pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Convolutional Networks for Image Recognition via a Discriminator
Authors: Shiqi Yang, Gang Peng
Date: 2018-07-06 00:00:00

In this paper, we introduce a simple but quite effective recognition
framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN.
The framework consists of two parallel CNNs, a discriminator and an extra
classifier which takes integrated features from parallel networks and gives
final prediction. The discriminator is core which drives parallel networks to
focus on different regions and learn different representations. The
corresponding training strategy is introduced to ensures utilization of
discriminator. We validate D-PCN with several CNN models on benchmark datasets:
CIFAR-100, and ImageNet, D-PCN enhances all models. In particular it yields
state of the art performance on CIFAR-100 compared with related works. We also
conduct visualization experiment on fine-grained Stanford Dogs dataset to
verify our motivation. Additionally, we apply D-PCN for segmentation on PASCAL
VOC 2012 and also find promotion.
Variance Reduction for Reinforcement Learning in Input-Driven Environments
Authors: Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh
Date: 2018-07-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Meta-Learning, MuJoCo, Object Tracking, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider reinforcement learning in input-driven environments, where an
exogenous, stochastic input process affects the dynamics of the system. Input
processes arise in many applications, including queuing systems, robotics
control with disturbances, and object tracking. Since the state dynamics and
rewards depend on the input process, the state alone provides limited
information for the expected future returns. Therefore, policy gradient methods
with standard state-dependent baselines suffer high variance during training.
We derive a bias-free, input-dependent baseline to reduce this variance, and
analytically show its benefits over state-dependent baselines. We then propose
a meta-learning approach to overcome the complexity of learning a baseline that
depends on a long sequence of inputs. Our experimental results show that across
environments from queuing systems, computer networks, and MuJoCo robotic
locomotion, input-dependent baselines consistently improve training stability
and result in better eventual policies.
Temporal graph-based clustering for historical record linkage
Authors: Charini Nanayakkara, Peter Christen, Thilina Ranbaduge
Date: 2018-07-06 00:00:00
Tasks: Clustering

Research in the social sciences is increasingly based on large and complex
data collections, where individual data sets from different domains are linked
and integrated to allow advanced analytics. A popular type of data used in such
a context are historical censuses, as well as birth, death, and marriage
certificates. Individually, such data sets however limit the types of studies
that can be conducted. Specifically, it is impossible to track individuals,
families, or households over time. Once such data sets are linked and family
trees spanning several decades are available it is possible to, for example,
investigate how education, health, mobility, employment, and social status
influence each other and the lives of people over two or even more generations.
A major challenge is however the accurate linkage of historical data sets which
is due to data quality and commonly also the lack of ground truth data being
available. Unsupervised techniques need to be employed, which can be based on
similarity graphs generated by comparing individual records. In this paper we
present initial results from clustering birth records from Scotland where we
aim to identify all births of the same mother and group siblings into clusters.
We extend an existing clustering technique for record linkage by incorporating
temporal constraints that must hold between births by the same mother, and
propose a novel greedy temporal clustering technique. Experimental results show
improvements over non-temporary approaches, however further work is needed to
obtain links of high quality.
Scalable Formal Concept Analysis algorithm for large datasets using Spark
Authors: Raghavendra K Chunduri, Aswani Kumar Cherukuri
Date: 2018-07-06 00:00:00
Tasks: graph construction

In the process of knowledge discovery and representation in large datasets
using formal concept analysis, complexity plays a major role in identifying all
the formal concepts and constructing the concept lattice(digraph of the
concepts). For identifying the formal concepts and constructing the digraph
from the identified concepts in very large datasets, various distributed
algorithms are available in the literature. However, the existing distributed
algorithms are not very well suitable for concept generation because it is an
iterative process. The existing algorithms are implemented using distributed
frameworks like MapReduce and Open MP, these frameworks are not appropriate for
iterative applications. Hence, in this paper we proposed efficient distributed
algorithms for both formal concept generation and concept lattice digraph
construction in large formal contexts using Apache Spark. Various performance
metrics are considered for the evaluation of the proposed work, the results of
the evaluation proves that the proposed algorithms are efficient for concept
generation and lattice graph construction in comparison with the existing
algorithms.
Dynamic Multimodal Instance Segmentation guided by natural language queries
Authors: Edgar Margffoy-Tuay, Juan C. Pérez, Emilio Botero, Pablo Arbeláez
Date: 2018-07-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Natural Language Queries, Object, Semantic Segmentation

