pwc-archive/papers-with-abstracts huggingface dataset:
Enlightening Deep Neural Networks with Knowledge of Confounding Factors
Authors: Yu Zhong, Gil Ettinger
Date: 2016-07-08 00:00:00
Tasks: Disentanglement, General Classification, Object Recognition

Deep learning techniques have demonstrated significant capacity in modeling
some of the most challenging real world problems of high complexity. Despite
the popularity of deep models, we still strive to better understand the
underlying mechanism that drives their success. Motivated by observations that
neurons in trained deep nets predict attributes indirectly related to the
training tasks, we recognize that a deep network learns representations more
general than the task at hand to disentangle impacts of multiple confounding
factors governing the data, in order to isolate the effects of the concerning
factors and optimize a given objective. Consequently, we propose a general
framework to augment training of deep models with information on auxiliary
explanatory data variables, in an effort to boost this disentanglement and
train deep networks that comprehend the data interactions and distributions
more accurately, and thus improve their generalizability. We incorporate
information on prominent auxiliary explanatory factors of the data population
into existing architectures as secondary objective/loss blocks that take inputs
from hidden layers during training. Once trained, these secondary circuits can
be removed to leave a model with the same architecture as the original, but
more generalizable and discerning thanks to its comprehension of data
interactions. Since pose is one of the most dominant confounding factors for
object recognition, we apply this principle to instantiate a pose-aware deep
convolutional neural network and demonstrate that auxiliary pose information
indeed improves the classification accuracy in our experiments on SAR target
classification tasks.
Convergence rates of Kernel Conjugate Gradient for random design regression
Authors: Gilles Blanchard, Nicole Krämer
Date: 2016-07-08 00:00:00
Tasks: Dimensionality Reduction, regression, Supervised dimensionality reduction

We prove statistical rates of convergence for kernel-based least squares
regression from i.i.d. data using a conjugate gradient algorithm, where
regularization against overfitting is obtained by early stopping. This method
is related to Kernel Partial Least Squares, a regression method that combines
supervised dimensionality reduction with least squares projection. Following
the setting introduced in earlier related literature, we study so-called "fast
convergence rates" depending on the regularity of the target regression
function (measured by a source condition in terms of the kernel integral
operator) and on the effective dimensionality of the data mapped into the
kernel space. We obtain upper bounds, essentially matching known minimax lower
bounds, for the $\mathcal{L}^2$ (prediction) norm as well as for the stronger
Hilbert norm, if the true regression function belongs to the reproducing kernel
Hilbert space. If the latter assumption is not fulfilled, we obtain similar
convergence rates for appropriate norms, provided additional unlabeled data are
available.
Lexical Based Semantic Orientation of Online Customer Reviews and Blogs
Authors: Aurangzeb khan, Khairullah khan, Shakeel Ahmad, Fazal Masood Kundi, Irum Tareen, Muhammad Zubair Asghar
Date: 2016-07-08 00:00:00
Tasks: Opinion Mining, Sentence, Sentiment Analysis, Sentiment Classification

Rapid increase in internet users along with growing power of online review
sites and social media has given birth to sentiment analysis or opinion mining,
which aims at determining what other people think and comment. Sentiments or
Opinions contain public generated content about products, services, policies
and politics. People are usually interested to seek positive and negative
opinions containing likes and dislikes, shared by users for features of
particular product or service. This paper proposed sentence-level lexical based
domain independent sentiment classification method for different types of data
such as reviews and blogs. The proposed method is based on general lexicons
i.e. WordNet, SentiWordNet and user defined lexical dictionaries for semantic
orientation. The relations and glosses of these dictionaries provide solution
to the domain portability problem. The method performs better than word and
text level corpus based machine learning methods for semantic orientation. The
results show the proposed method performs better as it shows precision of 87%
and83% at document and sentence levels respectively for online comments.
Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devices
Authors: Pouya Samangouei, Rama Chellappa
Date: 2016-04-29 00:00:00
Tasks: Attribute

We present a Deep Convolutional Neural Network (DCNN) architecture for the
task of continuous authentication on mobile devices. To deal with the limited
resources of these devices, we reduce the complexity of the networks by
learning intermediate features such as gender and hair color instead of
identities. We present a multi-task, part-based DCNN architecture for attribute
detection that performs better than the state-of-the-art methods in terms of
accuracy. As a byproduct of the proposed architecture, we are able to explore
the embedding space of the attributes extracted from different facial parts,
such as mouth and eyes, to discover new attributes. Furthermore, through
extensive experimentation, we show that the attribute features extracted by our
method outperform the previously presented attribute-based method and a
baseline LBP method for the task of active authentication. Lastly, we
demonstrate the effectiveness of the proposed architecture in terms of speed
and power consumption by deploying it on an actual mobile device.
Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments
Authors: Markus Wulfmeier, Dominic Zeng Wang, Ingmar Posner
Date: 2016-07-08 00:00:00

