pwc-archive/papers-with-abstracts huggingface dataset:
Generalized BackPropagation, Étude De Cas: Orthogonality
Authors: Mehrtash Harandi, Basura Fernando
Date: 2016-11-17 00:00:00
Tasks: Dimensionality Reduction, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

This paper introduces an extension of the backpropagation algorithm that
enables us to have layers with constrained weights in a deep network. In
particular, we make use of the Riemannian geometry and optimization techniques
on matrix manifolds to step outside of normal practice in training deep
networks, equipping the network with structures such as orthogonality or
positive definiteness. Based on our development, we make another contribution
by introducing the Stiefel layer, a layer with orthogonal weights. Among
various applications, Stiefel layers can be used to design orthogonal filter
banks, perform dimensionality reduction and feature extraction. We demonstrate
the benefits of having orthogonality in deep networks through a broad set of
experiments, ranging from unsupervised feature learning to fine-grained image
classification.
Generative One-Class Models for Text-based Person Retrieval in Forensic Applications
Authors: David Gerónimo, Hedvig Kjellström
Date: 2016-11-17 00:00:00
Tasks: Person Retrieval, Retrieval, Text based Person Retrieval, Text-based Person Retrieval

Automatic forensic image analysis assists criminal investigation experts in
the search for suspicious persons, abnormal behaviors detection and identity
matching in images. In this paper we propose a person retrieval system that
uses textual queries (e.g., "black trousers and green shirt") as descriptions
and a one-class generative color model with outlier filtering to represent the
images both to train the models and to perform the search. The method is
evaluated in terms of its efficiency in fulfilling the needs of a forensic
retrieval system: limited annotation, robustness, extensibility, adaptability
and computational cost. The proposed generative method is compared to a
corresponding discriminative approach. Experiments are carried out using a
range of queries in three different databases. The experiments show that the
two evaluated algorithms provide average retrieval performance and adaptable to
new datasets. The proposed generative algorithm has some advantages over the
discriminative one, specifically its capability to work with very few training
samples and its much lower computational requirements when the number of
training examples increases.
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic
Authors: Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos
Date: 2016-11-17 00:00:00
Tasks: Activity Recognition, Question Answering

In this work, we explore a genre of puzzles ("image riddles") which involves
a set of images and a question. Answering these puzzles require both
capabilities involving visual detection (including object, activity
recognition) and, knowledge-based or commonsense reasoning. We compile a
dataset of over 3k riddles where each riddle consists of 4 images and a
groundtruth answer. The annotations are validated using crowd-sourced
evaluation. We also define an automatic evaluation metric to track future
progress. Our task bears similarity with the commonly known IQ tasks such as
analogy solving, sequence filling that are often used to test intelligence.
  We develop a Probabilistic Reasoning-based approach that utilizes
probabilistic commonsense knowledge to answer these riddles with a reasonable
accuracy. We demonstrate the results of our approach using both automatic and
human evaluations. Our approach achieves some promising results for these
riddles and provides a strong baseline for future attempts. We make the entire
dataset and related materials publicly available to the community in
ImageRiddle Website (http://bit.ly/22f9Ala).
Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey
Authors: D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabaly, C. Quek
Date: 2016-11-17 00:00:00
Tasks: Foreground Segmentation, Object, object-detection, Object Detection, Object Tracking, Survey

We present a survey on maritime object detection and tracking approaches,
which are essential for the development of a navigational system for autonomous
ships. The electro-optical (EO) sensor considered here is a video camera that
operates in the visible or the infrared spectra, which conventionally
complement radar and sonar and have demonstrated effectiveness for situational
awareness at sea has demonstrated its effectiveness over the last few years.
This paper provides a comprehensive overview of various approaches of video
processing for object detection and tracking in the maritime environment. We
follow an approach-based taxonomy wherein the advantages and limitations of
each approach are compared. The object detection system consists of the
following modules: horizon detection, static background subtraction and
foreground segmentation. Each of these has been studied extensively in maritime
situations and has been shown to be challenging due to the presence of
background motion especially due to waves and wakes. The main processes
involved in object tracking include video frame registration, dynamic
background subtraction, and the object tracking algorithm itself. The
challenges for robust tracking arise due to camera motion, dynamic background
and low contrast of tracked object, possibly due to environmental degradation.
The survey also discusses multisensor approaches and commercial maritime
systems that use EO sensors. The survey also highlights methods from computer
vision research which hold promise to perform well in maritime EO data
processing. Performance of several maritime and computer vision techniques is
evaluated on newly proposed Singapore Maritime Dataset.
AutoScaler: Scale-Attention Networks for Visual Correspondence
Authors: Shenlong Wang, Linjie Luo, Ning Zhang, Jia Li
Date: 2016-11-17 00:00:00
Tasks: Optical Flow Estimation

