pwc-archive/papers-with-abstracts huggingface dataset:
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
Authors: Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, Venkatesh Babu Radhakrishnan
Date: 2017-06-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Image Generation

A class of recent approaches for generating images, called Generative
Adversarial Networks (GAN), have been used to generate impressively realistic
images of objects, bedrooms, handwritten digits and a variety of other image
modalities. However, typical GAN-based approaches require large amounts of
training data to capture the diversity across the image modality. In this
paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and
limited training data scenarios. In our approach, we reparameterize the latent
generative space as a mixture model and learn the mixture model's parameters
along with those of GAN. This seemingly simple modification to the GAN
framework is surprisingly effective and results in models which enable
diversity in generated samples although trained with limited data. In our work,
we show that DeLiGAN can generate images of handwritten digits, objects and
hand-drawn sketches, all using limited amounts of data. To quantitatively
characterize intra-class diversity of generated samples, we also introduce a
modified version of "inception-score", a measure which has been found to
correlate well with human assessment of generated samples.
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
Authors: Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen
Date: 2017-06-07 00:00:00
Tasks: Event Detection, Sound Event Detection

In this paper, we propose the use of spatial and harmonic features in
combination with long short term memory (LSTM) recurrent neural network (RNN)
for automatic sound event detection (SED) task. Real life sound recordings
typically have many overlapping sound events, making it hard to recognize with
just mono channel audio. Human listeners have been successfully recognizing the
mixture of overlapping sound events using pitch cues and exploiting the stereo
(multichannel) audio signal available at their ears to spatially localize these
events. Traditionally SED systems have only been using mono channel audio,
motivated by the human listener we propose to extend them to use multichannel
audio. The proposed SED system is compared against the state of the art mono
channel method on the development subset of TUT sound events detection 2016
database. The usage of spatial and harmonic features are shown to improve the
performance of SED.
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Authors: Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
Date: 2017-06-07 00:00:00
Tasks: Computed Tomography (CT)

Measuring airways in chest computed tomography (CT) images is important for
characterizing diseases such as cystic fibrosis, yet very time-consuming to
perform manually. Machine learning algorithms offer an alternative, but need
large sets of annotated data to perform well. We investigate whether
crowdsourcing can be used to gather airway annotations which can serve directly
for measuring the airways, or as training data for the algorithms. We generate
image slices at known locations of airways and request untrained crowd workers
to outline the airway lumen and airway wall. Our results show that the workers
are able to interpret the images, but that the instructions are too complex,
leading to many unusable annotations. After excluding unusable annotations,
quantitative results show medium to high correlations with expert measurements
of the airways. Based on this positive experience, we describe a number of
further research directions and provide insight into the challenges of
crowdsourcing in medical images from the perspective of first-time users.
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition
Authors: Miroslav Malik, Sharath Adavanne, Konstantinos Drossos, Tuomas Virtanen, Dasa Ticha, Roman Jarina
Date: 2017-06-07 00:00:00
Tasks: Emotion Recognition, Music Emotion Recognition

This paper studies the emotion recognition from musical tracks in the
2-dimensional valence-arousal (V-A) emotional space. We propose a method based
on convolutional (CNN) and recurrent neural networks (RNN), having
significantly fewer parameters compared with the state-of-the-art method for
the same task. We utilize one CNN layer followed by two branches of RNNs
trained separately for arousal and valence. The method was evaluated using the
'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for
arousal and 0.268 for valence, which is the best result reported on this
dataset.
Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network
Authors: Sharath Adavanne, Pasi Pertilä, Tuomas Virtanen
Date: 2017-06-07 00:00:00
Tasks: Event Detection, Sound Event Detection

