pwc-archive/papers-with-abstracts huggingface dataset:
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Authors: Michael C. Burkhart
Date: 2018-07-17 00:00:00
Tasks: Sequential Bayesian Inference

Given a stationary state-space model that relates a sequence of hidden states
and corresponding measurements or observations, Bayesian filtering provides a
principled statistical framework for inferring the posterior distribution of
the current state given all measurements up to the present time. For example,
the Apollo lunar module implemented a Kalman filter to infer its location from
a sequence of earth-based radar measurements and land safely on the moon.
  To perform Bayesian filtering, we require a measurement model that describes
the conditional distribution of each observation given state. The Kalman filter
takes this measurement model to be linear, Gaussian. Here we show how a
nonlinear, Gaussian approximation to the distribution of state given
observation can be used in conjunction with Bayes' rule to build a nonlinear,
non-Gaussian measurement model. The resulting approach, called the
Discriminative Kalman Filter (DKF), retains fast closed-form updates for the
posterior. We argue there are many cases where the distribution of state given
measurement is better-approximated as Gaussian, especially when the
dimensionality of measurements far exceeds that of states and the Bernstein-von
Mises theorem applies. Online neural decoding for brain-computer interfaces
provides a motivating example, where filtering incorporates increasingly
detailed measurements of neural activity to provide users control over external
devices. Within the BrainGate2 clinical trial, the DKF successfully enabled
three volunteers with quadriplegia to control an on-screen cursor in real-time
using mental imagery alone. Participant "T9" used the DKF to type out messages
on a tablet PC.
Learning Convex Partitions and Computing Game-theoretic Equilibria from Best Response Queries
Authors: Paul W. Goldberg, Francisco J. Marmolejo-Cossío
Date: 2018-07-17 00:00:00

Suppose that an $m$-simplex is partitioned into $n$ convex regions having
disjoint interiors and distinct labels, and we may learn the label of any point
by querying it. The learning objective is to know, for any point in the
simplex, a label that occurs within some distance $\epsilon$ from that point.
We present two algorithms for this task: Constant-Dimension Generalised Binary
Search (CD-GBS), which for constant $m$ uses $poly(n, \log \left(
\frac{1}{\epsilon} \right))$ queries, and Constant-Region Generalised Binary
Search (CR-GBS), which uses CD-GBS as a subroutine and for constant $n$ uses
$poly(m, \log \left( \frac{1}{\epsilon} \right))$ queries.
  We show via Kakutani's fixed-point theorem that these algorithms provide
bounds on the best-response query complexity of computing approximate
well-supported equilibria of bimatrix games in which one of the players has a
constant number of pure strategies. We also partially extend our results to
games with multiple players, establishing further query complexity bounds for
computing approximate well-supported equilibria in this setting.
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing
Authors: Gautam Kamath, Christos Tzamos
Date: 2018-07-17 00:00:00

We investigate distribution testing with access to non-adaptive conditional
samples. In the conditional sampling model, the algorithm is given the
following access to a distribution: it submits a query set $S$ to an oracle,
which returns a sample from the distribution conditioned on being from $S$. In
the non-adaptive setting, all query sets must be specified in advance of
viewing the outcomes.
  Our main result is the first polylogarithmic-query algorithm for equivalence
testing, deciding whether two unknown distributions are equal to or far from
each other. This is an exponential improvement over the previous best upper
bound, and demonstrates that the complexity of the problem in this model is
intermediate to the the complexity of the problem in the standard sampling
model and the adaptive conditional sampling model. We also significantly
improve the sample complexity for the easier problems of uniformity and
identity testing. For the former, our algorithm requires only $\tilde O(\log
n)$ queries, matching the information-theoretic lower bound up to a $O(\log
\log n)$-factor.
  Our algorithm works by reducing the problem from $\ell_1$-testing to
$\ell_\infty$-testing, which enjoys a much cheaper sample complexity.
Necessitated by the limited power of the non-adaptive model, our algorithm is
very simple to state. However, there are significant challenges in the
analysis, due to the complex structure of how two arbitrary distributions may
differ.
Explanations for Temporal Recommendations
Authors: Homanga Bharadhwaj, Shruti Joshi
Date: 2018-07-17 00:00:00
Tasks: Recommendation Systems

Recommendation systems are an integral part of Artificial Intelligence (AI)
and have become increasingly important in the growing age of commercialization
in AI. Deep learning (DL) techniques for recommendation systems (RS) provide
powerful latent-feature models for effective recommendation but suffer from the
major drawback of being non-interpretable. In this paper we describe a
framework for explainable temporal recommendations in a DL model. We consider
an LSTM based Recurrent Neural Network (RNN) architecture for recommendation
and a neighbourhood-based scheme for generating explanations in the model. We
demonstrate the effectiveness of our approach through experiments on the
Netflix dataset by jointly optimizing for both prediction accuracy and
explainability.
Layer-wise Relevance Propagation for Explainable Recommendations
Authors: Homanga Bharadhwaj
Date: 2018-07-17 00:00:00