Finding visual correspondence between local features is key to many computer
vision problems. While defining features with larger contextual scales usually
implies greater discriminativeness, it could also lead to less spatial accuracy
of the features. We propose AutoScaler, a scale-attention network to explicitly
optimize this trade-off in visual correspondence tasks. Our network consists of
a weight-sharing feature network to compute multi-scale feature maps and an
attention network to combine them optimally in the scale space. This allows our
network to have adaptive receptive field sizes over different scales of the
input. The entire network is trained end-to-end in a siamese framework for
visual correspondence tasks. Our method achieves favorable results compared to
state-of-the-art methods on challenging optical flow and semantic matching
benchmarks, including Sintel, KITTI and CUB-2011. We also show that our method
can generalize to improve hand-crafted descriptors (e.g Daisy) on general
visual correspondence tasks. Finally, our attention network can generate
visually interpretable scale attention maps.
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
Authors: Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Date: 2016-11-17 00:00:00
Tasks: Interpretable Machine Learning

At the core of interpretable machine learning is the question of whether
humans are able to make accurate predictions about a model's behavior. Assumed
in this question are three properties of the interpretable output: coverage,
precision, and effort. Coverage refers to how often humans think they can
predict the model's behavior, precision to how accurate humans are in those
predictions, and effort is either the up-front effort required in interpreting
the model, or the effort required to make predictions about a model's behavior.
  In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that
produces high-precision rule-based explanations for which the coverage
boundaries are very clear. We compare aLIME to linear LIME with simulated
experiments, and demonstrate the flexibility of aLIME with qualitative examples
from a variety of domains and tasks.
Navigational Rule Derivation: An algorithm to determine the effect of traffic signs on road networks
Authors: Daniil Galaktionov, Miguel R. Luaces, Ángeles S. Places
Date: 2016-11-17 00:00:00

In this paper we present an algorithm to build a road network map enriched
with traffic rules such as one-way streets and forbidden turns, based on the
interpretation of already detected and classified traffic signs. Such algorithm
helps to automatize the elaboration of maps for commercial navigation systems.
Our solution is based on simulating navigation along the road network,
determining at each point of interest the visibility of the signs and their
effect on the roads. We test our approach in a small urban network and discuss
various ways to generalize it to support more complex environments.
The Freiburg Groceries Dataset
Authors: Philipp Jund, Nichola Abdo, Andreas Eitel, Wolfram Burgard
Date: 2016-11-17 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Object Recognition

With the increasing performance of machine learning techniques in the last
few years, the computer vision and robotics communities have created a large
number of datasets for benchmarking object recognition tasks. These datasets
cover a large spectrum of natural images and object categories, making them not
only useful as a testbed for comparing machine learning approaches, but also a
great resource for bootstrapping different domain-specific perception and
robotic systems. One such domain is domestic environments, where an autonomous
robot has to recognize a large variety of everyday objects such as groceries.
This is a challenging task due to the large variety of objects and products,
and where there is great need for real-world training data that goes beyond
product images available online. In this paper, we address this issue and
present a dataset consisting of 5,000 images covering 25 different classes of
groceries, with at least 97 images per class. We collected all images from
real-world settings at different stores and apartments. In contrast to existing
groceries datasets, our dataset includes a large variety of perspectives,
lighting conditions, and degrees of clutter. Overall, our images contain
thousands of different object instances. It is our hope that machine learning
and robotics researchers find this dataset of use for training, testing, and
bootstrapping their approaches. As a baseline classifier to facilitate
comparison, we re-trained the CaffeNet architecture (an adaptation of the
well-known AlexNet) on our dataset and achieved a mean accuracy of 78.9%. We
release this trained model along with the code and data splits we used in our
experiments.
DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins
Authors: Hamid Reza Hassanzadeh, May D. Wang
Date: 2016-11-17 00:00:00
Tasks: Drug Design, Specificity

Transcription factors (TFs) are macromolecules that bind to
\textit{cis}-regulatory specific sub-regions of DNA promoters and initiate
transcription. Finding the exact location of these binding sites (aka motifs)
is important in a variety of domains such as drug design and development. To
address this need, several \textit{in vivo} and \textit{in vitro} techniques
have been developed so far that try to characterize and predict the binding
specificity of a protein to different DNA loci. The major problem with these
techniques is that they are not accurate enough in prediction of the binding
affinity and characterization of the corresponding motifs. As a result,
downstream analysis is required to uncover the locations where proteins of
interest bind. Here, we propose DeeperBind, a long short term recurrent
convolutional network for prediction of protein binding specificities with
respect to DNA probes. DeeperBind can model the positional dynamics of probe
sequences and hence reckons with the contributions made by individual
sub-regions in DNA sequences, in an effective way. Moreover, it can be trained
and tested on datasets containing varying-length sequences. We apply our
pipeline to the datasets derived from protein binding microarrays (PBMs), an
in-vitro high-throughput technology for quantification of protein-DNA binding
preferences, and present promising results. To the best of our knowledge, this
is the most accurate pipeline that can predict binding specificities of DNA
sequences from the data produced by high-throughput technologies through
utilization of the power of deep learning for feature generation and positional
dynamics modeling.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Authors: Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
Date: 2016-09-19 00:00:00
Tasks: Fairness, General Classification

