum Mean
Discrepancy as a special case. We also consider the connections between the
Kernel Score and the minimum risk of a proper loss function. We show that the
Kernel Score incorporates more information pertaining to the projected embedded
distributions compared to the Maximum Mean Discrepancy. Finally, we show how to
integrate the information provided from different Kernel Divergences, such as
the proposed Bhattacharyya Kernel Divergence, using a one-class classifier for
improved two-sample hypothesis testing results.
Controlling Recurrent Neural Networks by Conceptors
Authors: Herbert Jaeger
Date: 2014-03-13 00:00:00
Tasks: MORPH

The human brain is a dynamical system whose extremely complex sensor-driven neural processes give rise to conceptual, logical cognition. Understanding the interplay between nonlinear neural dynamics and concept-level cognition remains a major scientific challenge. Here I propose a mechanism of neurodynamical organization, called conceptors, which unites nonlinear dynamics with basic principles of conceptual abstraction and logic. It becomes possible to learn, store, abstract, focus, morph, generalize, de-noise and recognize a large number of dynamical patterns within a single neural system; novel patterns can be added without interfering with previously acquired ones; neural noise is automatically filtered. Conceptors help explaining how conceptual-level information processing emerges naturally and robustly in neural systems, and remove a number of roadblocks in the theory and applications of recurrent neural networks.
Convolutional Neural Networks for Facial Expression Recognition
Authors: Shima Alizadeh, Azar Fazel
Date: 2017-04-22 00:00:00
Tasks: Facial Expression Recognition (FER), GPU, L2 Regularization

We have developed convolutional neural networks (CNN) for a facial expression
recognition task. The goal is to classify each facial image into one of the
seven facial emotion categories considered in this study. We trained CNN models
with different depth using gray-scale images. We developed our models in Torch
and exploited Graphics Processing Unit (GPU) computation in order to expedite
the training process. In addition to the networks performing based on raw pixel
data, we employed a hybrid feature strategy by which we trained a novel CNN
model with the combination of raw pixel data and Histogram of Oriented
Gradients (HOG) features. To reduce the overfitting of the models, we utilized
different techniques including dropout and batch normalization in addition to
L2 regularization. We applied cross validation to determine the optimal
hyper-parameters and evaluated the performance of the developed models by
looking at their training histories. We also present the visualization of
different layers of a network to show what features of a face can be learned by
CNN models.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Authors: Siyuan Qiao, Wei Shen, Weichao Qiu, Chenxi Liu, Alan Yuille
Date: 2017-04-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Object Proposal Generation

Motivated by product detection in supermarkets, this paper studies the
problem of object proposal generation in supermarket images and other natural
images. We argue that estimation of object scales in images is helpful for
generating object proposals, especially for supermarket images where object
scales are usually within a small range. Therefore, we propose to estimate
object scales of images before generating object proposals. The proposed method
for predicting object scales is called ScaleNet. To validate the effectiveness
of ScaleNet, we build three supermarket datasets, two of which are real-world
datasets used for testing and the other one is a synthetic dataset used for
training. In short, we extend the previous state-of-the-art object proposal
methods by adding a scale prediction phase. The resulted method outperforms the
previous state-of-the-art on the supermarket datasets by a large margin. We
also show that the approach works for object proposal on other natural images
and it outperforms the previous state-of-the-art object proposal methods on the
MS COCO dataset. The supermarket datasets, the virtual supermarkets, and the
tools for creating more synthetic datasets will be made public.
Discrete Variational Autoencoders
Authors: Jason Tyler Rolfe
Date: 2016-09-07 00:00:00

Probabilistic models with discrete latent variables naturally capture
datasets composed of discrete classes. However, they are difficult to train
efficiently, since backpropagation through discrete variables is generally not
possible. We present a novel method to train a class of probabilistic models
with discrete latent variables using the variational autoencoder framework,
including backpropagation through the discrete latent variables. The associated
class of probabilistic models comprises an undirected discrete component and a
directed hierarchical continuous component. The discrete component captures the
distribution over the disconnected smooth manifolds induced by the continuous
component. As a result, this class of models efficiently learns both the class
of objects in an image, and their specific realization in pixels, from
unsupervised data, and outperforms state-of-the-art methods on the
permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets.
On Face Segmentation, Face Swapping, and Face Perception
Authors: Yuval Nirkin, Iacopo Masi, Anh Tuan Tran, Tal Hassner, Gerard Medioni
Date: 2017-04-22 00:00:00
Tasks: Face Swapping

