pwc-archive/papers-with-abstracts huggingface dataset:
Towards Interpretable R-CNN by Unfolding Latent Structures
Authors: Tianfu Wu, Wei Sun, Xilai Li, Xi Song, Bo Li
Date: 2017-11-14 00:00:00
Tasks: object-detection, Object Detection

This paper first proposes a method of formulating model interpretability in
visual understanding tasks based on the idea of unfolding latent structures. It
then presents a case study in object detection using popular two-stage
region-based convolutional network (i.e., R-CNN) detection systems. We focus on
weakly-supervised extractive rationale generation, that is learning to unfold
latent discriminative part configurations of object instances automatically and
simultaneously in detection without using any supervision for part
configurations. We utilize a top-down hierarchical and compositional grammar
model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold
the space of latent part configurations of regions of interest (RoIs). We
propose an AOGParsing operator to substitute the RoIPooling operator widely
used in R-CNN. In detection, a bounding box is interpreted by the best parse
tree derived from the AOG on-the-fly, which is treated as the qualitatively
extractive rationale generated for interpreting detection. We propose a
folding-unfolding method to train the AOG and convolutional networks
end-to-end. In experiments, we build on R-FCN and test our method on the PASCAL
VOC 2007 and 2012 datasets. We show that the method can unfold promising latent
structures without hurting the performance.
Evaluating gender portrayal in Bangladeshi TV
Authors: Md. Naimul Hoque, Rawshan E Fatima, Manash Kumar Mandal, Nazmus Saquib
Date: 2017-11-14 00:00:00

Computer Vision and machine learning methods were previously used to reveal
screen presence of genders in TV and movies. In this work, using head pose,
gender detection, and skin color estimation techniques, we demonstrate that the
gender disparity in TV in a South Asian country such as Bangladesh exhibits
unique characteristics and is sometimes counter-intuitive to popular
perception. We demonstrate a noticeable discrepancy in female screen presence
in Bangladeshi TV advertisements and political talk shows. Further, contrary to
popular hypotheses, we demonstrate that lighter-toned skin colors are less
prevalent than darker complexions, and additionally, quantifiable body language
markers do not provide conclusive insights about gender dynamics. Overall,
these gender portrayal parameters reveal the different layers of onscreen
gender politics and can help direct incentives to address existing disparities
in a nuanced and targeted manner.
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
Authors: Brett W. Israelsen, Nisar R. Ahmed
Date: 2017-11-08 00:00:00
Tasks: All

People who design, use, and are affected by autonomous artificially
intelligent agents want to be able to \emph{trust} such agents -- that is, to
know that these agents will perform correctly, to understand the reasoning
behind their actions, and to know how to use them appropriately. Many
techniques have been devised to assess and influence human trust in
artificially intelligent agents. However, these approaches are typically ad
hoc, and have not been formally related to each other or to formal trust
models. This paper presents a survey of \emph{algorithmic assurances}, i.e.
programmed components of agent operation that are expressly designed to
calibrate user trust in artificially intelligent agents. Algorithmic assurances
are first formally defined and classified from the perspective of formally
modeled human-artificially intelligent agent trust relationships. Building on
these definitions, a synthesis of research across communities such as machine
learning, human-computer interaction, robotics, e-commerce, and others reveals
that assurance algorithms naturally fall along a spectrum in terms of their
impact on an agent's core functionality, with seven notable classes ranging
from integral assurances (which impact an agent's core functionality) to
supplemental assurances (which have no direct effect on agent performance).
Common approaches within each of these classes are identified and discussed;
benefits and drawbacks of different approaches are also investigated.
Tree Projections and Constraint Optimization Problems: Fixed-Parameter Tractability and Parallel Algorithms
Authors: Georg Gottlob, Gianlugi Greco, Francesco Scarcello
Date: 2017-11-14 00:00:00

Tree projections provide a unifying framework to deal with most structural
decomposition methods of constraint satisfaction problems (CSPs). Within this
framework, a CSP instance is decomposed into a number of sub-problems, called
views, whose solutions are either already available or can be computed
efficiently. The goal is to arrange portions of these views in a tree-like
structure, called tree projection, which determines an efficiently solvable CSP
instance equivalent to the original one. Deciding whether a tree projection
exists is NP-hard. Solution methods have therefore been proposed in the
literature that do not require a tree projection to be given, and that either
correctly decide whether the given CSP instance is satisfiable, or return that
a tree projection actually does not exist. These approaches had not been
generalized so far on CSP extensions for optimization problems, where the goal
is to compute a solution of maximum value/minimum cost. The paper fills the
gap, by exhibiting a fixed-parameter polynomial-time algorithm that either
disproves the existence of tree projections or computes an optimal solution,
with the parameter being the size of the expression of the objective function
to be optimized over all possible solutions (and not the size of the whole
constraint formula, used in related works). Tractability results are also
established for the problem of returning the best K solutions. Finally,
parallel algorithms for such optimization problems are proposed and analyzed.
Given that the classes of acyclic hypergraphs, hypergraphs of bounded
treewidth, and hypergraphs of bounded generalized hypertree width are all
covered as special cases of the tree projection framework, the results in this
paper directly apply to these classes. These classes are extensively considered
in the CSP setting, as well as in conjunctive database query evaluation and
optimization.
Unsupervised patient representations from clinical notes with interpretable classification decisions
Authors: Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans
Date: 2017-11-14 00:00:00
Tasks: Classification, Denoising, General Classification