Recent discussion in the public sphere about algorithmic classification has
involved tension between competing notions of what it means for a probabilistic
classification to be fair to different groups. We formalize three fairness
conditions that lie at the heart of these debates, and we prove that except in
highly constrained special cases, there is no method that can satisfy these
three conditions simultaneously. Moreover, even satisfying all three conditions
approximately requires that the data lie in an approximate version of one of
the constrained special cases identified by our theorem. These results suggest
some of the ways in which key notions of fairness are incompatible with each
other, and hence provide a framework for thinking about the trade-offs between
them.
Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs
Authors: Guilherme Folego, Marcus A. Angeloni, José Augusto Stuchi, Alan Godoy, Anderson Rocha
Date: 2016-11-17 00:00:00
Tasks: Face Verification

Cross-domain biometrics has been emerging as a new necessity, which poses
several additional challenges, including harsh illumination changes, noise,
pose variation, among others. In this paper, we explore approaches to
cross-domain face verification, comparing self-portrait photographs ("selfies")
to ID documents. We approach the problem with proper image photometric
adjustment and data standardization techniques, along with deep learning
methods to extract the most prominent features from the data, reducing the
effects of domain shift in this problem. We validate the methods using a novel
dataset comprising 50 individuals. The obtained results are promising and
indicate that the adopted path is worth further investigation.
A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival
Authors: Hamid Reza Hassanzadeh, John H. Phan, May D. Wang
Date: 2016-11-17 00:00:00
Tasks: Survival Prediction

Cancer survival prediction is an active area of research that can help
prevent unnecessary therapies and improve patient's quality of life. Gene
expression profiling is being widely used in cancer studies to discover
informative biomarkers that aid predict different clinical endpoint prediction.
We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq)
to predict survival of cancer patients. Despite the wealth of information
available in expression profiles of cancer tumors, fulfilling the
aforementioned objective remains a big challenge, for the most part, due to the
paucity of data samples compared to the high dimension of the expression
profiles. As such, analysis of transcriptomic data modalities calls for
state-of-the-art big-data analytics techniques that can maximally use all the
available data to discover the relevant information hidden within a significant
amount of noise. In this paper, we propose a pipeline that predicts cancer
patients' survival by exploiting the structure of the input (manifold learning)
and by leveraging the unlabeled samples using Laplacian support vector
machines, a graph-based semi supervised learning (GSSL) paradigm. We show that
under certain circumstances, no single modality per se will result in the best
accuracy and by fusing different models together via a stacked generalization
strategy, we may boost the accuracy synergistically. We apply our approach to
two cancer datasets and present promising results. We maintain that a similar
pipeline can be used for predictive tasks where labeled samples are expensive
to acquire.
Compensating for Large In-Plane Rotations in Natural Images
Authors: Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Date: 2016-11-17 00:00:00
Tasks: Bayesian Optimization, Image Retrieval, Retrieval

Rotation invariance has been studied in the computer vision community
primarily in the context of small in-plane rotations. This is usually achieved
by building invariant image features. However, the problem of achieving
invariance for large rotation angles remains largely unexplored. In this work,
we tackle this problem by directly compensating for large rotations, as opposed
to building invariant features. This is inspired by the neuro-scientific
concept of mental rotation, which humans use to compare pairs of rotated
objects. Our contributions here are three-fold. First, we train a Convolutional
Neural Network (CNN) to detect image rotations. We find that generic CNN
architectures are not suitable for this purpose. To this end, we introduce a
convolutional template layer, which learns representations for canonical
'unrotated' images. Second, we use Bayesian Optimization to quickly sift
through a large number of candidate images to find the canonical 'unrotated'
image. Third, we use this method to achieve robustness to large angles in an
image retrieval scenario. Our method is task-agnostic, and can be used as a
pre-processing step in any computer vision system.
Fast Non-Parametric Tests of Relative Dependency and Similarity
Authors: Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blashcko
Date: 2016-11-17 00:00:00