In this work, we present an approach to learn cost maps for driving in
complex urban environments from a very large number of demonstrations of
driving behaviour by human experts. The learned cost maps are constructed
directly from raw sensor measurements, bypassing the effort of manually
designing cost maps as well as features. When deploying the learned cost maps,
the trajectories generated not only replicate human-like driving behaviour but
are also demonstrably robust against systematic errors in putative robot
configuration. To achieve this we deploy a Maximum Entropy based, non-linear
IRL framework which uses Fully Convolutional Neural Networks (FCNs) to
represent the cost model underlying expert driving behaviour. Using a deep,
parametric approach enables us to scale efficiently to large datasets and
complex behaviours by being run-time independent of dataset extent during
deployment. We demonstrate the scalability and the performance of the proposed
approach on an ambitious dataset collected over the course of one year
including more than 25k demonstration trajectories extracted from over 120km of
driving around pedestrianised areas in the city of Milton Keynes, UK. We
evaluate the resulting cost representations by showing the advantages over a
carefully manually designed cost map and, in addition, demonstrate its
robustness to systematic errors by learning precise cost-maps even in the
presence of system calibration perturbations.
Non-Central Catadioptric Cameras Pose Estimation using 3D Lines
Authors: Andre Mateus, Pedro Miraldo, Pedro U. Lima
Date: 2016-07-08 00:00:00
Tasks: Pose Estimation, Translation

In this article we purpose a novel method for planar pose estimation of
mobile robots. This method is based on an analytic solution (which we derived)
for the projection of 3D straight lines, onto the mirror of Non-Central
Catadioptric Cameras (NCCS). The resulting solution is rewritten as a function
of the rotation and translation parameters, which is then used as an error
function for a set of mirror points. Those should be the result of the
projection of a set of points incident with the respective 3D lines. The
camera's pose is given by minimizing the error function, with the associated
constraints. The method is validated by experiments both with synthetic and
real data. The latter was collected from a mobile robot equipped with a NCCS.
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Authors: Andreas Doumanoglou, Vassileios Balntas, Rigas Kouskouridas, Tae-Kyun Kim
Date: 2016-07-08 00:00:00
Tasks: 3D Pose Estimation, Object, Pose Estimation, regression

In this paper we tackle the problem of estimating the 3D pose of object
instances, using convolutional neural networks. State of the art methods
usually solve the challenging problem of regression in angle space indirectly,
focusing on learning discriminative features that are later fed into a separate
architecture for 3D pose estimation. In contrast, we propose an end-to-end
learning framework for directly regressing object poses by exploiting Siamese
Networks. For a given image pair, we enforce a similarity measure between the
representation of the sample images in the feature and pose space respectively,
that is shown to boost regression performance. Furthermore, we argue that our
pose-guided feature learning using our Siamese Regression Network generates
more discriminative features that outperform the state of the art. Last, our
feature learning formulation provides the ability of learning features that can
perform under severe occlusions, demonstrating high performance on our novel
hand-object dataset.
Introducing a Calculus of Effects and Handlers for Natural Language Semantics
Authors: Jirka Maršík, Maxime Amblard
Date: 2016-06-20 00:00:00

In compositional model-theoretic semantics, researchers assemble
truth-conditions or other kinds of denotations using the lambda calculus. It
was previously observed that the lambda terms and/or the denotations studied
tend to follow the same pattern: they are instances of a monad. In this paper,
we present an extension of the simply-typed lambda calculus that exploits this
uniformity using the recently discovered technique of effect handlers. We prove
that our calculus exhibits some of the key formal properties of the lambda
calculus and we use it to construct a modular semantics for a small fragment
that involves multiple distinct semantic phenomena.
Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks
Authors: Darryl D. Lin, Sachin S. Talathi
Date: 2016-07-08 00:00:00

It is known that training deep neural networks, in particular, deep
convolutional networks, with aggressively reduced numerical precision is
challenging. The stochastic gradient descent algorithm becomes unstable in the
presence of noisy gradient updates resulting from arithmetic with limited
numeric precision. One of the well-accepted solutions facilitating the training
of low precision fixed point networks is stochastic rounding. However, to the
best of our knowledge, the source of the instability in training neural
networks with noisy gradient updates has not been well investigated. This work
is an attempt to draw a theoretical connection between low numerical precision
and training algorithm stability. In doing so, we will also propose and verify
through experiments methods that are able to improve the training performance
of deep convolutional networks in fixed point.
From Collective Adaptive Systems to Human Centric Computation and Back: Spatial Model Checking for Medical Imaging
Authors: Gina Belmonte, Vincenzo Ciancia, Diego Latella, Mieke Massink
Date: 2016-07-08 00:00:00
Tasks: Position