In this paper, we tackle the problem of explanations in a deep-learning based
model for recommendations by leveraging the technique of layer-wise relevance
propagation. We use a Deep Convolutional Neural Network to extract relevant
features from the input images before identifying similarity between the images
in feature space. Relationships between the images are identified by the model
and layer-wise relevance propagation is used to infer pixel-level details of
the images that may have significantly informed the model's choice. We evaluate
our method on an Amazon products dataset and demonstrate the efficacy of our
approach.
Generative Adversarial Imitation from Observation
Authors: Faraz Torabi, Garrett Warnell, Peter Stone
Date: 2018-07-17 00:00:00
Tasks: Imitation Learning

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and also a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We conduct experiments in two different settings: (1) when demonstrations consist of low-dimensional, manually-defined state features, and (2) when demonstrations consist of high-dimensional, raw visual data. We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.
LSTMs with Attention for Aggression Detection
Authors: Nishant Nikhil, Ramit Pahwa, Mehul Kumar Nirala, Rohan Khilnani
Date: 2018-07-16 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification

In this paper, we describe the system submitted for the shared task on
Aggression Identification in Facebook posts and comments by the team Nishnik.
Previous works demonstrate that LSTMs have achieved remarkable performance in
natural language processing tasks. We deploy an LSTM model with an attention
unit over it. Our system ranks 6th and 4th in the Hindi subtask for Facebook
comments and subtask for generalized social media data respectively. And it
ranks 17th and 10th in the corresponding English subtasks.
Probably approximately correct learning of Horn envelopes from queries
Authors: Daniel Borchmann, Tom Hanika, Sergei Obiedkov
Date: 2018-07-16 00:00:00
Tasks: Attribute

We propose an algorithm for learning the Horn envelope of an arbitrary domain
using an expert, or an oracle, capable of answering certain types of queries
about this domain. Attribute exploration from formal concept analysis is a
procedure that solves this problem, but the number of queries it may ask is
exponential in the size of the resulting Horn formula in the worst case. We
recall a well-known polynomial-time algorithm for learning Horn formulas with
membership and equivalence queries and modify it to obtain a polynomial-time
probably approximately correct algorithm for learning the Horn envelope of an
arbitrary domain.
Longitudinal detection of radiological abnormalities with time-modulated LSTM
Authors: Ruggiero Santeramo, Samuel Withey, Giovanni Montana
Date: 2018-07-16 00:00:00
Tasks: General Classification

Convolutional neural networks (CNNs) have been successfully employed in
recent years for the detection of radiological abnormalities in medical images
such as plain x-rays. To date, most studies use CNNs on individual examinations
in isolation and discard previously available clinical information. In this
study we set out to explore whether Long-Short-Term-Memory networks (LSTMs) can
be used to improve classification performance when modelling the entire
sequence of radiographs that may be available for a given patient, including
their reports. A limitation of traditional LSTMs, though, is that they
implicitly assume equally-spaced observations, whereas the radiological exams
are event-based, and therefore irregularly sampled. Using both a simulated
dataset and a large-scale chest x-ray dataset, we demonstrate that a simple
modification of the LSTM architecture, which explicitly takes into account the
time lag between consecutive observations, can boost classification
performance. Our empirical results demonstrate improved detection of commonly
reported abnormalities on chest x-rays such as cardiomegaly, consolidation,
pleural effusion and hiatus hernia.
Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle
Authors: Catarina Moreira, Andreas Wichert
Date: 2018-07-16 00:00:00
Tasks: Decision Making

It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks to deal with decision-making scenarios by incorporating the notion of maximum expected utility in influence diagrams. The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action. This way, we are not proposing a new type of expected utility hypothesis. On the contrary, we are keeping it under its classical definition. We are only incorporating it as an extension of a probabilistic graphical model in a compact graphical representation called an influence diagram in which the utility function depends on the probabilistic influences of the quantum-like Bayesian network. Our findings suggest that the proposed quantum-like influence digram can indeed take advantage of the quantum interference effects of quantum-like Bayesian Networks to maximise the utility of a cooperative behaviour in detriment of a fully rational defect behaviour under the prisoner's dilemma game.
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Authors: Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M. Alvarez
Date: 2018-07-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Semantic Segmentation