We show that even when face images are unconstrained and arbitrarily paired,
face swapping between them is actually quite simple. To this end, we make the
following contributions. (a) Instead of tailoring systems for face
segmentation, as others previously proposed, we show that a standard fully
convolutional network (FCN) can achieve remarkably fast and accurate
segmentations, provided that it is trained on a rich enough example set. For
this purpose, we describe novel data collection and generation routines which
provide challenging segmented face examples. (b) We use our segmentations to
enable robust face swapping under unprecedented conditions. (c) Unlike previous
work, our swapping is robust enough to allow for extensive quantitative tests.
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure
the effect of intra- and inter-subject face swapping on recognition. We show
that our intra-subject swapped faces remain as recognizable as their sources,
testifying to the effectiveness of our method. In line with well known
perceptual studies, we show that better face swapping produces less
recognizable inter-subject results. This is the first time this effect was
quantitatively demonstrated for machine vision systems.
The More You Know: Using Knowledge Graphs for Image Classification
Authors: Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta
Date: 2016-12-14 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, image-classification, Image Classification, Knowledge Graphs, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

One characteristic that sets humans apart from modern learning-based computer
vision algorithms is the ability to acquire knowledge about the world and use
that knowledge to reason about the visual world. Humans can learn about the
characteristics of objects and the relationships that occur between them to
learn a large variety of visual concepts, often with few examples. This paper
investigates the use of structured prior knowledge in the form of knowledge
graphs and shows that using this knowledge improves performance on image
classification. We build on recent work on end-to-end learning on graphs,
introducing the Graph Search Neural Network as a way of efficiently
incorporating large knowledge graphs into a vision classification pipeline. We
show in a number of experiments that our method outperforms standard neural
network baselines for multi-label classification.
A Survey on Learning to Hash
Authors: Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, Heng Tao Shen
Date: 2016-06-01 00:00:00
Tasks: Quantization, Survey

Nearest neighbor search is a problem of finding the data points from the
database such that the distances from them to the query point are the smallest.
Learning to hash is one of the major solutions to this problem and has been
widely studied recently. In this paper, we present a comprehensive survey of
the learning to hash algorithms, categorize them according to the manners of
preserving the similarities into: pairwise similarity preserving, multiwise
similarity preserving, implicit similarity preserving, as well as quantization,
and discuss their relations. We separate quantization from pairwise similarity
preserving as the objective function is very different though quantization, as
we show, can be derived from preserving the pairwise similarities. In addition,
we present the evaluation protocols, and the general performance analysis, and
point out that the quantization algorithms perform superiorly in terms of
search accuracy, search time cost, and space cost. Finally, we introduce a few
emerging topics.
On the energy landscape of deep networks
Authors: Pratik Chaudhari, Stefano Soatto
Date: 2015-11-20 00:00:00

We introduce "AnnealSGD", a regularized stochastic gradient descent algorithm
motivated by an analysis of the energy landscape of a particular class of deep
networks with sparse random weights. The loss function of such networks can be
approximated by the Hamiltonian of a spherical spin glass with Gaussian
coupling. While different from currently-popular architectures such as
convolutional ones, spin glasses are amenable to analysis, which provides
insights on the topology of the loss function and motivates algorithms to
minimize it. Specifically, we show that a regularization term akin to a
magnetic field can be modulated with a single scalar parameter to transition
the loss function from a complex, non-convex landscape with exponentially many
local minima, to a phase with a polynomial number of minima, all the way down
to a trivial landscape with a unique minimum. AnnealSGD starts training in the
relaxed polynomial regime and gradually tightens the regularization parameter
to steer the energy towards the original exponential regime. Even for
convolutional neural networks, which are quite unlike sparse random networks,
we empirically show that AnnealSGD improves the generalization error using
competitive baselines on MNIST and CIFAR-10.
A dynamic resource allocation decision model for IT security
Authors: Lotfi Hajjem, Salah Benabdallah, Fouad Ben Abdelaziz
Date: 2017-04-21 00:00:00

