pwc-archive/papers-with-abstracts huggingface dataset:
A Bayesian Filtering Algorithm for Gaussian Mixture Models
Authors: Adrian G. Wills, Johannes Hendriks, Christopher Renton, Brett Ninness
Date: 2017-05-16 00:00:00
Tasks: State Estimation

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.
Unsupervised Human Action Detection by Action Matching
Authors: Basura Fernando, Sareh Shirazi, Stephen Gould
Date: 2016-12-02 00:00:00
Tasks: Action Detection, Activity Recognition

We propose a new task of unsupervised action detection by action matching.
Given two long videos, the objective is to temporally detect all pairs of
matching video segments. A pair of video segments are matched if they share the
same human action. The task is category independent---it does not matter what
action is being performed---and no supervision is used to discover such video
segments. Unsupervised action detection by action matching allows us to align
videos in a meaningful manner. As such, it can be used to discover new action
categories or as an action proposal technique within, say, an action detection
pipeline. Moreover, it is a useful pre-processing step for generating video
highlights, e.g., from sports videos.
  We present an effective and efficient method for unsupervised action
detection. We use an unsupervised temporal encoding method and exploit the
temporal consistency in human actions to obtain candidate action segments. We
evaluate our method on this challenging task using three activity recognition
benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action
detection benchmark and a new dataset called the IKEA dataset. On the MPII
Cooking dataset we detect action segments with a precision of 21.6% and recall
of 11.7% over 946 long video pairs and over 5000 ground truth action segments.
Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over
5094 ground truth action segment pairs.
Data clustering with edge domination in complex networks
Authors: Paulo Roberto Urio, Zhao Liang
Date: 2017-05-16 00:00:00
Tasks: Clustering, Community Detection

This paper presents a model for a dynamical system where particles dominate
edges in a complex network. The proposed dynamical system is then extended to
an application on the problem of community detection and data clustering. In
the case of the data clustering problem, 6 different techniques were simulated
on 10 different datasets in order to compare with the proposed technique. The
results show that the proposed algorithm performs well when prior knowledge of
the number of clusters is known to the algorithm.
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information
Authors: Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque, Jennifer Hicks, Eni Halilaj, Christopher Ré, Scott Delp
Date: 2017-05-13 00:00:00
Tasks: Time Series, Time Series Analysis

In healthcare applications, temporal variables that encode movement, health
status and longitudinal patient evolution are often accompanied by rich
structured information such as demographics, diagnostics and medical exam data.
However, current methods do not jointly optimize over structured covariates and
time series in the feature extraction process. We present ShortFuse, a method
that boosts the accuracy of deep learning models for time series by explicitly
modeling temporal interactions and dependencies with structured covariates.
ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the
covariates via weights that are shared across the temporal domain. ShortFuse
outperforms competing models by 3% on two biomedical applications, forecasting
osteoarthritis-related cartilage degeneration and predicting surgical outcomes
for cerebral palsy patients, matching or exceeding the accuracy of models that
use features engineered by domain experts.
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Authors: Franck Dernoncourt, Ji Young Lee, Peter Szolovits
Date: 2017-05-16 00:00:00
Proceeding: EMNLP 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone.
WordFence: Text Detection in Natural Images with Border Awareness
Authors: Andrei Polzounov, Artsiom Ablavatski, Sergio Escalera, Shijian Lu, Jianfei Cai
Date: 2017-05-15 00:00:00
Tasks: Semantic Segmentation, Text Detection

In recent years, text recognition has achieved remarkable success in
recognizing scanned document text. However, word recognition in natural images
is still an open problem, which generally requires time consuming
post-processing steps. We present a novel architecture for individual word
detection in scene images based on semantic segmentation. Our contributions are
twofold: the concept of WordFence, which detects border areas surrounding each
individual word and a novel pixelwise weighted softmax loss function which
penalizes background and emphasizes small text regions. WordFence ensures that
each word is detected individually, and the new loss function provides a strong
training signal to both text and word border localization. The proposed
technique avoids intensive post-processing, producing an end-to-end word
detection system. We achieve superior localization recall on common benchmark
datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT.
Furthermore, our end-to-end word recognition system achieves state-of-the-art
86% F-Score on ICDAR13.
Sharp Minima Can Generalize For Deep Nets
Authors: Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio
Date: 2017-03-15 00:00:00
Proceeding: ICML 2017 8