Recent research on formal verification for Collective Adaptive Systems (CAS)
pushed advancements in spatial and spatio-temporal model checking, and as a
side result provided novel image analysis methodologies, rooted in logical
methods for topological spaces. Medical Imaging (MI) is a field where such
technologies show potential for ground-breaking innovation. In this position
paper, we present a preliminary investigation centred on applications of
spatial model checking to MI. The focus is shifted from pure logics to a
mixture of logical, statistical and algorithmic approaches, driven by the
logical nature intrinsic to the specification of the properties of interest in
the field. As a result, novel operators are introduced, that could as well be
brought back to the setting of CAS.
Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images
Authors: Jinghua Wang, Gang Wang
Date: 2015-11-17 00:00:00
Tasks: Action Recognition, Action Recognition In Still Images, Temporal Action Localization

Recognizing actions from still images is popularly studied recently. In this
paper, we model an action class as a flexible number of spatial configurations
of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we
discover a set of parts in image collections via unsupervised learning. Then,
our new spatial SPN is applied to model the spatial relationship and also the
high-order correlations of parts. To learn robust networks, we further develop
a hierarchical spatial SPN method, which models pairwise spatial relationship
between parts inside sub-images and models the correlation of sub-images via
extra layers of SPN. Our method is shown to be effective on two benchmark
datasets.
Interpretable Classification Models for Recidivism Prediction
Authors: Jiaming Zeng, Berk Ustun, Cynthia Rudin
Date: 2015-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Decision Making, General Classification, Prediction

We investigate a long-debated question, which is how to create predictive
models of recidivism that are sufficiently accurate, transparent, and
interpretable to use for decision-making. This question is complicated as these
models are used to support different decisions, from sentencing, to determining
release on probation, to allocating preventative social services. Each use case
might have an objective other than classification accuracy, such as a desired
true positive rate (TPR) or false positive rate (FPR). Each (TPR, FPR) pair is
a point on the receiver operator characteristic (ROC) curve. We use popular
machine learning methods to create models along the full ROC curve on a wide
range of recidivism prediction problems. We show that many methods (SVM, Ridge
Regression) produce equally accurate models along the full ROC curve. However,
methods that designed for interpretability (CART, C5.0) cannot be tuned to
produce models that are accurate and/or interpretable. To handle this
shortcoming, we use a new method known as SLIM (Supersparse Linear Integer
Models) to produce accurate, transparent, and interpretable models along the
full ROC curve. These models can be used for decision-making for many different
use cases, since they are just as accurate as the most powerful black-box
machine learning models, but completely transparent, and highly interpretable.
Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
Authors: Ali Moharrer, Shuangqing Wei, George T. Amariucai, Jing Deng
Date: 2016-01-24 00:00:00

In latent Gaussian trees the pairwise correlation signs between the variables
are intrinsically unrecoverable. Such information is vital since it completely
determines the direction in which two variables are associated. In this work,
we resort to information theoretical approaches to achieve two fundamental
goals: First, we quantify the amount of information loss due to unrecoverable
sign information. Second, we show the importance of such information in
determining the maximum achievable rate region, in which the observed output
vector can be synthesized, given its probability density function. In
particular, we model the graphical model as a communication channel and propose
a new layered encoding framework to synthesize observed data using upper layer
Gaussian inputs and independent Bernoulli correlation sign inputs from each
layer. We find the achievable rate region for the rate tuples of multi-layer
latent Gaussian messages to synthesize the desired observables.
Single-Channel Multi-Speaker Separation using Deep Clustering
Authors: Yusuf Isik, Jonathan Le Roux, Zhuo Chen, Shinji Watanabe, John R. Hershey
Date: 2016-07-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Clustering, Deep Clustering, Speaker Separation, speech-recognition, Speech Recognition, Speech Separation

Deep clustering is a recently introduced deep learning architecture that uses
discriminatively trained embeddings as the basis for clustering. It was
recently applied to spectrogram segmentation, resulting in impressive results
on speaker-independent multi-speaker separation. In this paper we extend the
baseline system with an end-to-end signal approximation objective that greatly
improves performance on a challenging speech separation. We first significantly
improve upon the baseline system performance by incorporating better
regularization, larger temporal context, and a deeper architecture, culminating
in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB
compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1
dB SDR improvement for three-speaker separation. We then extend the model to
incorporate an enhancement layer to refine the signal estimates, and perform
end-to-end training through both the clustering and enhancement stages to
maximize signal fidelity. We evaluate the results using automatic speech
recognition. The new signal approximation objective, combined with end-to-end
training, produces unprecedented performance, reducing the word error rate
(WER) from 89.1% down to 30.8%. This represents a major advancement towards
solving the cocktail party problem.
Argumentation Models for Cyber Attribution
Authors: Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
Date: 2016-07-07 00:00:00

