pwc-archive/papers-with-abstracts huggingface dataset:
A Pipeline for Lenslet Light Field Quality Enhancement
Authors: Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic
Date: 2018-08-16 00:00:00
Tasks: Denoising

In recent years, light fields have become a major research topic and their
applications span across the entire spectrum of classical image processing.
Among the different methods used to capture a light field are the lenslet
cameras, such as those developed by Lytro. While these cameras give a lot of
freedom to the user, they also create light field views that suffer from a
number of artefacts. As a result, it is common to ignore a significant subset
of these views when doing high-level light field processing. We propose a
pipeline to process light field views, first with an enhanced processing of RAW
images to extract subaperture images, then a colour correction process using a
recent colour transfer algorithm, and finally a denoising process using a state
of the art light field denoising approach. We show that our method improves the
light field quality on many levels, by reducing ghosting artefacts and noise,
as well as retrieving more accurate and homogeneous colours across the
sub-aperture images.
On the Decision Boundary of Deep Neural Networks
Authors: Yu Li, Lizhong Ding, Xin Gao
Date: 2018-08-16 00:00:00
Tasks: Deep Learning

While deep learning models and techniques have achieved great empirical
success, our understanding of the source of success in many aspects remains
very limited. In an attempt to bridge the gap, we investigate the decision
boundary of a production deep learning architecture with weak assumptions on
both the training data and the model. We demonstrate, both theoretically and
empirically, that the last weight layer of a neural network converges to a
linear SVM trained on the output of the last hidden layer, for both the binary
case and the multi-class case with the commonly used cross-entropy loss.
Furthermore, we show empirically that training a neural network as a whole,
instead of only fine-tuning the last weight layer, may result in better bias
constant for the last weight layer, which is important for generalization. In
addition to facilitating the understanding of deep learning, our result can be
helpful for solving a broad range of practical problems of deep learning, such
as catastrophic forgetting and adversarial attacking. The experiment codes are
available at https://github.com/lykaust15/NN_decision_boundary
Typhoon track prediction using satellite images in a Generative Adversarial Network
Authors: Mario Rüttgers, Sangseung Lee, Donghyun You
Date: 2018-08-16 00:00:00
Tasks: Generative Adversarial Network, motion prediction

Tracks of typhoons are predicted using satellite images as input for a
Generative Adversarial Network (GAN). The satellite images have time gaps of 6
hours and are marked with a red square at the location of the typhoon center.
The GAN uses images from the past to generate an image one time step ahead. The
generated image shows the future location of the typhoon center, as well as the
future cloud structures. The errors between predicted and real typhoon centers
are measured quantitatively in kilometers. 42.4% of all typhoon center
predictions have absolute errors of less than 80 km, 32.1% lie within a range
of 80 - 120 km and the remaining 25.5% have accuracies above 120 km. The
relative error sets the above mentioned absolute error in relation to the
distance that has been traveled by a typhoon over the past 6 hours. High
relative errors are found in three types of situations, when a typhoon moves on
the open sea far away from land, when a typhoon changes its course suddenly and
when a typhoon is about to hit the mainland. The cloud structure prediction is
evaluated qualitatively. It is shown that the GAN is able to predict trends in
cloud motion. In order to improve both, the typhoon center and cloud motion
prediction, the present study suggests to add information about the sea surface
temperature, surface pressure and velocity fields to the input data.
Egocentric Gesture Recognition for Head-Mounted AR devices
Authors: Tejo Chalasani, Jan Ondrej, Aljosa Smolic
Date: 2018-08-16 00:00:00
Tasks: Data Augmentation, Gesture Recognition