Despite their overwhelming capacity to overfit, deep learning architectures
tend to generalize relatively well to unseen data, allowing them to be deployed
in practice. However, explaining why this is the case is still an open area of
research. One standing hypothesis that is gaining popularity, e.g. Hochreiter &
Schmidhuber (1997); Keskar et al. (2017), is that the flatness of minima of the
loss function found by stochastic gradient based methods results in good
generalization. This paper argues that most notions of flatness are problematic
for deep models and can not be directly applied to explain generalization.
Specifically, when focusing on deep networks with rectifier units, we can
exploit the particular geometry of parameter space induced by the inherent
symmetries that these architectures exhibit to build equivalent models
corresponding to arbitrarily sharper minima. Furthermore, if we allow to
reparametrize a function, the geometry of its parameters can change drastically
without affecting its generalization properties.
Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results
Authors: Ping Tak Peter Tang, Tsung-Han Lin, Mike Davies
Date: 2017-05-15 00:00:00

In a spiking neural network (SNN), individual neurons operate autonomously
and only communicate with other neurons sparingly and asynchronously via spike
signals. These characteristics render a massively parallel hardware
implementation of SNN a potentially powerful computer, albeit a non von Neumann
one. But can one guarantee that a SNN computer solves some important problems
reliably? In this paper, we formulate a mathematical model of one SNN that can
be configured for a sparse coding problem for feature extraction. With a
moderate but well-defined assumption, we prove that the SNN indeed solves
sparse coding. To the best of our knowledge, this is the first rigorous result
of this kind.
Handwritten Urdu Character Recognition using 1-Dimensional BLSTM Classifier
Authors: Saad Bin Ahmed, Saeeda Naz, Salahuddin Swati, Muhammad Imran Razzak
Date: 2017-05-15 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

The recognition of cursive script is regarded as a subtle task in optical
character recognition due to its varied representation. Every cursive script
has different nature and associated challenges. As Urdu is one of cursive
language that is derived from Arabic script, thats why it nearly shares the
same challenges and difficulties even more harder. We can categorized Urdu and
Arabic language on basis of its script they use. Urdu is mostly written in
Nastaliq style whereas, Arabic follows Naskh style of writing. This paper
presents new and comprehensive Urdu handwritten offline database name
Urdu-Nastaliq Handwritten Dataset (UNHD). Currently, there is no standard and
comprehensive Urdu handwritten dataset available publicly for researchers. The
acquired dataset covers commonly used ligatures that were written by 500
writers with their natural handwriting on A4 size paper. We performed
experiments using recurrent neural networks and reported a significant accuracy
for handwritten Urdu character recognition.
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
Authors: Mehmet Turan, Yasin Almalioglu, Helder Araujo, Ender Konukoglu, Metin Sitti
Date: 2017-05-15 00:00:00
Tasks: Diagnostic, Simultaneous Localization and Mapping

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups have made significant progress to turn such passive capsule
endoscopes into robotic active capsule endoscopes to achieve almost all
functions of current active flexible endoscopes. However, the use of robotic
capsule endoscopy still has some challenges. One such challenge is the precise
localization of such active devices in 3D world, which is essential for a
precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of
the explored inner organ could assist the doctors to make more intuitive and
correct diagnosis. In this paper, we propose to our knowledge for the first
time in literature a visual simultaneous localization and mapping (SLAM) method
specifically developed for endoscopic capsule robots. The proposed RGB-Depth
SLAM method is capable of capturing comprehensive dense globally consistent
surfel-based maps of the inner organs explored by an endoscopic capsule robot
in real time. This is achieved by using dense frame-to-model camera tracking
and windowed surfelbased fusion coupled with frequent model refinement through
non-rigid surface deformations.
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
Authors: Mehmet Turan, Yasin Almalioglu, Ender Konukoglu, Metin Sitti
Date: 2017-05-15 00:00:00
Tasks: CPU, Translation