We have two main contributions in this work: 1. We explore the usage of a
stacked denoising autoencoder, and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. We
evaluate these representations by using them as features in multiple supervised
setups, and compare their performance with those of sparse representations. 2.
To understand and interpret the representations, we explore the best encoded
features within the patient representations obtained from the autoencoder
model. Further, we calculate the significance of the input features of the
trained classifiers when we use these pretrained representations as input.
Joint Gaussian Processes for Biophysical Parameter Retrieval
Authors: Daniel Heestermans Svendsen, Luca Martino, Manuel Campos-Taberner, Francisco Javier García-Haro, Gustau Camps-Valls
Date: 2017-11-14 00:00:00
Tasks: Gaussian Processes, regression, Retrieval

Solving inverse problems is central to geosciences and remote sensing.
Radiative transfer models (RTMs) represent mathematically the physical laws
which govern the phenomena in remote sensing applications (forward models). The
numerical inversion of the RTM equations is a challenging and computationally
demanding problem, and for this reason, often the application of a nonlinear
statistical regression is preferred. In general, regression models predict the
biophysical parameter of interest from the corresponding received radiance.
However, this approach does not employ the physical information encoded in the
RTMs. An alternative strategy, which attempts to include the physical
knowledge, consists in learning a regression model trained using data simulated
by an RTM code. In this work, we introduce a nonlinear nonparametric regression
model which combines the benefits of the two aforementioned approaches. The
inversion is performed taking into account jointly both real observations and
RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid
framework for exploiting the regularities between the two types of data. The
JGP automatically detects the relative quality of the simulated and real data,
and combines them accordingly. This occurs by learning an additional
hyper-parameter w.r.t. a standard GP model, and fitting parameters through
maximizing the pseudo-likelihood of the real observations. The resulting scheme
is both simple and robust, i.e., capable of adapting to different scenarios.
The advantages of the JGP method compared to benchmark strategies are shown
considering RTM-simulated and real observations in different experiments.
Specifically, we consider leaf area index (LAI) retrieval from Landsat data
combined with simulated data generated by the PROSAIL model.
A learning problem that is independent of the set theory ZFC axioms
Authors: Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff
Date: 2017-11-14 00:00:00
Tasks: General Classification, PAC learning

We consider the following statistical estimation problem: given a family F of
real valued functions over some domain X and an i.i.d. sample drawn from an
unknown distribution P over X, find h in F such that the expectation of h
w.r.t. P is probably approximately equal to the supremum over expectations on
members of F. This Expectation Maximization (EMX) problem captures many well
studied learning problems; in fact, it is equivalent to Vapnik's general
setting of learning.
  Surprisingly, we show that the EMX learnability, as well as the learning
rates of some basic class F, depend on the cardinality of the continuum and is
therefore independent of the set theory ZFC axioms (that are widely accepted as
a formalization of the notion of a mathematical proof).
  We focus on the case where the functions in F are Boolean, which generalizes
classification problems. We study the interaction between the statistical
sample complexity of F and its combinatorial structure. We introduce a new
version of sample compression schemes and show that it characterizes EMX
learnability for a wide family of classes. However, we show that for the class
of finite subsets of the real line, the existence of such compression schemes
is independent of set theory. We conclude that the learnability of that class
with respect to the family of probability distributions of countable support is
independent of the set theory ZFC axioms.
  We also explore the existence of a "VC-dimension-like" parameter that
captures learnability in this setting. Our results imply that that there exist
no "finitary" combinatorial parameter that characterizes EMX learnability in a
way similar to the VC-dimension based characterization of binary valued
classification problems.
CryptoDL: Deep Neural Networks over Encrypted Data
Authors: Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi
Date: 2017-11-14 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Privacy Preserving