We address the problem of segmenting an object given a natural language
expression that describes it. Current techniques tackle this task by either
(\textit{i}) directly or recursively merging linguistic and visual information
in the channel dimension and then performing convolutions; or by (\textit{ii})
mapping the expression to a space in which it can be thought of as a filter,
whose response is directly related to the presence of the object at a given
spatial coordinate in the image, so that a convolution can be applied to look
for the object. We propose a novel method that integrates these two insights in
order to fully exploit the recursive nature of language. Additionally, during
the upsampling process, we take advantage of the intermediate information
generated when downsampling the image, so that detailed segmentations can be
obtained. We compare our method against the state-of-the-art approaches in four
standard datasets, in which it surpasses all previous methods in six of eight
of the splits for this task.
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
Authors: Cheng-Wei Wu, Jen-Yu Liu, Yi-Hsuan Yang, Jyh-Shing R. Jang
Date: 2018-07-06 00:00:00
Tasks: Style Transfer

Can we make a famous rap singer like Eminem sing whatever our favorite song?
Singing style transfer attempts to make this possible, by replacing the vocal
of a song from the source singer to the target singer. This paper presents a
method that learns from unpaired data for singing style transfer using
generative adversarial networks.
Minutia Texture Cylinder Codes for fingerprint matching
Authors: Wajih Ullah Baig, Umar Munir, Waqas Ellahi, Adeel Ejaz, Kashif Sardar
Date: 2018-07-06 00:00:00

Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that
take into account minutiae information in a fingerprint image for fingerprint
matching. In this paper, we present a modification to the underlying
information of the MCC descriptor and show that using different features, the
accuracy of matching is highly affected by such changes. MCC originally being a
minutia only descriptor is transformed into a texture descriptor. The
transformation is from minutiae angular information to orientation, frequency
and energy information using Short Time Fourier Transform (STFT) analysis. The
minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC).
Based on a fixed set of parameters, the proposed changes to MCC show improved
performance on FVC 2002 and 2004 data sets and surpass the traditional MCC
performance.
Face-Cap: Image Captioning using Facial Expression Analysis
Authors: Omid Mohamad Nezami, Mark Dras, Peter Anderson, Len Hamey
Date: 2018-07-06 00:00:00
Tasks: Descriptive, Image Captioning

Image captioning is the process of generating a natural language description
of an image. Most current image captioning models, however, do not take into
account the emotional aspect of an image, which is very relevant to activities
and interpersonal relationships represented therein. Towards developing a model
that can produce human-like captions incorporating these, we use facial
expression features extracted from images including human faces, with the aim
of improving the descriptive ability of the model. In this work, we present two
variants of our Face-Cap model, which embed facial expression features in
different ways, to generate image captions. Using all standard evaluation
metrics, our Face-Cap models outperform a state-of-the-art baseline model for
generating image captions when applied to an image caption dataset extracted
from the standard Flickr 30K dataset, consisting of around 11K images
containing faces. An analysis of the captions finds that, perhaps surprisingly,
the improvement in caption quality appears to come not from the addition of
adjectives linked to emotional aspects of the images, but from more variety in
the actions described in the captions.
Adversarial Learning for Fine-grained Image Search
Authors: Kevin Lin, Fan Yang, Qiaosong Wang, Robinson Piramuthu
Date: 2018-07-06 00:00:00
Tasks: Generative Adversarial Network, Image Retrieval

Fine-grained image search is still a challenging problem due to the
difficulty in capturing subtle differences regardless of pose variations of
objects from fine-grained categories. In practice, a dynamic inventory with new
fine-grained categories adds another dimension to this challenge. In this work,
we propose an end-to-end network, called FGGAN, that learns discriminative
representations by implicitly learning a geometric transformation from
multi-view images for fine-grained image search. We integrate a generative
adversarial network (GAN) that can automatically handle complex view and pose
variations by converting them to a canonical view without any predefined
transformations. Moreover, in an open-set scenario, our network is able to
better match images from unseen and unknown fine-grained categories. Extensive
experiments on two public datasets and a newly collected dataset have
demonstrated the outstanding robust performance of the proposed FGGAN in both
closed-set and open-set scenarios, providing as much as 10% relative
improvement compared to baselines.
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Authors: Pengyuan Lyu, Minghui Liao, Cong Yao, Wenhao Wu, Xiang Bai
Date: 2018-07-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Scene Text Detection, Semantic Segmentation, Text Detection, Text Spotting

