pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Differentiable Proving
Authors: Tim Rocktäschel, Sebastian Riedel
Date: 2017-05-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Link Prediction

We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules.
Learning with Biased Complementary Labels
Authors: Xiyu Yu, Tongliang Liu, Mingming Gong, DaCheng Tao
Date: 2017-11-27 00:00:00
Proceeding: ECCV 2018 9

In this paper, we study the classification problem in which we have access to
easily obtainable surrogate for true labels, namely complementary labels, which
specify classes that observations do \textbf{not} belong to. Let $Y$ and
$\bar{Y}$ be the true and complementary labels, respectively. We first model
the annotation of complementary labels via transition probabilities
$P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of
classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall
i\neq j$, are identical, which is not true in practice because humans are
biased toward their own experience. For example, as shown in Figure 1, if an
annotator is more familiar with monkeys than prairie dogs when providing
complementary labels for meerkats, she is more likely to employ "monkey" as a
complementary label. We therefore reason that the transition probabilities will
be different. In this paper, we propose a framework that contributes three main
innovations to learning with \textbf{biased} complementary labels: (1) It
estimates transition probabilities with no bias. (2) It provides a general
method to modify traditional loss functions and extends standard deep neural
network classifiers to learn with biased complementary labels. (3) It
theoretically ensures that the classifier learned with complementary labels
converges to the optimal one learned with true labels. Comprehensive
experiments on several benchmark datasets validate the superiority of our
method to current state-of-the-art methods.
In folly ripe. In reason rotten. Putting machine theology to rest
Authors: Mihai Nadin
Date: 2017-12-03 00:00:00

Computation has changed the world more than any previous expressions of
knowledge. In its particular algorithmic embodiment, it offers a perspective,
within which the digital computer (one of many possible) exercises a role
reminiscent of theology. Since it is closed to meaning, algorithmic digital
computation can at most mimic the creative aspects of life. AI, in the
perspective of time, proved to be less an acronym for artificial intelligence
and more of automating tasks associated with intelligence. The entire
development led to the hypostatized role of the machine: outputting nothing
else but reality, including that of the humanity that made the machine happen.
The convergence machine called deep learning is only the latest form through
which the deterministic theology of the machine claims more than what extremely
effective data processing actually is. A new understanding of complexity, as
well as the need to distinguish between the reactive nature of the artificial
and the anticipatory nature of the living are suggested as practical responses
to the challenges posed by machine theology.
A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity
Authors: Aixiang Chen, Bingchuan Chen, Xiaolong Chai, Rui Bian, Hengguang Li
Date: 2017-10-21 00:00:00

SGD (Stochastic Gradient Descent) is a popular algorithm for large scale
optimization problems due to its low iterative cost. However, SGD can not
achieve linear convergence rate as FGD (Full Gradient Descent) because of the
inherent gradient variance. To attack the problem, mini-batch SGD was proposed
to get a trade-off in terms of convergence rate and iteration cost. In this
paper, a general CVI (Convergence-Variance Inequality) equation is presented to
state formally the interaction of convergence rate and gradient variance. Then
a novel algorithm named SSAG (Stochastic Stratified Average Gradient) is
introduced to reduce gradient variance based on two techniques, stratified
sampling and averaging over iterations that is a key idea in SAG (Stochastic
Average Gradient). Furthermore, SSAG can achieve linear convergence rate of
$\mathcal {O}((1-\frac{\mu}{8CL})^k)$ at smaller storage and iterative costs,
where $C\geq 2$ is the category number of training data. This convergence rate
depends mainly on the variance between classes, but not on the variance within
the classes. In the case of $C\ll N$ ($N$ is the training data size), SSAG's
convergence rate is much better than SAG's convergence rate of $\mathcal
{O}((1-\frac{\mu}{8NL})^k)$. Our experimental results show SSAG outperforms SAG
and many other algorithms.
From Images to 3D Shape Attributes
Authors: David F. Fouhey, Abhinav Gupta, Andrew Zisserman
Date: 2016-12-20 00:00:00