Natural interaction with virtual objects in AR/VR environments makes for a
smooth user experience. Gestures are a natural extension from real world to
augmented space to achieve these interactions. Finding discriminating
spatio-temporal features relevant to gestures and hands in ego-view is the
primary challenge for recognising egocentric gestures. In this work we propose
a data driven end-to-end deep learning approach to address the problem of
egocentric gesture recognition, which combines an ego-hand encoder network to
find ego-hand features, and a recurrent neural network to discern temporally
discriminating features. Since deep learning networks are data intensive, we
propose a novel data augmentation technique using green screen capture to
alleviate the problem of ground truth annotation. In addition we publish a
dataset of 10 gestures performed in a natural fashion in front of a green
screen for training and the same 10 gestures performed in different natural
scenes without green screen for validation. We also present the results of our
network's performance in comparison to the state-of-the-art using the AirGest
dataset
Neural Architecture Search: A Survey
Authors: Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
Date: 2018-08-16 00:00:00
Tasks: Machine Translation, Neural Architecture Search, speech-recognition, Speech Recognition, Survey, Translation

Deep Learning has enabled remarkable progress over the last years on a
variety of tasks, such as image recognition, speech recognition, and machine
translation. One crucial aspect for this progress are novel neural
architectures. Currently employed architectures have mostly been developed
manually by human experts, which is a time-consuming and error-prone process.
Because of this, there is growing interest in automated neural architecture
search methods. We provide an overview of existing work in this field of
research and categorize them according to three dimensions: search space,
search strategy, and performance estimation strategy.
Computing Word Classes Using Spectral Clustering
Authors: Effi Levi, Saggy Herman, Ari Rappoport
Date: 2018-08-16 00:00:00
Tasks: Clustering, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Semantic Role Labeling, speech-recognition, Speech Recognition, Text Categorization

Clustering a lexicon of words is a well-studied problem in natural language
processing (NLP). Word clusters are used to deal with sparse data in
statistical language processing, as well as features for solving various NLP
tasks (text categorization, question answering, named entity recognition and
others).
  Spectral clustering is a widely used technique in the field of image
processing and speech recognition. However, it has scarcely been explored in
the context of NLP; specifically, the method used in this (Meila and Shi, 2001)
has never been used to cluster a general word lexicon.
  We apply spectral clustering to a lexicon of words, evaluating the resulting
clusters by using them as features for solving two classical NLP tasks:
semantic role labeling and dependency parsing. We compare performance with
Brown clustering, a widely-used technique for word clustering, as well as with
other clustering methods. We show that spectral clusters produce similar
results to Brown clusters, and outperform other clustering methods. In
addition, we quantify the overlap between spectral and Brown clusters, showing
that each model captures some information which is uncaptured by the other.
Conceptual Domain Adaptation Using Deep Learning
Authors: Behrang Mehrparvar, Ricardo Vilalta
Date: 2018-08-16 00:00:00
Tasks: Deep Learning, Domain Adaptation

Deep learning has recently been shown to be instrumental in the problem of
domain adaptation, where the goal is to learn a model on a target domain using
a similar --but not identical-- source domain. The rationale for coupling both
techniques is the possibility of extracting common concepts across domains.
Considering (strictly) local representations, traditional deep learning assumes
common concepts must be captured in the same hidden units. We contend that
jointly training a model with source and target data using a single deep
network is prone to failure when there is inherently lower-level
representational discrepancy between the two domains; such discrepancy leads to
a misalignment of corresponding concepts in separate hidden units. We introduce
a search framework to correctly align high-level representations when training
deep networks; such framework leads to the notion of conceptual --as opposed to
representational-- domain adaptation.
EmbNum: Semantic labeling for numerical values with deep metric learning
Authors: Phuc Nguyen, Khai Nguyen, Ryutaro Ichise, Hideaki Takeda
Date: 2018-06-26 00:00:00
Tasks: Attribute, Metric Learning, Retrieval