Recently, models based on deep neural networks have dominated the fields of
scene text detection and recognition. In this paper, we investigate the problem
of scene text spotting, which aims at simultaneous text detection and
recognition in natural images. An end-to-end trainable neural network model for
scene text spotting is proposed. The proposed model, named as Mask TextSpotter,
is inspired by the newly published work Mask R-CNN. Different from previous
methods that also accomplish text spotting with end-to-end trainable deep
neural networks, Mask TextSpotter takes advantage of simple and smooth
end-to-end learning procedure, in which precise text detection and recognition
are acquired via semantic segmentation. Moreover, it is superior to previous
methods in handling text instances of irregular shapes, for example, curved
text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the
proposed method achieves state-of-the-art results in both scene text detection
and end-to-end text recognition tasks.
Towards more Reliable Transfer Learning
Authors: Zirui Wang, Jaime Carbonell
Date: 2018-07-06 00:00:00
Tasks: Active Learning, Transfer Learning

Multi-source transfer learning has been proven effective when within-target
labeled data is scarce. Previous work focuses primarily on exploiting domain
similarities and assumes that source domains are richly or at least comparably
labeled. While this strong assumption is never true in practice, this paper
relaxes it and addresses challenges related to sources with diverse labeling
volume and diverse reliability. The first challenge is combining domain
similarity and source reliability by proposing a new transfer learning method
that utilizes both source-target similarities and inter-source relationships.
The second challenge involves pool-based active learning where the oracle is
only available in source domains, resulting in an integrated active transfer
learning framework that incorporates distribution matching and uncertainty
sampling. Extensive experiments on synthetic and two real-world datasets
clearly demonstrate the superiority of our proposed methods over several
baselines including state-of-the-art transfer learning methods.
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers
Authors: Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu
Date: 2018-07-06 00:00:00

Self-paced learning (SPL) mimics the cognitive process of humans, who
generally learn from easy samples to hard ones. One key issue in SPL is the
training process required for each instance weight depends on the other samples
and thus cannot easily be run in a distributed manner in a large-scale dataset.
In this paper, we reformulate the self-paced learning problem into a
distributed setting and propose a novel Distributed Self-Paced Learning method
(DSPL) to handle large-scale datasets. Specifically, both the model and
instance weights can be optimized in parallel for each batch based on a
consensus alternating direction method of multipliers. We also prove the
convergence of our algorithm under mild conditions. Extensive experiments on
both synthetic and real datasets demonstrate that our approach is superior to
those of existing methods.
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling
Authors: Yan Zhang, Xiang Huang, Nicola Ferrier, Emine B. Gulsoy, Charudatta Phatak
Date: 2018-07-06 00:00:00

Novel data acquisition schemes have been an emerging need for scanning
microscopy based imaging techniques to reduce the time in data acquisition and
to minimize probing radiation in sample exposure. Varies sparse sampling
schemes have been studied and are ideally suited for such applications where
the images can be reconstructed from a sparse set of measurements. Dynamic
sparse sampling methods, particularly supervised learning based iterative
sampling algorithms, have shown promising results for sampling pixel locations
on the edges or boundaries during imaging. However, dynamic sampling for
imaging skeleton-like objects such as metal dendrites remains difficult. Here,
we address a new unsupervised learning approach using Hierarchical Gaussian
Mixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique
is very useful if the users are interested in fast imaging the primary and
secondary arms of metal dendrites in solidification process in materials
science.
Progressive Spatial Recurrent Neural Network for Intra Prediction
Authors: Yueyu Hu, Wenhan Yang, Mading Li, Jiaying Liu
Date: 2018-07-06 00:00:00
Tasks: Prediction

Intra prediction is an important component of modern video codecs, which is able to efficiently squeeze out the spatial redundancy in video frames. With preceding pixels as the context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i.e. modes) for blocks to be encoded. However, these modes are relatively simple and their predictions may fail when facing blocks with complex textures, which leads to additional bits encoding the residue. In this paper, we design a Progressive Spatial Recurrent Neural Network (PS-RNN) that learns to conduct intra prediction. Specifically, our PS-RNN consists of three spatial recurrent units and progressively generates predictions by passing information along from preceding contents to blocks to be encoded. To make our network generate predictions considering both distortion and bit-rate, we propose to use Sum of Absolute Transformed Difference (SATD) as the loss function to train PS-RNN since SATD is able to measure rate-distortion cost of encoding a residue block. Moreover, our method supports variable-block-size for intra prediction, which is more practical in real coding conditions. The proposed intra prediction scheme achieves on average 2.5% bit-rate reduction on variable-block-size settings under the same reconstruction quality compared with HEVC.
A Concept Specification and Abstraction-based Semantic Representation: Addressing the Barriers to Rule-based Machine Translation
Authors: Patrick Connor
Date: 2018-07-06 00:00:00
Tasks: Machine Translation, Text Generation, Translation