Machine learning algorithms based on deep neural networks have achieved
remarkable results and are being extensively used in different domains.
However, the machine learning algorithms requires access to raw data which is
often privacy sensitive. To address this issue, we develop new techniques to
provide solutions for running deep neural networks over encrypted data. In this
paper, we develop new techniques to adopt deep neural networks within the
practical limitation of current homomorphic encryption schemes. More
specifically, we focus on classification of the well-known convolutional neural
networks (CNN). First, we design methods for approximation of the activation
functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree
polynomials which is essential for efficient homomorphic encryption schemes.
Then, we train convolutional neural networks with the approximation polynomials
instead of original activation functions and analyze the performance of the
models. Finally, we implement convolutional neural networks over encrypted data
and measure performance of the models. Our experimental results validate the
soundness of our approach with several convolutional neural networks with
varying number of layers and structures. When applied to the MNIST optical
character recognition tasks, our approach achieves 99.52\% accuracy which
significantly outperforms the state-of-the-art solutions and is very close to
the accuracy of the best non-private version, 99.77\%. Also, it can make close
to 164000 predictions per hour. We also applied our approach to CIFAR-10, which
is much more complex compared to MNIST, and were able to achieve 91.5\%
accuracy with approximation polynomials used as activation functions. These
results show that CryptoDL provides efficient, accurate and scalable
privacy-preserving predictions.
Reconstruction of the External Stimuli from Brain Signals
Authors: Pouya Ghaemmaghami
Date: 2017-11-14 00:00:00
Tasks: Brain Decoding, regression

Despite the rapid advances in Brain-computer Interfacing (BCI) and continuous
effort to improve the accuracy of brain decoding systems, the urge for the
systems to reconstruct the experiences of the users has been widely
acknowledged. This urge has been investigated by some researchers during the
past years in terms of reconstruction of the naturalistic images, abstract
images, video and audio. In this study, we try to tackle this issue by
regressing the stimuli spectrogram using the spectrogram analysis of the brain
signals. The results of our regression-based method suggest the feasibility of
such reconstructions using the neuroimaging techniques that are appropriate for
out-of-lab scenarios.
Adversarial Information Factorization
Authors: Antonia Creswell, Yumnah Mohamied, Biswa Sengupta, Anil A. Bharath
Date: 2017-11-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: Attribute, Facial Attribute Classification, Image Generation, Object

We propose a novel generative model architecture designed to learn
representations for images that factor out a single attribute from the rest of
the representation. A single object may have many attributes which when altered
do not change the identity of the object itself. Consider the human face; the
identity of a particular person is independent of whether or not they happen to
be wearing glasses. The attribute of wearing glasses can be changed without
changing the identity of the person. However, the ability to manipulate and
alter image attributes without altering the object identity is not a trivial
task. Here, we are interested in learning a representation of the image that
separates the identity of an object (such as a human face) from an attribute
(such as 'wearing glasses'). We demonstrate the success of our factorization
approach by using the learned representation to synthesize the same face with
and without a chosen attribute. We refer to this specific synthesis process as
image attribute manipulation. We further demonstrate that our model achieves
competitive scores, with state of the art, on a facial attribute classification
task.
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
Authors: Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang
Date: 2017-11-14 00:00:00
Tasks: Experimental Design

The experimental design problem concerns the selection of k points from a
potentially large design pool of p-dimensional vectors, so as to maximize the
statistical efficiency regressed on the selected k design points. Statistical
efficiency is measured by optimality criteria, including A(verage),
D(eterminant), T(race), E(igen), V(ariance) and G-optimality. Except for the
T-optimality, exact optimization is NP-hard.
  We propose a polynomial-time regret minimization framework to achieve a
$(1+\varepsilon)$ approximation with only $O(p/\varepsilon^2)$ design points,
for all the optimality criteria above.
  In contrast, to the best of our knowledge, before our work, no
polynomial-time algorithm achieves $(1+\varepsilon)$ approximations for
D/E/G-optimality, and the best poly-time algorithm achieving
$(1+\varepsilon)$-approximation for A/V-optimality requires $k =
\Omega(p^2/\varepsilon)$ design points.
On Extending Neural Networks with Loss Ensembles for Text Classification
Authors: Hamideh Hajiabadi, Diego Molla-Aliod, Reza Monsefi
Date: 2017-11-14 00:00:00
Proceeding: ALTA 2017 12
Tasks: General Classification, Meta-Learning, text-classification, Text Classification