A major challenge in cyber-threat analysis is combining information from
different sources to find the person or the group responsible for the
cyber-attack. It is one of the most important technical and policy challenges
in cyber-security. The lack of ground truth for an individual responsible for
an attack has limited previous studies. In this paper, we take a first step
towards overcoming this limitation by building a dataset from the
capture-the-flag event held at DEFCON, and propose an argumentation model based
on a formal reasoning framework called DeLP (Defeasible Logic Programming)
designed to aid an analyst in attributing a cyber-attack. We build models from
latent variables to reduce the search space of culprits (attackers), and show
that this reduction significantly improves the performance of
classification-based approaches from 37% to 62% in identifying the attacker.
Attribute And-Or Grammar for Joint Parsing of Human Attributes, Part and Pose
Authors: Se-Young Park, Bruce Xiaohan Nie, Song-Chun Zhu
Date: 2016-05-06 00:00:00
Tasks: Attribute, Human Detection, Pose Estimation

This paper presents an attribute and-or grammar (A-AOG) model for jointly
inferring human body pose and human attributes in a parse graph with attributes
augmented to nodes in the hierarchical representation. In contrast to other
popular methods in the current literature that train separate classifiers for
poses and individual attributes, our method explicitly represents the
decomposition and articulation of body parts, and account for the correlations
between poses and attributes. The A-AOG model is an amalgamation of three
traditional grammar formulations: (i) Phrase structure grammar representing the
hierarchical decomposition of the human body from whole to parts; (ii)
Dependency grammar modeling the geometric articulation by a kinematic graph of
the body pose; and (iii) Attribute grammar accounting for the compatibility
relations between different parts in the hierarchy so that their appearances
follow a consistent style. The parse graph outputs human detection, pose
estimation, and attribute prediction simultaneously, which are intuitive and
interpretable. We conduct experiments on two tasks on two datasets, and
experimental results demonstrate the advantage of joint modeling in comparison
with computing poses and attributes independently. Furthermore, our model
obtains better performance over existing methods for both pose estimation and
attribute prediction tasks.
Inverting face embeddings with convolutional neural networks
Authors: Andrey Zhmoginov, Mark Sandler
Date: 2016-06-14 00:00:00
Tasks: Face Transfer

Deep neural networks have dramatically advanced the state of the art for many
areas of machine learning. Recently they have been shown to have a remarkable
ability to generate highly complex visual artifacts such as images and text
rather than simply recognize them.
  In this work we use neural networks to effectively invert low-dimensional
face embeddings while producing realistically looking consistent images. Our
contribution is twofold, first we show that a gradient ascent style approaches
can be used to reproduce consistent images, with a help of a guiding image.
Second, we demonstrate that we can train a separate neural network to
effectively solve the minimization problem in one pass, and generate images in
real-time. We then evaluate the loss imposed by using a neural network instead
of the gradient descent by comparing the final values of the minimized loss
function.
Machine-based Multimodal Pain Assessment Tool for Infants: A Review
Authors: Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun, Terri Ashmeade
Date: 2016-07-01 00:00:00

Bedside caregivers assess infants' pain at constant intervals by observing
specific behavioral and physiological signs of pain. This standard has two main
limitations. The first limitation is the intermittent assessment of pain, which
might lead to missing pain when the infants are left unattended. Second, it is
inconsistent since it depends on the observer's subjective judgment and differs
between observers. The intermittent and inconsistent assessment can induce poor
treatment and, therefore, cause serious long-term consequences. To mitigate
these limitations, the current standard can be augmented by an automated system
that monitors infants continuously and provides quantitative and consistent
assessment of pain. Several automated methods have been introduced to assess
infants' pain automatically based on analysis of behavioral or physiological
pain indicators. This paper comprehensively reviews the automated approaches
(i.e., approaches to feature extraction) for analyzing infants' pain and the
current efforts in automatic pain recognition. In addition, it reviews the
databases available to the research community and discusses the current
limitations of the automated pain assessment.
A Classification Framework for Partially Observed Dynamical Systems
Authors: Yuan Shen, Peter Tino, Krasimira Tsaneva-Atanasova
Date: 2016-07-07 00:00:00
Tasks: Classification, General Classification

We present a general framework for classifying partially observed dynamical
systems based on the idea of learning in the model space. In contrast to the
existing approaches using model point estimates to represent individual data
items, we employ posterior distributions over models, thus taking into account
in a principled manner the uncertainty due to both the generative
(observational and/or dynamic noise) and observation (sampling in time)
processes. We evaluate the framework on two testbeds - a biological pathway
model and a stochastic double-well system. Crucially, we show that the
classifier performance is not impaired when the model class used for inferring
posterior distributions is much more simple than the observation-generating
model class, provided the reduced complexity inferential model class captures
the essential characteristics needed for the given classification task.