We introduce two novel non-parametric statistical hypothesis tests. The first
test, called the relative test of dependency, enables us to determine whether
one source variable is significantly more dependent on a first target variable
or a second. Dependence is measured via the Hilbert-Schmidt Independence
Criterion (HSIC). The second test, called the relative test of similarity, is
use to determine which of the two samples from arbitrary distributions is
significantly closer to a reference sample of interest and the relative measure
of similarity is based on the Maximum Mean Discrepancy (MMD). To construct
these tests, we have used as our test statistics the difference of HSIC
statistics and of MMD statistics, respectively. The resulting tests are
consistent and unbiased, and have favorable convergence properties. The
effectiveness of the relative dependency test is demonstrated on several
real-world problems: we identify languages groups from a multilingual parallel
corpus, and we show that tumor location is more dependent on gene expression
than chromosome imbalance. We also demonstrate the performance of the relative
test of similarity over a broad selection of model comparisons problems in deep
generative models.
Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs
Authors: Yaniv Altshuler, Alex Pentland, Shlomo Bekhor, Yoram Shiftan, Alfred Bruckstein
Date: 2016-11-17 00:00:00

Current state of the art in the field of UAV activation relies solely on
human operators for the design and adaptation of the drones' flying routes.
Furthermore, this is being done today on an individual level (one vehicle per
operators), with some exceptions of a handful of new systems, that are
comprised of a small number of self-organizing swarms, manually guided by a
human operator.
  Drones-based monitoring is of great importance in variety of civilian
domains, such as road safety, homeland security, and even environmental
control. In its military aspect, efficiently detecting evading targets by a
fleet of unmanned drones has an ever increasing impact on the ability of modern
armies to engage in warfare. The latter is true both traditional symmetric
conflicts among armies as well as asymmetric ones. Be it a speeding driver, a
polluting trailer or a covert convoy, the basic challenge remains the same --
how can its detection probability be maximized using as little number of drones
as possible.
  In this work we propose a novel approach for the optimization of large scale
swarms of reconnaissance drones -- capable of producing on-demand optimal
coverage strategies for any given search scenario. Given an estimation cost of
the threat's potential damages, as well as types of monitoring drones available
and their comparative performance, our proposed method generates an
analytically provable strategy, stating the optimal number and types of drones
to be deployed, in order to cost-efficiently monitor a pre-defined region for
targets maneuvering using a given roads networks.
  We demonstrate our model using a unique dataset of the Israeli transportation
network, on which different deployment schemes for drones deployment are
evaluated.
Gated End-to-End Memory Networks
Authors: Julien Perez, Fei Liu
Date: 2016-10-13 00:00:00
Proceeding: EACL 2017 4
Tasks: dialog state tracking, Question Answering, Reading Comprehension

Machine reading using differentiable reasoning models has recently shown
remarkable progress. In this context, End-to-End trainable Memory Networks,
MemN2N, have demonstrated promising performance on simple natural language
based reasoning tasks such as factual reasoning and basic deduction. However,
other tasks, namely multi-fact question-answering, positional reasoning or
dialog related tasks, remain challenging particularly due to the necessity of
more complex interactions between the memory and controller modules composing
this family of models. In this paper, we introduce a novel end-to-end memory
access regulation mechanism inspired by the current progress on the connection
short-cutting principle in the field of computer vision. Concretely, we develop
a Gated End-to-End trainable Memory Network architecture, GMemN2N. From the
machine learning perspective, this new capability is learned in an end-to-end
fashion without the use of any additional supervision signal which is, as far
as our knowledge goes, the first of its kind. Our experiments show significant
improvements on the most challenging tasks in the 20 bAbI dataset, without the
use of any domain knowledge. Then, we show improvements on the dialog bAbI
tasks including the real human-bot conversion-based Dialog State Tracking
Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state
of the art.
GENESIM: genetic extraction of a single, interpretable model
Authors: Gilles Vandewiele, Olivier Janssens, Femke Ongenae, Filip De Turck, Sofie Van Hoecke
Date: 2016-11-17 00:00:00
Tasks: Decision Making, Interpretable Machine Learning, model

Models obtained by decision tree induction techniques excel in being
interpretable.However, they can be prone to overfitting, which results in a low
predictive performance. Ensemble techniques are able to achieve a higher
accuracy. However, this comes at a cost of losing interpretability of the
resulting model. This makes ensemble techniques impractical in applications
where decision support, instead of decision making, is crucial.
  To bridge this gap, we present the GENESIM algorithm that transforms an
ensemble of decision trees to a single decision tree with an enhanced
predictive performance by using a genetic algorithm. We compared GENESIM to
prevalent decision tree induction and ensemble techniques using twelve publicly
available data sets. The results show that GENESIM achieves a better predictive
performance on most of these data sets than decision tree induction techniques
and a predictive performance in the same order of magnitude as the ensemble
techniques. Moreover, the resulting model of GENESIM has a very low complexity,
making it very interpretable, in contrast to ensemble techniques.