Today, with the continued growth in using information and communication
technologies (ICT) for business purposes, business organizations become
increasingly dependent on their information systems. Thus, they need to protect
them from the different attacks exploiting their vulnerabilities. To do so, the
organization has to use security technologies, which may be proactive or
reactive ones. Each security technology has a relative cost and addresses
specific vulnerabilities. Therefore, the organization has to put in place the
appropriate security technologies set that minimizes the information system s
vulnerabilities with a minimal cost. This bi objective problem will be
considered as a resources allocation problem (RAP) where security technologies
represent the resources to be allocated. However, the set of vulnerabilities
may change, periodically, with the continual appearance of new ones. Therefore,
the security technologies set should be flexible to face these changes, in real
time, and the problem becomes a dynamic one. In this paper, we propose a
harmony search based algorithm to solve the bi objective dynamic resource
allocation decision model. This approach was compared to a genetic algorithm
and provided good results.
Construction Inspection through Spatial Database
Authors: Ahmad Hasan, Ashraf Qadir, Ian Nordeng, Jeremiah Neubert
Date: 2016-11-11 00:00:00
Tasks: Simultaneous Localization and Mapping

This paper presents a novel pipeline for development of an efficient set of
tools for extracting information from the video of a structure, captured by an
Unmanned Aircraft System (UAS) to produce as-built documentation to aid
inspection of large multi-storied building during construction. Our system uses
the output from a Simultaneous Localization and Mapping system and a 3D CAD
model of the structure in order to construct a spatial database to store images
into the 3D CAD model space. This allows the user to perform a spatial query
for images through spatial indexing into the 3D CAD model space. The image
returned by the spatial query is used to extract metric information. The
spatial database is also used to generate a 3D textured model which provides a
visual as-built documentation.
Improving Semantic Composition with Offset Inference
Authors: Thomas Kober, Julie Weeds, Jeremy Reffin, David Weir
Date: 2017-04-21 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Composition

Count-based distributional semantic models suffer from sparsity due to
unobserved but plausible co-occurrences in any text collection. This problem is
amplified for models like Anchored Packed Trees (APTs), that take the
grammatical type of a co-occurrence into account. We therefore introduce a
novel form of distributional inference that exploits the rich type structure in
APTs and infers missing data by the same mechanism that is used for semantic
composition.
Scatteract: Automated extraction of data from scatter plots
Authors: Mathieu Cliche, David Rosenberg, Dhruv Madeka, Connie Yee
Date: 2017-04-21 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Charts are an excellent way to convey patterns and trends in data, but they
do not facilitate further modeling of the data or close inspection of
individual data points. We present a fully automated system for extracting the
numerical values of data points from images of scatter plots. We use deep
learning techniques to identify the key components of the chart, and optical
character recognition together with robust regression to map from pixels to the
coordinate system of the chart. We focus on scatter plots with linear scales,
which already have several interesting challenges. Previous work has done fully
automatic extraction for other types of charts, but to our knowledge this is
the first approach that is fully automatic for scatter plots. Our method
performs well, achieving successful data extraction on 89% of the plots in our
test set.
A hybrid exact-ACO algorithm for the joint scheduling, power and cluster assignment in cooperative wireless networks
Authors: Fabio D'Andreagiovanni
Date: 2017-04-21 00:00:00
Tasks: Scheduling

Base station cooperation (BSC) has recently arisen as a promising way to
increase the capacity of a wireless network. Implementing BSC adds a new design
dimension to the classical wireless network design problem: how to define the
subset of base stations (clusters) that coordinate to serve a user. Though the
problem of forming clusters has been extensively discussed from a technical
point of view, there is still a lack of effective optimization models for its
representation and algorithms for its solution. In this work, we make a further
step towards filling such gap: 1) we generalize the classical network design
problem by adding cooperation as an additional decision dimension; 2) we
develop a strong formulation for the resulting problem; 3) we define a new
hybrid solution algorithm that combines exact large neighborhood search and ant
colony optimization. Finally, we assess the performance of our new model and
algorithm on a set of realistic instances of a WiMAX network.