This paper proposes to use low-level spatial features extracted from
multichannel audio for sound event detection. We extend the convolutional
recurrent neural network to handle more than one type of these multichannel
features by learning from each of them separately in the initial stages. We
show that instead of concatenating the features of each channel into a single
feature vector the network learns sound events in multichannel audio better
when they are presented as separate layers of a volume. Using the proposed
spatial features over monaural features on the same network gives an absolute
F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and
2.7% on the TUT-SED 2009 dataset that is fifteen times larger.
Unsupervised Place Discovery for Place-Specific Change Classifier
Authors: Fei Xiaoxiao, Tanaka Kanji
Date: 2017-06-07 00:00:00
Tasks: Change Detection, Open-Ended Question Answering, Visual Place Recognition

In this study, we address the problem of supervised change detection for
robotic map learning applications, in which the aim is to train a
place-specific change classifier (e.g., support vector machine (SVM)) to
predict changes from a robot's view image. An open question is the manner in
which to partition a robot's workspace into places (e.g., SVMs) to maximize the
overall performance of change classifiers. This is a chicken-or-egg problem: if
we have a well-trained change classifier, partitioning the robot's workspace
into places is rather easy. However, training a change classifier requires a
set of place-specific training data. In this study, we address this novel
problem, which we term unsupervised place discovery. In addition, we present a
solution powered by convolutional-feature-based visual place recognition, and
validate our approach by applying it to two place-specific change classifiers,
namely, nuisance and anomaly predictors.
Are Saddles Good Enough for Deep Learning?
Authors: Adepu Ravi Sankar, Vineeth N. Balasubramanian
Date: 2017-06-07 00:00:00
Tasks: Deep Learning

Recent years have seen a growing interest in understanding deep neural
networks from an optimization perspective. It is understood now that converging
to low-cost local minima is sufficient for such models to become effective in
practice. However, in this work, we propose a new hypothesis based on recent
theoretical findings and empirical studies that deep neural network models
actually converge to saddle points with high degeneracy. Our findings from this
work are new, and can have a significant impact on the development of gradient
descent based methods for training deep networks. We validated our hypotheses
using an extensive experimental evaluation on standard datasets such as MNIST
and CIFAR-10, and also showed that recent efforts that attempt to escape
saddles finally converge to saddles with high degeneracy, which we define as
`good saddles'. We also verified the famous Wigner's Semicircle Law in our
experimental results.
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
Authors: Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne
Date: 2017-06-07 00:00:00
Tasks: Multiple Instance Learning

A method for automatically quantifying emphysema regions using
High-Resolution Computed Tomography (HRCT) scans of patients with chronic
obstructive pulmonary disease (COPD) that does not require manually annotated
scans for training is presented. HRCT scans of controls and of COPD patients
with diverse disease severity are acquired at two different centers. Textural
features from co-occurrence matrices and Gaussian filter banks are used to
characterize the lung parenchyma in the scans. Two robust versions of multiple
instance learning (MIL) classifiers, miSVM and MILES, are investigated. The
classifiers are trained with the weak labels extracted from the forced
expiratory volume in one minute (FEV$_1$) and diffusing capacity of the lungs
for carbon monoxide (DLCO). At test time, the classifiers output a patient
label indicating overall COPD diagnosis and local labels indicating the
presence of emphysema. The classifier performance is compared with manual
annotations by two radiologists, a classical density based method, and
pulmonary function tests (PFTs). The miSVM classifier performed better than
MILES on both patient and emphysema classification. The classifier has a
stronger correlation with PFT than the density based method, the percentage of
emphysema in the intersection of annotations from both radiologists, and the
percentage of emphysema annotated by one of the radiologists. The correlation
between the classifier and the PFT is only outperformed by the second
radiologist. The method is therefore promising for facilitating assessment of
emphysema and reducing inter-observer variability.
Epistemic Logic with Functional Dependency Operator
Authors: Yifeng Ding
Date: 2017-06-07 00:00:00