Ensemble techniques are powerful approaches that combine several weak
learners to build a stronger one. As a meta learning framework, ensemble
techniques can easily be applied to many machine learning techniques. In this
paper we propose a neural network extended with an ensemble loss function for
text classification. The weight of each weak loss function is tuned within the
training phase through the gradient propagation optimization method of the
neural network. The approach is evaluated on several text classification
datasets. We also evaluate its performance in various environments with several
degrees of label noise. Experimental results indicate an improvement of the
results and strong resilience against label noise in comparison with other
methods.
Robust Keyframe-based Dense SLAM with an RGB-D Camera
Authors: Hao-Min Liu, Chen Li, Guojun Chen, Guofeng Zhang, Michael Kaess, Hujun Bao
Date: 2017-11-14 00:00:00
Tasks: CPU

In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM
approach for an RGB-D camera that can robustly handle fast motion and dense
loop closure, and run without time limitation in a moderate size scene. It not
only can be used to scan high-quality 3D models, but also can satisfy the
demand of VR and AR applications. First, we combine color and depth information
to construct a very fast keyframe-based tracking method on a CPU, which can
work robustly in challenging cases (e.g.~fast camera motion and complex loops).
For reducing accumulation error, we also introduce a very efficient incremental
bundle adjustment (BA) algorithm, which can greatly save unnecessary
computation and perform local and global BA in a unified optimization
framework. An efficient keyframe-based depth representation and fusion method
is proposed to generate and timely update the dense 3D surface with online
correction according to the refined camera poses of keyframes through BA. The
experimental results and comparisons on a variety of challenging datasets and
TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.
Saliency-based Sequential Image Attention with Multiset Prediction
Authors: Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang
Date: 2017-11-14 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-Label Image Classification, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans process visual scenes selectively and sequentially using attention.
Central to models of human visual attention is the saliency map. We propose a
hierarchical visual architecture that operates on a saliency map and uses a
novel attention mechanism to sequentially focus on salient regions and take
additional glimpses within those regions. The architecture is motivated by
human visual attention, and is used for multi-label image classification on a
novel multiset task, demonstrating that it achieves high precision and recall
while localizing objects with its attention. Unlike conventional multi-label
image classification models, the model supports multiset prediction due to a
reinforcement-learning based training process that allows for arbitrary label
permutation and multiple instances per label.
The scarcity of crossing dependencies: a direct outcome of a specific constraint?
Authors: Carlos Gómez-Rodríguez, Ramon Ferrer-i-Cancho
Date: 2016-01-13 00:00:00
Tasks: Sentence

The structure of a sentence can be represented as a network where vertices
are words and edges indicate syntactic dependencies. Interestingly, crossing
syntactic dependencies have been observed to be infrequent in human languages.
This leads to the question of whether the scarcity of crossings in languages
arises from an independent and specific constraint on crossings. We provide
statistical evidence suggesting that this is not the case, as the proportion of
dependency crossings of sentences from a wide range of languages can be
accurately estimated by a simple predictor based on a null hypothesis on the
local probability that two dependencies cross given their lengths. The relative
error of this predictor never exceeds 5% on average, whereas the error of a
baseline predictor assuming a random ordering of the words of a sentence is at
least 6 times greater. Our results suggest that the low frequency of crossings
in natural languages is neither originated by hidden knowledge of language nor
by the undesirability of crossings per se, but as a mere side effect of the
principle of dependency length minimization.
Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarisation
Authors: Diego Molla
Date: 2017-11-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

Supervised approaches for text summarisation suffer from the problem of
mismatch between the target labels/scores of individual sentences and the
evaluation score of the final summary. Reinforcement learning can solve this
problem by providing a learning mechanism that uses the score of the final
summary as a guide to determine the decisions made at the time of selection of
each sentence. In this paper we present a proof-of-concept approach that
applies a policy-gradient algorithm to learn a stochastic policy using an
undiscounted reward. The method has been applied to a policy consisting of a
simple neural network and simple features. The resulting deep reinforcement
learning system is able to learn a global policy and obtain encouraging
results.
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging
Authors: Keunwoo Choi, George Fazekas, Kyunghyun Cho, Mark Sandler
Date: 2017-06-07 00:00:00
Tasks: Music Classification, Music Tagging, TAG

Deep neural networks (DNN) have been successfully applied to music
classification including music tagging. However, there are several open
questions regarding the training, evaluation, and analysis of DNNs. In this
article, we investigate specific aspects of neural networks, the effects of
noisy labels, to deepen our understanding of their properties. We analyse and
(re-)validate a large music tagging dataset to investigate the reliability of
training and evaluation. Using a trained network, we compute label vector
similarities which is compared to groundtruth similarity.
  The results highlight several important aspects of music tagging and neural
networks. We show that networks can be effective despite relatively large error
rates in groundtruth datasets, while conjecturing that label noise can be the
cause of varying tag-wise performance differences. Lastly, the analysis of our
trained network provides valuable insight into the relationships between music
tags. These results highlight the benefit of using data-driven methods to
address automatic music tagging.