pwc-archive/papers-with-abstracts huggingface dataset:
Underwater object classification using scattering transform of sonar signals
Authors: Naoki Saito, David S. Weber
Date: 2017-07-11 00:00:00
Tasks: Binary Classification, Classification, General Classification, Object, Translation

In this paper, we apply the scattering transform (ST), a nonlinear map based
off of a convolutional neural network (CNN), to classification of underwater
objects using sonar signals. The ST formalizes the observation that the filters
learned by a CNN have wavelet like structure. We achieve effective binary
classification both on a real dataset of Unexploded Ordinance (UXOs), as well
as synthetically generated examples. We also explore the effects on the
waveforms with respect to changes in the object domain (e.g., translation,
rotation, and acoustic impedance, etc.), and examine the consequences coming
from theoretical results for the scattering transform. We show that the
scattering transform is capable of excellent classification on both the
synthetic and real problems, thanks to having more quasi-invariance properties
that are well-suited to translation and rotation of the object.
A Context-aware Attention Network for Interactive Question Answering
Authors: Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav
Date: 2016-12-22 00:00:00
Tasks: Decoder, Question Answering, Sentence

Neural network based sequence-to-sequence models in an encoder-decoder
framework have been successfully applied to solve Question Answering (QA)
problems, predicting answers from statements and questions. However, almost all
previous models have failed to consider detailed context information and
unknown states under which systems do not have enough information to answer
given questions. These scenarios with incomplete or ambiguous information are
very common in the setting of Interactive Question Answering (IQA). To address
this challenge, we develop a novel model, employing context-dependent
word-level attention for more accurate statement representations and
question-guided sentence-level attention for better context modeling. We also
generate unique IQA datasets to test our model, which will be made publicly
available. Employing these attention mechanisms, our model accurately
understands when it can output an answer or when it requires generating a
supplementary question for additional input depending on different contexts.
When available, user's feedback is encoded and directly applied to update
sentence-level attention to infer an answer. Extensive experiments on QA and
IQA datasets quantitatively demonstrate the effectiveness of our model with
significant improvement over state-of-the-art conventional QA models.
Understanding the Logical and Semantic Structure of Large Documents
Authors: Muhammad Mahbubur Rahman, Tim Finin
Date: 2017-09-03 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Information Retrieval, Language Modeling, Language Modelling, Question Answering, Retrieval

Current language understanding approaches focus on small documents, such as
newswire articles, blog posts, product reviews and discussion forum entries.
Understanding and extracting information from large documents like legal
briefs, proposals, technical manuals and research articles is still a
challenging task. We describe a framework that can analyze a large document and
help people to know where a particular information is in that document. We aim
to automatically identify and classify semantic sections of documents and
assign consistent and human-understandable labels to similar sections across
documents. A key contribution of our research is modeling the logical and
semantic structure of an electronic document. We apply machine learning
techniques, including deep learning, in our prototype system. We also make
available a dataset of information about a collection of scholarly articles
from the arXiv eprints collection that includes a wide range of metadata for
each article, including a table of contents, section labels, section
summarizations and more. We hope that this dataset will be a useful resource
for the machine learning and NLP communities in information retrieval,
content-based question answering and language modeling.
Neural Probabilistic Model for Non-projective MST Parsing
Authors: Xuezhe Ma, Eduard Hovy
Date: 2017-01-04 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: model, Sentence

In this paper, we propose a probabilistic parsing model, which defines a
proper conditional probability distribution over non-projective dependency
trees for a given sentence, using neural representations as inputs. The neural
network architecture is based on bi-directional LSTM-CNNs which benefits from
both word- and character-level representations automatically, by using
combination of bidirectional LSTM and CNN. On top of the neural network, we
introduce a probabilistic structured layer, defining a conditional log-linear
model over non-projective trees. We evaluate our model on 17 different
datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree
Theorem (Tutte, 1984), the partition functions and marginals can be computed
efficiently, leading to a straight-forward end-to-end model training procedure
via back-propagation. Our parser achieves state-of-the-art parsing performance
on nine datasets.
Safe Reinforcement Learning via Shielding
Authors: Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu
Date: 2017-08-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement learning algorithms discover policies that maximize reward, but
do not necessarily guarantee safety during learning or execution phases. We
introduce a new approach to learn optimal policies while enforcing properties
expressed in temporal logic. To this end, given the temporal logic
specification that is to be obeyed by the learning system, we propose to
synthesize a reactive system called a shield. The shield is introduced in the
traditional learning process in two alternative ways, depending on the location
at which the shield is implemented. In the first one, the shield acts each time
the learning agent is about to make a decision and provides a list of safe
actions. In the second way, the shield is introduced after the learning agent.
The shield monitors the actions from the learner and corrects them only if the
chosen action causes a violation of the specification. We discuss which
requirements a shield must meet to preserve the convergence guarantees of the
learner. Finally, we demonstrate the versatility of our approach on several
challenging reinforcement learning scenarios.
Formalization, Mechanization and Automation of Gödel's Proof of God's Existence
Authors: Christoph Benzmüller, Bruno Woltzenlogel Paleo
Date: 2013-08-21 00:00:00