Epistemic logic with non-standard knowledge operators, especially the
"knowing-value" operator, has recently gathered much attention. With the
"knowing-value" operator, we can express knowledge of individual variables, but
not of the relations between them in general. In this paper, we propose a new
operator Kf to express knowledge of the functional dependencies between
variables. The semantics of this Kf operator uses a function domain which
imposes a constraint on what counts as a functional dependency relation. By
adjusting this function domain, different interesting logics arise, and in this
paper we axiomatize three such logics in a single agent setting. Then we show
how these three logics can be unified by allowing the function domain to vary
relative to different agents and possible worlds. A multiagent axiomatization
is given in this case.
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection
Authors: Sharath Adavanne, Konstantinos Drossos, Emre Çakır, Tuomas Virtanen
Date: 2017-06-07 00:00:00
Tasks: Bird Audio Detection, Data Augmentation, Domain Adaptation

This paper studies the detection of bird calls in audio segments using
stacked convolutional and recurrent neural networks. Data augmentation by
blocks mixing and domain adaptation using a novel method of test mixing are
proposed and evaluated in regard to making the method robust to unseen data.
The contributions of two kinds of acoustic features (dominant frequency and log
mel-band energy) and their combinations are studied in the context of bird
audio detection. Our best achieved AUC measure on five cross-validations of the
development data is 95.5% and 88.1% on the unseen evaluation data.
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
Authors: Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang
Date: 2016-08-15 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

In this paper, for the purpose of data centre energy consumption monitoring
and analysis, we propose to detect the running programs in a server by
classifying the observed power consumption series. Time series classification
problem has been extensively studied with various distance measurements
developed; also recently the deep learning based sequence models have been
proved to be promising. In this paper, we propose a novel distance measurement
and build a time series classification algorithm hybridizing nearest neighbour
and long short term memory (LSTM) neural network. More specifically, first we
propose a new distance measurement termed as Local Time Warping (LTW), which
utilizes a user-specified set for local warping, and is designed to be
non-commutative and non-dynamic programming. Second we hybridize the 1NN-LTW
and LSTM together. In particular, we combine the prediction probability vector
of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using
the power consumption data from a real data center, we show that the proposed
LTW can improve the classification accuracy of DTW from about 84% to 90%. Our
experimental results prove that the proposed LTW is competitive on our data set
compared with existed DTW variants and its non-commutative feature is indeed
beneficial. We also test a linear version of LTW and it can significantly
outperform existed linear runtime lower bound methods like LB_Keogh.
Furthermore, with the hybrid algorithm, for the power series classification
task we achieve an accuracy up to about 93%. Our research can inspire more
studies on time series distance measurement and the hybrid of the deep learning
models with other traditional models.
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Authors: Xiaoguang Han, Chang Gao, Yizhou Yu
Date: 2017-06-07 00:00:00
Tasks: Caricature

Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Authors: Suncong Zheng, Feng Wang, Hongyun Bao, Yuexing Hao, Peng Zhou, Bo Xu
Date: 2017-06-07 00:00:00
Proceeding: ACL 2017 7
Tasks: Joint Entity and Relation Extraction, Relation Extraction

Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and their relations directly, without identifying entities and
relations separately. We conduct experiments on a public dataset produced by
distant supervision method and the experimental results show that the tagging
based methods are better than most of the existing pipelined and joint learning
methods. What's more, the end-to-end model proposed in this paper, achieves the
best results on the public dataset.
Axioms in Model-based Planners
Authors: Shuwa Miura, Alex Fukunaga
Date: 2017-03-11 00:00:00
Tasks: model

Axioms can be used to model derived predicates in domain- independent
planning models. Formulating models which use axioms can sometimes result in
problems with much smaller search spaces and shorter plans than the original
model. Previous work on axiom-aware planners focused solely on state- space
search planners. We propose axiom-aware planners based on answer set
programming and integer programming. We evaluate them on PDDL domains with
axioms and show that they can exploit additional expressivity of axioms.
Priv'IT: Private and Sample Efficient Identity Testing
Authors: Bryan Cai, Constantinos Daskalakis, Gautam Kamath
Date: 2017-03-29 00:00:00
Tasks: Two-sample testing, Vocal Bursts Type Prediction