Semantic labeling for numerical values is a task of assigning semantic labels
to unknown numerical attributes. The semantic labels could be numerical
properties in ontologies, instances in knowledge bases, or labeled data that
are manually annotated by domain experts. In this paper, we refer to semantic
labeling as a retrieval setting where the label of an unknown attribute is
assigned by the label of the most relevant attribute in labeled data. One of
the greatest challenges is that an unknown attribute rarely has the same set of
values with the similar one in the labeled data. To overcome the issue,
statistical interpretation of value distribution is taken into account.
However, the existing studies assume a specific form of distribution. It is not
appropriate in particular to apply open data where there is no knowledge of
data in advance. To address these problems, we propose a neural numerical
embedding model (EmbNum) to learn useful representation vectors for numerical
attributes without prior assumptions on the distribution of data. Then, the
"semantic similarities" between the attributes are measured on these
representation vectors by the Euclidean distance. Our empirical experiments on
City Data and Open Data show that EmbNum significantly outperforms
state-of-the-art methods for the task of numerical attribute semantic labeling
regarding effectiveness and efficiency.
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing
Authors: Anurag Dwarakanath, Manish Ahuja, Samarth Sikand, Raghotham M. Rao, R. P. Jagadeesh Chandra Bose, Neville Dubash, Sanjay Podder
Date: 2018-08-16 00:00:00
Tasks: BIG-bench Machine Learning, speech-recognition, Speech Recognition, Translation

We have recently witnessed tremendous success of Machine Learning (ML) in
practical applications. Computer vision, speech recognition and language
translation have all seen a near human level performance. We expect, in the
near future, most business applications will have some form of ML. However,
testing such applications is extremely challenging and would be very expensive
if we follow today's methodologies. In this work, we present an articulation of
the challenges in testing ML based applications. We then present our solution
approach, based on the concept of Metamorphic Testing, which aims to identify
implementation bugs in ML based image classifiers. We have developed
metamorphic relations for an application based on Support Vector Machine and a
Deep Learning based application. Empirical validation showed that our approach
was able to catch 71% of the implementation bugs in the ML applications.
Tool Breakage Detection using Deep Learning
Authors: Guang Li, Xin Yang, DuanBing Chen, Anxing Song, Yuke Fang, Junlin Zhou
Date: 2018-08-16 00:00:00
Tasks: Deep Learning, Management

In manufacture, steel and other metals are mainly cut and shaped during the
fabrication process by computer numerical control (CNC) machines. To keep high
productivity and efficiency of the fabrication process, engineers need to
monitor the real-time process of CNC machines, and the lifetime management of
machine tools. In a real manufacturing process, breakage of machine tools
usually happens without any indication, this problem seriously affects the
fabrication process for many years. Previous studies suggested many different
approaches for monitoring and detecting the breakage of machine tools. However,
there still exists a big gap between academic experiments and the complex real
fabrication processes such as the high demands of real-time detections, the
difficulty in data acquisition and transmission. In this work, we use the
spindle current approach to detect the breakage of machine tools, which has the
high performance of real-time monitoring, low cost, and easy to install. We
analyze the features of the current of a milling machine spindle through tools
wearing processes, and then we predict the status of tool breakage by a
convolutional neural network(CNN). In addition, we use a BP neural network to
understand the reliability of the CNN. The results show that our CNN approach
can detect tool breakage with an accuracy of 93%, while the best performance of
BP is 80%.
Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM
Authors: Szu-Wei Fu, Yu Tsao, Hsin-Te Hwang, Hsin-Min Wang
Date: 2018-08-16 00:00:00
Tasks: Speech Enhancement