G\"odel's ontological proof has been analysed for the first-time with an
unprecedent degree of detail and formality with the help of higher-order
theorem provers. The following has been done (and in this order): A detailed
natural deduction proof. A formalization of the axioms, definitions and
theorems in the TPTP THF syntax. Automatic verification of the consistency of
the axioms and definitions with Nitpick. Automatic demonstration of the
theorems with the provers LEO-II and Satallax. A step-by-step formalization
using the Coq proof assistant. A formalization using the Isabelle proof
assistant, where the theorems (and some additional lemmata) have been automated
with Sledgehammer and Metis.
Decision Stream: Cultivating Deep Decision Trees
Authors: Dmitry Ignatov, Andrey Ignatov
Date: 2017-04-25 00:00:00
Tasks: Classification, feature selection, General Classification, image-classification, Image Classification, regression, Sentiment Analysis, Twitter Sentiment Analysis

Various modifications of decision trees have been extensively used during the
past years due to their high efficiency and interpretability. Tree node
splitting based on relevant feature selection is a key step of decision tree
learning, at the same time being their major shortcoming: the recursive nodes
partitioning leads to geometric reduction of data quantity in the leaf nodes,
which causes an excessive model complexity and data overfitting. In this paper,
we present a novel architecture - a Decision Stream, - aimed to overcome this
problem. Instead of building a tree structure during the learning process, we
propose merging nodes from different branches based on their similarity that is
estimated with two-sample test statistics, which leads to generation of a deep
directed acyclic graph of decision rules that can consist of hundreds of
levels. To evaluate the proposed solution, we test it on several common machine
learning problems - credit scoring, twitter sentiment analysis, aircraft flight
control, MNIST and CIFAR image classification, synthetic data classification
and regression. Our experimental results reveal that the proposed approach
significantly outperforms the standard decision tree learning methods on both
regression and classification tasks, yielding a prediction error decrease up to
35%.
An Improved Algorithm for E-Generalization
Authors: Jochen Burghardt
Date: 2017-09-03 00:00:00

E-generalization computes common generalizations of given ground terms w.r.t.
a given equational background theory E. In 2005 [arXiv:1403.8118], we had
presented a computation approach based on standard regular tree grammar
algorithms, and a Prolog prototype implementation. In this report, we present
algorithmic improvements, prove them correct and complete, and give some
details of an efficiency-oriented implementation in C that allows us to handle
problems larger by several orders of magnitude.
Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints
Authors: Ali Khodabakhsh, Evdokia Nikolova
Date: 2016-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization

We consider the problem of maximizing a non-monotone DR-submodular function
subject to a cardinality constraint. Diminishing returns (DR) submodularity is
a generalization of the diminishing returns property for functions defined over
the integer lattice. This generalization can be used to solve many machine
learning or combinatorial optimization problems such as optimal budget
allocation, revenue maximization, etc. In this work we propose the first
polynomial-time approximation algorithms for non-monotone constrained
maximization. We implement our algorithms for a revenue maximization problem
with a real-world dataset to check their efficiency and performance.
Formalising Type-Logical Grammars in Agda
Authors: Wen Kokke
Date: 2017-09-03 00:00:00
Tasks: Sentence, Vocal Bursts Type Prediction

In recent years, the interest in using proof assistants to formalise and
reason about mathematics and programming languages has grown. Type-logical
grammars, being closely related to type theories and systems used in functional
programming, are a perfect candidate to next apply this curiosity to. The
advantages of using proof assistants is that they allow one to write formally
verified proofs about one's type-logical systems, and that any theory, once
implemented, can immediately be computed with. The downside is that in many
cases the formal proofs are written as an afterthought, are incomplete, or use
obtuse syntax. This makes it that the verified proofs are often much more
difficult to read than the pen-and-paper proofs, and almost never directly
published. In this paper, we will try to remedy that by example.
  Concretely, we use Agda to model the Lambek-Grishin calculus, a grammar logic
with a rich vocabulary of type-forming operations. We then present a verified
procedure for cut elimination in this system. Then we briefly outline a CPS
translation from proofs in the Lambek-Grishin calculus to programs in Agda. And
finally, we will put our system to use in the analysis of a simple example
sentence.
Human Detection and Tracking for Video Surveillance A Cognitive Science Approach
Authors: Vandit Gajjar, Ayesha Gurnani, Yash Khandhediya
Date: 2017-09-03 00:00:00
Tasks: General Classification, Human Detection, Saliency Prediction