Our goal in this paper is to investigate properties of 3D shape that can be
determined from a single image. We define 3D shape attributes -- generic
properties of the shape that capture curvature, contact and occupied space. Our
first objective is to infer these 3D shape attributes from a single image. A
second objective is to infer a 3D shape embedding -- a low dimensional vector
representing the 3D shape.
  We study how the 3D shape attributes and embedding can be obtained from a
single image by training a Convolutional Neural Network (CNN) for this task. We
start with synthetic images so that the contribution of various cues and
nuisance parameters can be controlled. We then turn to real images and
introduce a large scale image dataset of sculptures containing 143K images
covering 2197 works from 242 artists.
  For the CNN trained on the sculpture dataset we show the following: (i) which
regions of the imaged sculpture are used by the CNN to infer the 3D shape
attributes; (ii) that the shape embedding can be used to match previously
unseen sculptures largely independent of viewpoint; and (iii) that the 3D
attributes generalize to images of other (non-sculpture) object classes.
Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection
Authors: Kyle Hundman, Thamme Gowda, Mayank Kejriwal, Benedikt Boecking
Date: 2017-12-03 00:00:00
Tasks: Decision Making

Web-based human trafficking activity has increased in recent years but it
remains sparsely dispersed among escort advertisements and difficult to
identify due to its often-latent nature. The use of intelligent systems to
detect trafficking can thus have a direct impact on investigative resource
allocation and decision-making, and, more broadly, help curb a widespread
social problem. Trafficking detection involves assigning a normalized score to
a set of escort advertisements crawled from the Web -- a higher score indicates
a greater risk of trafficking-related (involuntary) activities. In this paper,
we define and study the problem of trafficking detection and present a
trafficking detection pipeline architecture developed over three years of
research within the DARPA Memex program. Drawing on multi-institutional data,
systems, and experiences collected during this time, we also conduct post hoc
bias analyses and present a bias mitigation plan. Our findings show that, while
automatic trafficking detection is an important application of AI for social
good, it also provides cautionary lessons for deploying predictive machine
learning algorithms without appropriate de-biasing. This ultimately led to
integration of an interpretable solution into a search system that contains
over 100 million advertisements and is used by over 200 law enforcement
agencies to investigate leads.
Large-scale analysis of disease pathways in the human interactome
Authors: Monica Agrawal, Marinka Zitnik, Jure Leskovec
Date: 2017-12-03 00:00:00
Tasks: Prognosis

Discovering disease pathways, which can be defined as sets of proteins
associated with a given disease, is an important problem that has the potential
to provide clinically actionable insights for disease diagnosis, prognosis, and
treatment. Computational methods aid the discovery by relying on
protein-protein interaction (PPI) networks. They start with a few known
disease-associated proteins and aim to find the rest of the pathway by
exploring the PPI network around the known disease proteins. However, the
success of such methods has been limited, and failure cases have not been well
understood. Here we study the PPI network structure of 519 disease pathways. We
find that 90% of pathways do not correspond to single well-connected components
in the PPI network. Instead, proteins associated with a single disease tend to
form many separate connected components/regions in the network. We then
evaluate state-of-the-art disease pathway discovery methods and show that their
performance is especially poor on diseases with disconnected pathways. Thus, we
conclude that network connectivity structure alone may not be sufficient for
disease pathway discovery. However, we show that higher-order network
structures, such as small subgraphs of the pathway, provide a promising
direction for the development of new methods.
An Error Detection and Correction Framework for Connectomics
Authors: Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung
Date: 2017-08-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 3D Reconstruction, Image Segmentation, Object, Semantic Segmentation

We define and study error detection and correction tasks that are useful for
3D reconstruction of neurons from electron microscopic imagery, and for image
segmentation more generally. Both tasks take as input the raw image and a
binary mask representing a candidate object. For the error detection task, the
desired output is a map of split and merge errors in the object. For the error
correction task, the desired output is the true object. We call this object
mask pruning, because the candidate object mask is assumed to be a superset of
the true object. We train multiscale 3D convolutional networks to perform both
tasks. We find that the error-detecting net can achieve high accuracy. The
accuracy of the error-correcting net is enhanced if its input object mask is
"advice" (union of erroneous objects) from the error-detecting net.
Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects
Authors: Jakob Suchan, Mehul Bhatt, Przemysław Wałęga, Carl Schultz
Date: 2017-12-03 00:00:00
Tasks: Object Tracking

We propose a hybrid architecture for systematically computing robust visual
explanation(s) encompassing hypothesis formation, belief revision, and default
reasoning with video data. The architecture consists of two tightly integrated
synergistic components: (1) (functional) answer set programming based abductive
reasoning with space-time tracklets as native entities; and (2) a visual
processing pipeline for detection based object tracking and motion analysis.
  We present the formal framework, its general implementation as a
(declarative) method in answer set programming, and an example application and
evaluation based on two diverse video datasets: the MOTChallenge benchmark
developed by the vision community, and a recently developed Movie Dataset.
A Variational Approach to Shape-from-shading Under Natural Illumination
Authors: Yvain Quéau, Jean Mélou, Fabien Castan, Daniel Cremers, Jean-Denis Durou
Date: 2017-09-29 00:00:00
Tasks: Denoising