We develop differentially private hypothesis testing methods for the small
sample regime. Given a sample $\cal D$ from a categorical distribution $p$ over
some domain $\Sigma$, an explicitly described distribution $q$ over $\Sigma$,
some privacy parameter $\varepsilon$, accuracy parameter $\alpha$, and
requirements $\beta_{\rm I}$ and $\beta_{\rm II}$ for the type I and type II
errors of our test, the goal is to distinguish between $p=q$ and
$d_{\rm{TV}}(p,q) \geq \alpha$.
  We provide theoretical bounds for the sample size $|{\cal D}|$ so that our
method both satisfies $(\varepsilon,0)$-differential privacy, and guarantees
$\beta_{\rm I}$ and $\beta_{\rm II}$ type I and type II errors. We show that
differential privacy may come for free in some regimes of parameters, and we
always beat the sample complexity resulting from running the $\chi^2$-test with
noisy counts, or standard approaches such as repetition for endowing
non-private $\chi^2$-style statistics with differential privacy guarantees. We
experimentally compare the sample complexity of our method to that of recently
proposed methods for private hypothesis testing.
Imposing Hard Constraints on Deep Networks: Promises and Limitations
Authors: Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua
Date: 2017-06-07 00:00:00

Imposing constraints on the output of a Deep Neural Net is one way to improve
the quality of its predictions while loosening the requirements for labeled
training data. Such constraints are usually imposed as soft constraints by
adding new terms to the loss function that is minimized during training. An
alternative is to impose them as hard constraints, which has a number of
theoretical benefits but has not been explored so far due to the perceived
intractability of the problem.
  In this paper, we show that imposing hard constraints can in fact be done in
a computationally feasible way and delivers reasonable results. However, the
theoretical benefits do not materialize and the resulting technique is no
better than existing ones relying on soft constraints. We analyze the reasons
for this and hope to spur other researchers into proposing better solutions.
Network Sketching: Exploiting Binary Structure in Deep CNNs
Authors: Yiwen Guo, Anbang Yao, Hao Zhao, Yurong Chen
Date: 2017-06-07 00:00:00
Proceeding: CVPR 2017 7

Convolutional neural networks (CNNs) with deep architectures have
substantially advanced the state-of-the-art in computer vision tasks. However,
deep networks are typically resource-intensive and thus difficult to be
deployed on mobile devices. Recently, CNNs with binary weights have shown
compelling efficiency to the community, whereas the accuracy of such models is
usually unsatisfactory in practice. In this paper, we introduce network
sketching as a novel technique of pursuing binary-weight CNNs, targeting at
more faithful inference and better trade-off for practical applications. Our
basic idea is to exploit binary structure directly in pre-trained filter banks
and produce binary-weight models via tensor expansion. The whole process can be
treated as a coarse-to-fine model approximation, akin to the pencil drawing
steps of outlining and shading. To further speedup the generated models, namely
the sketches, we also propose an associative implementation of binary tensor
convolutions. Experimental results demonstrate that a proper sketch of AlexNet
(or ResNet) outperforms the existing binary-weight models by large margins on
the ImageNet large scale classification task, while the committed memory for
network parameters only exceeds a little.
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
Authors: Rishabh Mehrotra, Emine Yilmaz
Date: 2017-06-06 00:00:00

A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies.
Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics
Authors: Sacha Sokoloski
Date: 2015-12-22 00:00:00

In order to interact intelligently with objects in the world, animals must
first transform neural population responses into estimates of the dynamic,
unknown stimuli which caused them. The Bayesian solution to this problem is
known as a Bayes filter, which applies Bayes' rule to combine population
responses with the predictions of an internal model. In this paper we present a
method for learning to approximate a Bayes filter when the stimulus dynamics
are unknown. To do this we use the inferential properties of probabilistic
population codes to compute Bayes' rule, and train a neural network to compute
approximate predictions by the method of maximum likelihood. In particular, we
perform stochastic gradient descent on the negative log-likelihood with a novel
approximation of the gradient. We demonstrate our methods on a finite-state, a
linear, and a nonlinear filtering problem, and show how the hidden layer of the
neural network develops tuning curves which are consistent with findings in
experimental neuroscience.