With crimes on the rise all around the world, video surveillance is becoming
more important day by day. Due to the lack of human resources to monitor this
increasing number of cameras manually new computer vision algorithms to perform
lower and higher level tasks are being developed. We have developed a new
method incorporating the most acclaimed Histograms of Oriented Gradients the
theory of Visual Saliency and the saliency prediction model Deep Multi Level
Network to detect human beings in video sequences. Furthermore we implemented
the k Means algorithm to cluster the HOG feature vectors of the positively
detected windows and determined the path followed by a person in the video. We
achieved a detection precision of 83.11% and a recall of 41.27%. We obtained
these results 76.866 times faster than classification on normal images.
Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion
Authors: Maryam Karimi, Najmeh Soltanian, Shadrokh Samavi, Nader Karimi, S. M. Reza Soroushmehr, Kayvan Najarian
Date: 2017-09-03 00:00:00
Tasks: Image Quality Assessment

The use of 3D and stereo imaging is rapidly increasing. Compression,
transmission, and processing could degrade the quality of stereo images.
Quality assessment of such images is different than their 2D counterparts.
Metrics that represent 3D perception by human visual system (HVS) are expected
to assess stereoscopic quality more accurately. In this paper, inspired by
brain sensory/motor fusion process, two stereo images are fused together. Then
from every fused image two synthesized images are extracted. Effects of
different distortions on statistical distributions of the synthesized images
are shown. Based on the observed statistical changes, features are extracted
from these synthesized images. These features can reveal type and severity of
distortions. Then, a stacked neural network model is proposed, which learns the
extracted features and accurately evaluates the quality of stereo images. This
model is tested on 3D images of popular databases. Experimental results show
the superiority of this method over state of the art stereo image quality
assessment approaches
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable
Authors: Saed Khawaldeh, Tajwar Abrar Aleef, Usama Pervaiz, Vu Hoang Minh, Yeman Brhane Hagos
Date: 2017-09-03 00:00:00
Tasks: 3D Reconstruction, Object Recognition, Physical Simulations

3D reconstruction is a technique used in computer vision which has a wide
range of applications in areas like object recognition, city modelling, virtual
reality, physical simulations, video games and special effects. Previously, to
perform a 3D reconstruction, specialized hardwares were required. Such systems
were often very expensive and was only available for industrial or research
purpose. With the rise of the availability of high-quality low cost 3D sensors,
it is now possible to design inexpensive complete 3D scanning systems. The
objective of this work was to design an acquisition and processing system that
can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully
automated by building and integrating a turntable alongside the software. This
means the user can perform a full 3D scan only by a press of a few buttons from
our dedicated graphical user interface. Three main steps were followed to go
from acquisition of point clouds to the finished reconstructed 3D model. First,
our system acquires point cloud data of a person/object using inexpensive
camera sensor. Second, align and convert the acquired point cloud data into a
watertight mesh of good quality. Third, export the reconstructed model to a 3D
printer to obtain a proper 3D print of the model.
Ideological Sublations: Resolution of Dialectic in Population-based Optimization
Authors: S. Hossein Hosseini, Afshin Ebrahimi
Date: 2017-07-21 00:00:00
Tasks: compressed sensing, Evolutionary Algorithms, Philosophy

A population-based optimization algorithm was designed, inspired by two main
thinking modes in philosophy, both based on dialectic concept and
thesis-antithesis paradigm. They impose two different kinds of dialectics.
Idealistic and materialistic antitheses are formulated as optimization models.
Based on the models, the population is coordinated for dialectical
interactions. At the population-based context, the formulated optimization
models are reduced to a simple detection problem for each thinker (particle).
According to the assigned thinking mode to each thinker and her/his
measurements of corresponding dialectic with other candidate particles, they
deterministically decide to interact with a thinker in maximum dialectic with
their theses. The position of a thinker at maximum dialectic is known as an
available antithesis among the existing solutions. The dialectical interactions
at each ideological community are distinguished by meaningful distributions of
step-sizes for each thinking mode. In fact, the thinking modes are regarded as
exploration and exploitation elements of the proposed algorithm. The result is
a delicate balance without any requirement for adjustment of step-size
coefficients. Main parameter of the proposed algorithm is the number of
particles appointed to each thinking modes, or equivalently for each kind of
motions. An additional integer parameter is defined to boost the stability of
the final algorithm in some particular problems. The proposed algorithm is
evaluated by a testbed of 12 single-objective continuous benchmark functions.
Moreover, its performance and speed were highlighted in sparse reconstruction
and antenna selection problems, at the context of compressed sensing and
massive MIMO, respectively. The results indicate fast and efficient performance
in comparison with well-known evolutionary algorithms and dedicated
state-of-the-art algorithms.
Recovery Conditions and Sampling Strategies for Network Lasso
Authors: Alexandru Mara, Alexander Jung
Date: 2017-09-03 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