Training a deep network to perform semantic segmentation requires large
amounts of labeled data. To alleviate the manual effort of annotating real
images, researchers have investigated the use of synthetic data, which can be
labeled automatically. Unfortunately, a network trained on synthetic data
performs relatively poorly on real images. While this can be addressed by
domain adaptation, existing methods all require having access to real images
during training. In this paper, we introduce a drastically different way to
handle synthetic images that does not require seeing any real images at
training time. Our approach builds on the observation that foreground and
background classes are not affected in the same manner by the domain shift, and
thus should be treated differently. In particular, the former should be handled
in a detection-based manner to better account for the fact that, while their
texture in synthetic images is not photo-realistic, their shape looks natural.
Our experiments evidence the effectiveness of our approach on Cityscapes and
CamVid with models trained on synthetic data only.
Rectification from Radially-Distorted Scales
Authors: James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
Date: 2018-07-16 00:00:00

This paper introduces the first minimal solvers that jointly estimate lens
distortion and affine rectification from repetitions of rigidly transformed
coplanar local features. The proposed solvers incorporate lens distortion into
the camera model and extend accurate rectification to wide-angle images that
contain nearly any type of coplanar repeated content. We demonstrate a
principled approach to generating stable minimal solvers by the Grobner basis
method, which is accomplished by sampling feasible monomial bases to maximize
numerical stability. Synthetic and real-image experiments confirm that the
solvers give accurate rectifications from noisy measurements when used in a
RANSAC-based estimator. The proposed solvers demonstrate superior robustness to
noise compared to the state-of-the-art. The solvers work on scenes without
straight lines and, in general, relax the strong assumptions on scene content
made by the state-of-the-art. Accurate rectifications on imagery that was taken
with narrow focal length to near fish-eye lenses demonstrate the wide
applicability of the proposed method. The method is fully automated, and the
code is publicly available at https://github.com/prittjam/repeats.
Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations
Authors: Mansurul Bhuiyan, Amita Misra, Saurabh Tripathy, Jalal Mahmud, Rama Akkiraju
Date: 2018-07-16 00:00:00
Tasks: Negation

In the last several years, Twitter is being adopted by the companies as an
alternative platform to interact with the customers to address their concerns.
With the abundance of such unconventional conversation resources, push for
developing effective virtual agents is more than ever. To address this
challenge, a better understanding of such customer service conversations is
required. Lately, there have been several works proposing a novel taxonomy for
fine-grained dialogue acts as well as develop algorithms for automatic
detection of these acts. The outcomes of these works are providing stepping
stones for the ultimate goal of building efficient and effective virtual
agents. But none of these works consider handling the notion of negation into
the proposed algorithms. In this work, we developed an SVM-based dialogue acts
prediction algorithm for Twitter customer service conversations where negation
handling is an integral part of the end-to-end solution. For negation handling,
we propose several efficient heuristics as well as adopt recent state-of- art
third party machine learning based solutions. Empirically we show model's
performance gain while handling negation compared to when we don't. Our
experiments show that for the informal text such as tweets, the heuristic-based
approach is more effective.
An agent-based model of an endangered population of the Arctic fox from Mednyi Island
Authors: Angelina Brilliantova, Anton Pletenev, Liliya Doronina, Hadi Hosseini
Date: 2018-07-16 00:00:00

Artificial Intelligence techniques such as agent-based modeling and
probabilistic reasoning have shown promise in modeling complex biological
systems and testing ecological hypotheses through simulation. We develop an
agent-based model of Arctic foxes from Medniy Island while utilizing
Probabilistic Graphical Models to capture the conditional dependencies between
the random variables. Such models provide valuable insights in analyzing
factors behind catastrophic degradation of this population and in revealing
evolutionary mechanisms of its persistence in high-density environment. Using
empirical data from studies in Medniy Island, we create a realistic model of
Arctic foxes as agents, and study their survival and population dynamics under
a variety of conditions.
A PTAS for $\ell_p$-Low Rank Approximation
Authors: Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, Euiwoong Lee, David P. Woodruff
Date: 2018-07-16 00:00:00

A number of recent works have studied algorithms for entrywise $\ell_p$-low rank approximation, namely, algorithms which given an $n \times d$ matrix $A$ (with $n \geq d$), output a rank-$k$ matrix $B$ minimizing $\|A-B\|_p^p=\sum_{i,j}|A_{i,j}-B_{i,j}|^p$ when $p > 0$; and $\|A-B\|_0=\sum_{i,j}[A_{i,j}\neq B_{i,j}]$ for $p=0$. On the algorithmic side, for $p \in (0,2)$, we give the first $(1+\epsilon)$-approximation algorithm running in time $n^{\text{poly}(k/\epsilon)}$. Further, for $p = 0$, we give the first almost-linear time approximation scheme for what we call the Generalized Binary $\ell_0$-Rank-$k$ problem. Our algorithm computes $(1+\epsilon)$-approximation in time $(1/\epsilon)^{2^{O(k)}/\epsilon^{2}} \cdot nd^{1+o(1)}$. On the hardness of approximation side, for $p \in (1,2)$, assuming the Small Set Expansion Hypothesis and the Exponential Time Hypothesis (ETH), we show that there exists $\delta := \delta(\alpha) > 0$ such that the entrywise $\ell_p$-Rank-$k$ problem has no $\alpha$-approximation algorithm running in time $2^{k^{\delta}}$.
Prognostics Estimations with Dynamic States
Authors: Rong-Jing Bao, Hai-Jun Rong, Zhi-Xin Yang, Badong Chen
Date: 2018-07-16 00:00:00
Tasks: Management