A numerical solution to shape-from-shading under natural illumination is
presented. It builds upon an augmented Lagrangian approach for solving a
generic PDE-based shape-from-shading model which handles directional or
spherical harmonic lighting, orthographic or perspective projection, and
greylevel or multi-channel images. Real-world applications to shading-aware
depth map denoising, refinement and completion are presented.
Informational Neurobayesian Approach to Neural Networks Training. Opportunities and Prospects
Authors: Artem Artemov, Eugeny Lutsenko, Edward Ayunts, Ivan Bolokhov
Date: 2017-10-19 00:00:00
Tasks: General Classification

A study of the classification problem in context of information theory is
presented in the paper. Current research in that field is focused on
optimisation and bayesian approach. Although that gives satisfying results,
they require a vast amount of data and computations to train on. Authors
propose a new concept named Informational Neurobayesian Approach (INA), which
allows to solve the same problems, but requires significantly less training
data as well as computational power. Experiments were conducted to compare its
performance with the traditional one and the results showed that capacity of
the INA is quite promising.
Avaliação da doença de Alzheimer pela análise multiespectral de imagens DW-MR por redes RBF como alternativa aos mapas ADC
Authors: Wellington Pinheiro dos Santos, Ricardo Emmanuel de Souza, Ascendino Flávio Dias e Silva, Plínio Batista dos Santos Filho
Date: 2017-12-03 00:00:00
Tasks: Classification, General Classification

Alzheimer's disease is the most common cause of dementia, yet difficult to
accurately diagnose without the use of invasive techniques, particularly at the
beginning of the disease. This work addresses the classification and analysis
of multispectral synthetic images composed by diffusion-weighted magnetic
resonance brain volumes for evaluation of the area of cerebrospinal fluid and
its correlation with the progression of Alzheimer's disease. A 1.5 T MR imaging
system was used to acquire all the images presented. The classification methods
are based on multilayer perceptrons and classifiers of radial basis function
networks. It is assumed that the classes of interest can be separated by
hyperquadrics. A polynomial network of degree 2 is used to classify the
original volumes, generating a ground-truth volume. The classification results
are used to improve the usual analysis by the map of apparent diffusion
coefficients.
Semi-Global Stereo Matching with Surface Orientation Priors
Authors: Daniel Scharstein, Tatsunori Taniai, Sudipta N. Sinha
Date: 2017-12-03 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.
Dialectical Multispectral Classification of Diffusion-Weighted Magnetic Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to Perform Anatomical Analysis
Authors: Wellington Pinheiro dos Santos, Francisco Marcos de Assis, Ricardo Emmanuel de Souza, Plínio Batista dos Santos Filho, Fernando Buarque de Lima Neto
Date: 2017-12-03 00:00:00
Tasks: General Classification, Philosophy

Multispectral image analysis is a relatively promising field of research with
applications in several areas, such as medical imaging and satellite
monitoring. A considerable number of current methods of analysis are based on
parametric statistics. Alternatively, some methods in Computational
Intelligence are inspired by biology and other sciences. Here we claim that
Philosophy can be also considered as a source of inspiration. This work
proposes the Objective Dialectical Method (ODM): a method for classification
based on the Philosophy of Praxis. ODM is instrumental in assembling evolvable
mathematical tools to analyze multispectral images. In the case study described
in this paper, multispectral images are composed of diffusion-weighted (DW)
magnetic resonance (MR) images. The results are compared to ground-truth images
produced by polynomial networks using a morphological similarity index. The
classification results are used to improve the usual analysis of the apparent
diffusion coefficient map. Such results proved that gray and white matter can
be distinguished in DW-MR multispectral analysis and, consequently, DW-MR
images can also be used to furnish anatomical information.
Avaliação do método dialético na quantização de imagens multiespectrais
Authors: Wellington Pinheiro dos Santos, Francisco Marcos de Assis
Date: 2017-12-03 00:00:00
Tasks: Philosophy, Quantization