Nowadays, most of the objective speech quality assessment tools (e.g.,
perceptual evaluation of speech quality (PESQ)) are based on the comparison of
the degraded/processed speech with its clean counterpart. The need of a
"golden" reference considerably restricts the practicality of such assessment
tools in real-world scenarios since the clean reference usually cannot be
accessed. On the other hand, human beings can readily evaluate the speech
quality without any reference (e.g., mean opinion score (MOS) tests), implying
the existence of an objective and non-intrusive (no clean reference needed)
quality assessment mechanism. In this study, we propose a novel end-to-end,
non-intrusive speech quality evaluation model, termed Quality-Net, based on
bidirectional long short-term memory. The evaluation of utterance-level quality
in Quality-Net is based on the frame-level assessment. Frame constraints and
sensible initializations of forget gate biases are applied to learn meaningful
frame-level quality assessment from the utterance-level quality label.
Experimental results show that Quality-Net can yield high correlation to PESQ
(0.9 for the noisy speech and 0.84 for the speech processed by speech
enhancement). We believe that Quality-Net has potential to be used in a wide
variety of applications of speech signal processing.
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2018-08-16 00:00:00
Tasks: Chord Recognition, Language Modelling

Common temporal models for automatic chord recognition model chord changes on
a frame-wise basis. Due to this fact, they are unable to capture musical
knowledge about chord progressions. In this paper, we propose a temporal model
that enables explicit modelling of chord changes and durations. We then apply
N-gram models and a neural-network-based acoustic model within this framework,
and evaluate the effect of model overconfidence. Our results show that model
overconfidence plays only a minor role (but target smoothing still improves the
acoustic model), and that stronger chord language models do improve recognition
results, however their effects are small compared to other domains.
Genre-Agnostic Key Classification With Convolutional Neural Networks
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2018-08-16 00:00:00
Tasks: Classification, General Classification

We propose modifications to the model structure and training procedure to a
recently introduced Convolutional Neural Network for musical key
classification. These modifications enable the network to learn a
genre-independent model that performs better than models trained for specific
music styles, which has not been the case in existing work. We analyse this
generalisation capability on three datasets comprising distinct genres. We then
evaluate the model on a number of unseen data sets, and show its superior
performance compared to the state of the art. Finally, we investigate the
model's performance on short excerpts of audio. From these experiments, we
conclude that models need to consider the harmonic coherence of the whole piece
when classifying the local key of short segments of audio.
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs
Authors: Sunil Prakash, Gaelan Gu
Date: 2018-08-16 00:00:00
Tasks: Depth Estimation, Depth Prediction, Position, Simultaneous Localization and Mapping

In this paper, we propose an novel implementation of a simultaneous
localization and mapping (SLAM) system based on a monocular camera from an
unmanned aerial vehicle (UAV) using Depth prediction performed with Capsule
Networks (CapsNet), which possess improvements over the drawbacks of the more
widely-used Convolutional Neural Networks (CNN). An Extended Kalman Filter will
assist in estimating the position of the UAV so that we are able to update the
belief for the environment. Results will be evaluated on a benchmark dataset to
portray the accuracy of our intended approach.
Improved Chord Recognition by Combining Duration and Harmonic Language Models
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2018-08-16 00:00:00
Tasks: Chord Recognition, Language Modeling, Language Modelling

Chord recognition systems typically comprise an acoustic model that predicts
chords for each audio frame, and a temporal model that casts these predictions
into labelled chord segments. However, temporal models have been shown to only
smooth predictions, without being able to incorporate musical information about
chord progressions. Recent research discovered that it might be the low
hierarchical level such models have been applied to (directly on audio frames)
which prevents learning musical relationships, even for expressive models such
as recurrent neural networks (RNNs). However, if applied on the level of chord
sequences, RNNs indeed can become powerful chord predictors. In this paper, we
disentangle temporal models into a harmonic language model---to be applied on
chord sequences---and a chord duration model that connects the chord-level
predictions of the language model to the frame-level predictions of the
acoustic model. In our experiments, we explore the impact of each model on the
chord recognition score, and show that using harmonic language and duration
models improves the results.
Active Distribution Learning from Indirect Samples
Authors: Samarth Gupta, Gauri Joshi, Osman Yağan
Date: 2018-08-16 00:00:00
Tasks: Privacy Preserving