The health state assessment and remaining useful life (RUL) estimation play
very important roles in prognostics and health management (PHM), owing to their
abilities to reduce the maintenance and improve the safety of machines or
equipment. However, they generally suffer from this problem of lacking prior
knowledge to pre-define the exact failure thresholds for a machinery operating
in a dynamic environment with a high level of uncertainty. In this case,
dynamic thresholds depicted by the discrete states is a very attractive way to
estimate the RUL of a dynamic machinery. Currently, there are only very few
works considering the dynamic thresholds, and these studies adopted different
algorithms to determine the discrete states and predict the continuous states
separately, which largely increases the complexity of the learning process. In
this paper, we propose a novel prognostics approach for RUL estimation of
aero-engines with self-joint prediction of continuous and discrete states,
wherein the prediction of continuous and discrete states are conducted
simultaneously and dynamically within one learning framework.
Repeatability of Multiparametric Prostate MRI Radiomics Features
Authors: Michael Schwier, Joost van Griethuysen, Mark G Vangel, Steve Pieper, Sharon Peled, Clare M. Tempany, Hugo JWL Aerts, Ron Kikinis, Fiona M Fennessy, Andrey Fedorov
Date: 2018-07-16 00:00:00
Tasks: Image Registration

In this study we assessed the repeatability of the values of radiomics
features for small prostate tumors using test-retest Multiparametric Magnetic
Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative
image features can serve as biomarkers characterizing disease. For such
biomarkers to be useful, repeatability is a basic requirement, meaning its
value must remain stable between two scans, if the conditions remain stable. We
investigated repeatability of radiomics features under various preprocessing
and extraction configurations including various image normalization schemes,
different image pre-filtering, 2D vs 3D texture computation, and different bin
widths for image discretization. Image registration as means to re-identify
regions of interest across time points was evaluated against human-expert
segmented regions in both time points. Even though we found many radiomics
features and preprocessing combinations with a high repeatability (Intraclass
Correlation Coefficient (ICC) > 0.85), our results indicate that overall the
repeatability is highly sensitive to the processing parameters (under certain
configurations, it can be below 0.0). Image normalization, using a variety of
approaches considered, did not result in consistent improvements in
repeatability. There was also no consistent improvement of repeatability
through the use of pre-filtering options, or by using image registration
between timepoints to improve consistency of the region of interest
localization. Based on these results we urge caution when interpreting
radiomics features and advise paying close attention to the processing
configuration details of reported results. Furthermore, we advocate reporting
all processing details in radiomics studies and strongly recommend making the
implementation available.
A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation
Authors: Max-Heinrich Laves, Jens Bicker, Lüder A. Kahrs, Tobias Ortmaier
Date: 2018-07-16 00:00:00
Tasks: Data Augmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Purpose Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer and robot aided interventions. Recent methods based on deep convolutional neural networks (CNN) have outperformed former heuristic methods. However, those methods were primarily evaluated on rigid, real-world environments. In this study, existing segmentation methods were evaluated for their use on a new dataset of transoral endoscopic exploration. Methods Four machine learning based methods SegNet, UNet, ENet and ErfNet were trained with supervision on a novel 7-class dataset of the human larynx. The dataset contains 536 manually segmented images from two patients during laser incisions. The Intersection-over-Union (IoU) evaluation metric was used to measure the accuracy of each method. Data augmentation and network ensembling were employed to increase segmentation accuracy. Stochastic inference was used to show uncertainties of the individual models. Patient-to-patient transfer was investigated using patient-specific fine-tuning. Results In this study, a weighted average ensemble network of UNet and ErfNet was best suited for the segmentation of laryngeal soft tissue with a mean IoU of 84.7 %. The highest efficiency was achieved by ENet with a mean inference time of 9.22 ms per image. It is shown that 10 additional images from a new patient are sufficient for patient-specific fine-tuning. Conclusion CNN-based methods for semantic segmentation are applicable to endoscopic images of laryngeal soft tissue. The segmentation can be used for active constraints or to monitor morphological changes and autonomously detect pathologies. Further improvements could be achieved by using a larger dataset or training the models in a self-supervised manner on additional unlabeled data.