The network Lasso is a recently proposed convex optimization method for
machine learning from massive network structured datasets, i.e., big data over
networks. It is a variant of the well-known least absolute shrinkage and
selection operator (Lasso), which is underlying many methods in learning and
signal processing involving sparse models. Highly scalable implementations of
the network Lasso can be obtained by state-of-the art proximal methods, e.g.,
the alternating direction method of multipliers (ADMM). By generalizing the
concept of the compatibility condition put forward by van de Geer and Buehlmann
as a powerful tool for the analysis of plain Lasso, we derive a sufficient
condition, i.e., the network compatibility condition, on the underlying network
topology such that network Lasso accurately learns a clustered underlying graph
signal. This network compatibility condition relates the location of the
sampled nodes with the clustering structure of the network. In particular, the
NCC informs the choice of which nodes to sample, or in machine learning terms,
which data points provide most information if labeled.
Neural Machine Translation Training in a Multi-Domain Scenario
Authors: Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Yonatan Belinkov, Stephan Vogel
Date: 2017-08-29 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Machine Translation, Translation

In this paper, we explore alternative ways to train a neural machine
translation system in a multi-domain scenario. We investigate data
concatenation (with fine tuning), model stacking (multi-level fine tuning),
data selection and multi-model ensemble. Our findings show that the best
translation quality can be achieved by building an initial system on a
concatenation of available out-of-domain data and then fine-tuning it on
in-domain data. Model stacking works best when training begins with the
furthest out-of-domain data and the model is incrementally fine-tuned with the
next furthest domain and so on. Data selection did not give the best results,
but can be considered as a decent compromise between training time and
translation quality. A weighted ensemble of different individual models
performed better than data selection. It is beneficial in a scenario when there
is no time for fine-tuning an already trained model.
The placement of the head that maximizes predictability. An information theoretic approach
Authors: Ramon Ferrer-i-Cancho
Date: 2017-05-28 00:00:00
Tasks: Diversity

The minimization of the length of syntactic dependencies is a
well-established principle of word order and the basis of a mathematical theory
of word order. Here we complete that theory from the perspective of information
theory, adding a competing word order principle: the maximization of
predictability of a target element. These two principles are in conflict: to
maximize the predictability of the head, the head should appear last, which
maximizes the costs with respect to dependency length minimization. The
implications of such a broad theoretical framework to understand the
optimality, diversity and evolution of the six possible orderings of subject,
object and verb are reviewed.
The Little Engine that Could: Regularization by Denoising (RED)
Authors: Yaniv Romano, Michael Elad, Peyman Milanfar
Date: 2016-11-09 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising, Super-Resolution

Removal of noise from an image is an extensively studied problem in image
processing. Indeed, the recent advent of sophisticated and highly effective
denoising algorithms lead some to believe that existing methods are touching
the ceiling in terms of noise removal performance. Can we leverage this
impressive achievement to treat other tasks in image processing? Recent work
has answered this question positively, in the form of the Plug-and-Play Prior
($P^3$) method, showing that any inverse problem can be handled by sequentially
applying image denoising steps. This relies heavily on the ADMM optimization
technique in order to obtain this chained denoising interpretation.
  Is this the only way in which tasks in image processing can exploit the image
denoising engine? In this paper we provide an alternative, more powerful and
more flexible framework for achieving the same goal. As opposed to the $P^3$
method, we offer Regularization by Denoising (RED): using the denoising engine
in defining the regularization of the inverse problem. We propose an explicit
image-adaptive Laplacian-based regularization functional, making the overall
objective functional clearer and better defined. With a complete flexibility to
choose the iterative optimization procedure for minimizing the above
functional, RED is capable of incorporating any image denoising algorithm,
treat general inverse problems very effectively, and is guaranteed to converge
to the globally optimal result. We test this approach and demonstrate
state-of-the-art results in the image deblurring and super-resolution problems.
Disentangling ASR and MT Errors in Speech Translation
Authors: Ngoc-Tien Le, Benjamin Lecouteux, Laurent Besacier
Date: 2017-09-03 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Translation

The main aim of this paper is to investigate automatic quality assessment for
spoken language translation (SLT). More precisely, we investigate SLT errors
that can be due to transcription (ASR) or to translation (MT) modules. This
paper investigates automatic detection of SLT errors using a single classifier
based on joint ASR and MT features. We evaluate both 2-class (good/bad) and
3-class (good/badASR/badMT ) labeling tasks. The 3-class problem necessitates
to disentangle ASR and MT errors in the speech translation output and we
propose two label extraction methods for this non trivial step. This enables -
as a by-product - qualitative analysis on the SLT errors and their origin (are
they due to transcription or to translation step?) on our large in-house corpus
for French-to-English speech translation.