This paper studies the problem of {\em learning} the probability distribution
$P_X$ of a discrete random variable $X$ using indirect and sequential samples.
At each time step, we choose one of the possible $K$ functions, $g_1, \ldots,
g_K$ and observe the corresponding sample $g_i(X)$. The goal is to estimate the
probability distribution of $X$ by using a minimum number of such sequential
samples. This problem has several real-world applications including inference
under non-precise information and privacy-preserving statistical estimation. We
establish necessary and sufficient conditions on the functions $g_1, \ldots,
g_K$ under which asymptotically consistent estimation is possible. We also
derive lower bounds on the estimation error as a function of total samples and
show that it is order-wise achievable. Leveraging these results, we propose an
iterative algorithm that i) chooses the function to observe at each step based
on past observations; and ii) combines the obtained samples to estimate $p_X$.
The performance of this algorithm is investigated numerically under various
scenarios, and shown to outperform baseline approaches.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Authors: Risheng Liu, Shichao Cheng, Yi He, Xin Fan, Zhouchen Lin, Zhongxuan Luo
Date: 2018-08-16 00:00:00
Tasks: Scheduling

Numerous tasks at the core of statistics, learning and vision areas are
specific cases of ill-posed inverse problems. Recently, learning-based (e.g.,
deep) iterative methods have been empirically shown to be useful for these
problems. Nevertheless, integrating learnable structures into iterations is
still a laborious process, which can only be guided by intuitions or empirical
insights. Moreover, there is a lack of rigorous analysis about the convergence
behaviors of these reimplemented iterations, and thus the significance of such
methods is a little bit vague. This paper moves beyond these limits and
proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and
provable paradigm for nonconvex inverse problems. Our theoretical analysis
reveals that FIMA allows us to generate globally convergent trajectories for
learning-based iterative methods. Meanwhile, the devised scheduling policies on
flexible modules should also be beneficial for classical numerical methods in
the nonconvex scenario. Extensive experiments on real applications verify the
superiority of FIMA.
Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection
Authors: Ruinan Zhang, Fanglan Zheng, Wei Min
Date: 2018-08-16 00:00:00
Tasks: Dynamic Time Warping, Fraud Detection

Due to the popularity of the Internet and smart mobile devices, more and more
financial transactions and activities have been digitalized. Compared to
traditional financial fraud detection strategies using credit-related features,
customers are generating a large amount of unstructured behavioral data every
second. In this paper, we propose an Recurrent Neural Netword (RNN) based
deep-learning structure integrated with Markov Transition Field (MTF) for
predicting online fraud behaviors using customer's interactions with websites
or smart-phone apps as a series of states. In practice, we tested and proved
that the proposed network structure for processing sequential behavioral data
could significantly boost fraud predictive ability comparing with the
multilayer perceptron network and distance based classifier with Dynamic Time
Warping(DTW) as distance metric.
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Authors: Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi
Date: 2018-08-16 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Common Sense Reasoning, Multiple-choice, Natural Language Inference, Question Answering

Given a partial description like "she opened the hood of the car," humans can
reason about the situation and anticipate what might come next ("then, she
examined the engine"). In this paper, we introduce the task of grounded
commonsense inference, unifying natural language inference and commonsense
reasoning.
  We present SWAG, a new dataset with 113k multiple choice questions about a
rich spectrum of grounded situations. To address the recurring challenges of
the annotation artifacts and human biases found in many existing datasets, we
propose Adversarial Filtering (AF), a novel procedure that constructs a
de-biased dataset by iteratively training an ensemble of stylistic classifiers,
and using them to filter the data. To account for the aggressive adversarial
filtering, we use state-of-the-art language models to massively oversample a
diverse set of potential counterfactuals. Empirical results demonstrate that
while humans can solve the resulting inference problems with high accuracy
(88%), various competitive models struggle on our task. We provide
comprehensive analysis that indicates significant opportunities for future
research.