We present a robust deep learning based 6 degrees-of-freedom (DoF)
localization system for endoscopic capsule robots. Our system mainly focuses on
localization of endoscopic capsule robots inside the GI tract using only visual
information captured by a mono camera integrated to the robot. The proposed
system is a 23-layer deep convolutional neural network (CNN) that is capable to
estimate the pose of the robot in real time using a standard CPU. The dataset
for the evaluation of the system was recorded inside a surgical human stomach
model with realistic surface texture, softness, and surface liquid properties
so that the pre-trained CNN architecture can be transferred confidently into a
real endoscopic scenario. An average error of 7:1% and 3:4% for translation and
rotation has been obtained, respectively. The results accomplished from the
experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images
performs accurately inside the GI tract and is robust to various challenges
posed by reflection distortions, lens imperfections, vignetting, noise, motion
blur, low resolution, and lack of unique landmarks to track.
Logic-based Clustering and Learning for Time-Series Data
Authors: Marcell Vazquez-Chanlatte, Jyotirmoy V. Deshmukh, Xiaoqing Jin, Sanjit A. Seshia
Date: 2016-12-22 00:00:00
Tasks: Clustering, General Classification, Time Series, Time Series Analysis

To effectively analyze and design cyberphysical systems (CPS), designers
today have to combat the data deluge problem, i.e., the burden of processing
intractably large amounts of data produced by complex models and experiments.
In this work, we utilize monotonic Parametric Signal Temporal Logic (PSTL) to
design features for unsupervised classification of time series data. This
enables using off-the-shelf machine learning tools to automatically cluster
similar traces with respect to a given PSTL formula. We demonstrate how this
technique produces interpretable formulas that are amenable to analysis and
understanding using a few representative examples. We illustrate this with case
studies related to automotive engine testing, highway traffic analysis, and
auto-grading massively open online courses.
A statistical physics approach to learning curves for the Inverse Ising problem
Authors: Ludovica Bachschmid-Romano, Manfred Opper
Date: 2017-05-15 00:00:00

Using methods of statistical physics, we analyse the error of learning
couplings in large Ising models from independent data (the inverse Ising
problem). We concentrate on learning based on local cost functions, such as the
pseudo-likelihood method for which the couplings are inferred independently for
each spin. Assuming that the data are generated from a true Ising model, we
compute the reconstruction error of the couplings using a combination of the
replica method with the cavity approach for densely connected systems. We show
that an explicit estimator based on a quadratic cost function achieves minimal
reconstruction error, but requires the length of the true coupling vector as
prior knowledge. A simple mean field estimator of the couplings which does not
need such knowledge is asymptotically optimal, i.e. when the number of
observations is much large than the number of spins. Comparison of the theory
with numerical simulations shows excellent agreement for data generated from
two models with random couplings in the high temperature region: a model with
independent couplings (Sherrington-Kirkpatrick model), and a model where the
matrix of couplings has a Wishart distribution.
Learning Probabilistic Programs Using Backpropagation
Authors: Avi Pfeffer
Date: 2017-05-15 00:00:00
Tasks: Probabilistic Programming

Probabilistic modeling enables combining domain knowledge with learning from
data, thereby supporting learning from fewer training instances than purely
data-driven methods. However, learning probabilistic models is difficult and
has not achieved the level of performance of methods such as deep neural
networks on many tasks. In this paper, we attempt to address this issue by
presenting a method for learning the parameters of a probabilistic program
using backpropagation. Our approach opens the possibility to building deep
probabilistic programming models that are trained in a similar way to neural
networks.
Probabilistically Safe Policy Transfer
Authors: David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel
Date: 2017-05-15 00:00:00

Although learning-based methods have great potential for robotics, one
concern is that a robot that updates its parameters might cause large amounts
of damage before it learns the optimal policy. We formalize the idea of safe
learning in a probabilistic sense by defining an optimization problem: we
desire to maximize the expected return while keeping the expected damage below
a given safety limit. We study this optimization for the case of a robot
manipulator with safety-based torque limits. We would like to ensure that the
damage constraint is maintained at every step of the optimization and not just
at convergence. To achieve this aim, we introduce a novel method which predicts
how modifying the torque limit, as well as how updating the policy parameters,
might affect the robot's safety. We show through a number of experiments that
our approach allows the robot to improve its performance while ensuring that
the expected damage constraint is not violated during the learning process.
Maximum Selection and Ranking under Noisy Comparisons
Authors: Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh
Date: 2017-05-15 00:00:00
Proceeding: ICML 2017 8