The unsupervised classification has a very important role in the analysis of
multispectral images, given its ability to assist the extraction of a priori
knowledge of images. Algorithms like k-means and fuzzy c-means has long been
used in this task. Computational Intelligence has proven to be an important
field to assist in building classifiers optimized according to the quality of
the grouping of classes and the evaluation of the quality of vector
quantization. Several studies have shown that Philosophy, especially the
Dialectical Method, has served as an important inspiration for the construction
of new computational methods. This paper presents an evaluation of four methods
based on the Dialectics: the Objective Dialectical Classifier and the
Dialectical Optimization Method adapted to build a version of k-means with
optimal quality indices; each of them is presented in two versions: a canonical
version and another version obtained by applying the Principle of Maximum
Entropy. These methods were compared to k-means, fuzzy c-means and Kohonen's
self-organizing maps. The results showed that the methods based on Dialectics
are robust to noise, and quantization can achieve results as good as those
obtained with the Kohonen map, considered an optimal quantizer.
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões
Authors: Higor Neto Lima, Wellington Pinheiro dos Santos, Mêuser Jorge Silva Valença
Date: 2017-12-03 00:00:00
Tasks: Classification, Content-Based Image Retrieval, feature selection, General Classification, image-classification, Image Classification, Image Retrieval, Quantization, Retrieval

The importance of organizing medical images according to their nature,
application and relevance is increasing. Furhermore, a previous selection of
medical images can be useful to accelerate the task of analysis by
pathologists. Herein this work we propose an image classifier to integrate a
CBIR (Content-Based Image Retrieval) selection system. This classifier is based
on pattern spectra and neural networks. Feature selection is performed using
pattern spectra and principal component analysis, whilst image classification
is based on multilayer perceptrons and a composition of self-organizing maps
and learning vector quantization. These methods were applied for content
selection of immunohistochemical images of placenta and newdeads lungs. Results
demonstrated that this approach can reach reasonable classification
performance.
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering
Authors: Wellington Pinheiro dos Santos, Francisco Marcos de Assis, Ricardo Emmanuel de Souza, Priscilla B. Mendes, Henrique S. S. Monteiro, Havana Diogo Alves
Date: 2017-12-03 00:00:00
Tasks: Classification, Clustering, General Classification, image-classification, Image Classification, Philosophy, Quantization

The materialist dialectical method is a philosophical investigative method to
analyze aspects of reality. These aspects are viewed as complex processes
composed by basic units named poles, which interact with each other. Dialectics
has experienced considerable progress in the 19th century, with Hegel's
dialectics and, in the 20th century, with the works of Marx, Engels, and
Gramsci, in Philosophy and Economics. The movement of poles through their
contradictions is viewed as a dynamic process with intertwined phases of
evolution and revolutionary crisis. In order to build a computational process
based on dialectics, the interaction between poles can be modeled using fuzzy
membership functions. Based on this assumption, we introduce the Objective
Dialectical Classifier (ODC), a non-supervised map for classification based on
materialist dialectics and designed as an extension of fuzzy c-means
classifier. As a case study, we used ODC to classify 181 magnetic resonance
synthetic multispectral images composed by proton density, $T_1$- and
$T_2$-weighted synthetic brain images. Comparing ODC to k-means, fuzzy c-means,
and Kohonen's self-organized maps, concerning with image fidelity indexes as
estimatives of quantization distortion, we proved that ODC can reach almost the
same quantization performance as optimal non-supervised classifiers like
Kohonen's self-organized maps.
A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images
Authors: Filipe Rolim Cordeiro, Wellington Pinheiro dos Santos, Abel Guilhermino da Silva Filho
Date: 2017-12-03 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

According to the World Health Organization, breast cancer is the most common
form of cancer in women. It is the second leading cause of death among women
round the world, becoming the most fatal form of cancer. Mammographic image
segmentation is a fundamental task to support image analysis and diagnosis,
taking into account shape analysis of mammary lesions and their borders.
However, mammogram segmentation is a very hard process, once it is highly
dependent on the types of mammary tissues. In this work we present a new
semi-supervised segmentation algorithm based on the modification of the GrowCut
algorithm to perform automatic mammographic image segmentation once a region of
interest is selected by a specialist. In our proposal, we used fuzzy Gaussian
membership functions to modify the evolution rule of the original GrowCut
algorithm, in order to estimate the uncertainty of a pixel being object or
background. The main impact of the proposed method is the significant reduction
of expert effort in the initialization of seed points of GrowCut to perform
accurate segmentation, once it removes the need of selection of background
seeds. We also constructed an automatic point selection process based on the
simulated annealing optimization method, avoiding the need of human
intervention. The proposed approach was qualitatively compared with other
state-of-the-art segmentation techniques, considering the shape of segmented
regions. In order to validate our proposal, we built an image classifier using
a classical multilayer perceptron. We used Zernike moments to extract segmented
image features. This analysis employed 685 mammograms from IRMA breast cancer
database, using fat and fibroid tissues. Results show that the proposed
technique could achieve a classification rate of 91.28\% for fat tissues,
evidencing the feasibility of our approach.