Rule-based machine translation is more data efficient than the big data-based
machine translation approaches, making it appropriate for languages with low
bilingual corpus resources -- i.e., minority languages. However, the rule-based
approach has declined in popularity relative to its big data cousins primarily
because of the extensive training and labour required to define the language
rules. To address this, we present a semantic representation that 1) treats all
bits of meaning as individual concepts that 2) modify or further specify one
another to build a network that relates entities in space and time. Also, the
representation can 3) encapsulate propositions and thereby define concepts in
terms of other concepts, supporting the abstraction of underlying linguistic
and ontological details. These features afford an exact, yet intuitive semantic
representation aimed at handling the great variety in language and reducing
labour and training time. The proposed natural language generation, parsing,
and translation strategies are also amenable to probabilistic modeling and thus
to learning the necessary rules from example data.
The Data Science of Hollywood: Using Emotional Arcs of Movies to Drive Business Model Innovation in Entertainment Industries
Authors: Marco Del Vecchio, Alexander Kharlamov, Glenn Parry, Ganna Pogrebna
Date: 2018-07-06 00:00:00

Much of business literature addresses the issues of consumer-centric design:
how can businesses design customized services and products which accurately
reflect consumer preferences? This paper uses data science natural language
processing methodology to explore whether and to what extent emotions shape
consumer preferences for media and entertainment content. Using a unique
filtered dataset of 6,174 movie scripts, we generate a mapping of screen
content to capture the emotional trajectory of each motion picture. We then
combine the obtained mappings into clusters which represent groupings of
consumer emotional journeys. These clusters are used to predict overall success
parameters of the movies including box office revenues, viewer satisfaction
levels (captured by IMDb ratings), awards, as well as the number of viewers'
and critics' reviews. We find that like books all movie stories are dominated
by 6 basic shapes. The highest box offices are associated with the Man in a
Hole shape which is characterized by an emotional fall followed by an emotional
rise. This shape results in financially successful movies irrespective of genre
and production budget. Yet, Man in a Hole succeeds not because it produces most
"liked" movies but because it generates most "talked about" movies.
Interestingly, a carefully chosen combination of production budget and genre
may produce a financially successful movie with any emotional shape.
Implications of this analysis for generating on-demand content and for driving
business model innovation in entertainment industries are discussed.
The price of debiasing automatic metrics in natural language evaluation
Authors: Arun Tejasvi Chaganty, Stephen Mussman, Percy Liang
Date: 2018-07-06 00:00:00
Tasks: Question Answering

For evaluating generation systems, automatic metrics such as BLEU cost
nothing to run but have been shown to correlate poorly with human judgment,
leading to systematic bias against certain model improvements. On the other
hand, averaging human judgments, the unbiased gold standard, is often too
expensive. In this paper, we use control variates to combine automatic metrics
with human evaluation to obtain an unbiased estimator with lower cost than
human evaluation alone. In practice, however, we obtain only a 7-13% cost
reduction on evaluating summarization and open-response question answering
systems. We then prove that our estimator is optimal: there is no unbiased
estimator with lower cost. Our theory further highlights the two fundamental
bottlenecks---the automatic metric and the prompt shown to human
evaluators---both of which need to be improved to obtain greater cost savings.
Natural Language Processing for Music Knowledge Discovery
Authors: Sergio Oramas, Luis Espinosa-Anke, Francisco Gómez, Xavier Serra
Date: 2018-07-06 00:00:00
Tasks: Graph Generation, Sentiment Analysis

Today, a massive amount of musical knowledge is stored in written form, with
testimonies dated as far back as several centuries ago. In this work, we
present different Natural Language Processing (NLP) approaches to harness the
potential of these text collections for automatic music knowledge discovery,
covering different phases in a prototypical NLP pipeline, namely corpus
compilation, text-mining, information extraction, knowledge graph generation
and sentiment analysis. Each of these approaches is presented alongside
different use cases (i.e., flamenco, Renaissance and popular music) where large
collections of documents are processed, and conclusions stemming from
data-driven analyses are presented and discussed.
A Survey of Knowledge Representation in Service Robotics
Authors: David Paulius, Yu Sun
Date: 2018-07-05 00:00:00
Tasks: Activity Recognition, BIG-bench Machine Learning, Motion Planning, object-detection, Object Detection, Retrieval, Scene Understanding, Survey, Temporal Action Localization

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.