We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking for general
probabilistic models whose comparisons probabilities satisfy strong stochastic
transitivity and stochastic triangle inequality. Modifying the popular knockout
tournament, we propose a maximum-selection algorithm that uses
$\mathcal{O}\left(\frac{n}{\epsilon^2}\log \frac{1}{\delta}\right)$
comparisons, a number tight up to a constant factor. We then derive a general
framework that improves the performance of many ranking algorithms, and combine
it with merge sort and binary search to obtain a ranking algorithm that uses
$\mathcal{O}\left(\frac{n\log n (\log \log n)^3}{\epsilon^2}\right)$
comparisons for any $\delta\ge\frac1n$, a number optimal up to a $(\log \log
n)^3$ factor.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Authors: Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, Devi Parikh
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Problems at the intersection of vision and language are of significant
importance both as challenging research questions and for the rich set of
applications they enable. However, inherent structure in our world and bias in
our language tend to be a simpler signal for learning than visual modalities,
resulting in models that ignore visual information, leading to an inflated
sense of their capability.
  We propose to counter these language priors for the task of Visual Question
Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance
the popular VQA dataset by collecting complementary images such that every
question in our balanced dataset is associated with not just a single image,
but rather a pair of similar images that result in two different answers to the
question. Our dataset is by construction more balanced than the original VQA
dataset and has approximately twice the number of image-question pairs. Our
complete balanced dataset is publicly available at www.visualqa.org as part of
the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA
v2.0).
  We further benchmark a number of state-of-art VQA models on our balanced
dataset. All models perform significantly worse on our balanced dataset,
suggesting that these models have indeed learned to exploit language priors.
This finding provides the first concrete empirical evidence for what seems to
be a qualitative sense among practitioners.
  Finally, our data collection protocol for identifying complementary images
enables us to develop a novel interpretable model, which in addition to
providing an answer to the given (image, question) pair, also provides a
counter-example based explanation. Specifically, it identifies an image that is
similar to the original image, but it believes has a different answer to the
same question. This can help in building trust for machines among their users.
Curiosity-driven Exploration by Self-supervised Prediction
Authors: Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell
Date: 2017-05-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Prediction, Unsupervised Reinforcement Learning

In many real-world scenarios, rewards extrinsic to the agent are extremely
sparse, or absent altogether. In such cases, curiosity can serve as an
intrinsic reward signal to enable the agent to explore its environment and
learn skills that might be useful later in its life. We formulate curiosity as
the error in an agent's ability to predict the consequence of its own actions
in a visual feature space learned by a self-supervised inverse dynamics model.
Our formulation scales to high-dimensional continuous state spaces like images,
bypasses the difficulties of directly predicting pixels, and, critically,
ignores the aspects of the environment that cannot affect the agent. The
proposed approach is evaluated in two environments: VizDoom and Super Mario
Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where
curiosity allows for far fewer interactions with the environment to reach the
goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent
to explore more efficiently; and 3) generalization to unseen scenarios (e.g.
new levels of the same game) where the knowledge gained from earlier experience
helps the agent explore new places much faster than starting from scratch. Demo
video and code available at https://pathak22.github.io/noreward-rl/
Constrained Bayesian Networks: Theory, Optimization, and Applications
Authors: Paul Beaumont, Michael Huth
Date: 2017-05-15 00:00:00
Tasks: Bayesian Inference

We develop the theory and practice of an approach to modelling and
probabilistic inference in causal networks that is suitable when
application-specific or analysis-specific constraints should inform such
inference or when little or no data for the learning of causal network
structure or probability values at nodes are available. Constrained Bayesian
Networks generalize a Bayesian Network such that probabilities can be symbolic,
arithmetic expressions and where the meaning of the network is constrained by
finitely many formulas from the theory of the reals. A formal semantics for
constrained Bayesian Networks over first-order logic of the reals is given,
which enables non-linear and non-convex optimisation algorithms that rely on
decision procedures for this logic, and supports the composition of several
constrained Bayesian Networks. A non-trivial case study in arms control, where
few or no data are available to assess the effectiveness of an arms inspection
process, evaluates our approach. An open-access prototype implementation of
these foundations and their algorithms uses the SMT solver Z3 as decision
procedure, leverages an open-source package for Bayesian inference to symbolic
computation, and is evaluated experimentally.