pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Authors: Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Date: 2016-09-22 00:00:00
Tasks: Earth Observation, Scene Labeling, Semantic Segmentation

This work investigates the use of deep fully convolutional neural networks
(DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially,
we train a variant of the SegNet architecture on remote sensing data over an
urban area and study different strategies for performing accurate semantic
segmentation. Our contributions are the following: 1) we transfer efficiently a
DFCNN from generic everyday images to remote sensing images; 2) we introduce a
multi-kernel convolutional layer for fast aggregation of predictions at
multiple scales; 3) we perform data fusion from heterogeneous sensors (optical
and laser) using residual correction. Our framework improves state-of-the-art
accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
On the usability of deep networks for object-based image analysis
Authors: Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Date: 2016-09-22 00:00:00
Tasks: Earth Observation, General Classification, Object, object-detection, Object Detection, Scene Understanding

As computer vision before, remote sensing has been radically changed by the
introduction of Convolution Neural Networks. Land cover use, object detection
and scene understanding in aerial images rely more and more on deep learning to
achieve new state-of-the-art results. Recent architectures such as Fully
Convolutional Networks (Long et al., 2015) can even produce pixel level
annotations for semantic mapping. In this work, we show how to use such deep
networks to detect, segment and classify different varieties of wheeled
vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to
tackle object detection and classification on a complex dataset made up of
visually similar classes, and to demonstrate the relevance of such a subclass
modeling approach. Especially, we want to show that deep learning is also
suitable for object-oriented analysis of Earth Observation data. First, we
train a FCN variant on the ISPRS Potsdam dataset and show how the learnt
semantic maps can be used to extract precise segmentation of vehicles, which
allow us studying the repartition of vehicles in the city. Second, we train a
CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie,
2016) dataset, and transfer its knowledge to classify candidate segmented
vehicles on the Potsdam dataset.
Hawkes Processes with Stochastic Excitations
Authors: Young Lee, Kar Wai Lim, Cheng Soon Ong
Date: 2016-09-22 00:00:00

We propose an extension to Hawkes processes by treating the levels of
self-excitation as a stochastic differential equation. Our new point process
allows better approximation in application domains where events and intensities
accelerate each other with correlated levels of contagion. We generalize a
recent algorithm for simulating draws from Hawkes processes whose levels of
excitation are stochastic processes, and propose a hybrid Markov chain Monte
Carlo approach for model fitting. Our sampling procedure scales linearly with
the number of required events and does not require stationarity of the point
process. A modular inference procedure consisting of a combination between
Gibbs and Metropolis Hastings steps is put forward. We recover expectation
maximization as a special case. Our general approach is illustrated for
contagion following geometric Brownian motion and exponential Langevin
dynamics.
Conversational Recommendation System with Unsupervised Learning
Authors: Yueming Sun, Yi Zhang, Yunfei Chen, Roger Jin
Date: 2016-09-22 00:00:00
Tasks: Conversational Recommendation, Recommendation Systems

We will demonstrate a conversational products recommendation agent. This
system shows how we combine research in personalized recommendation systems
with research in dialogue systems to build a virtual sales agent. Based on new
deep learning technologies we developed, the virtual agent is capable of
learning how to interact with users, how to answer user questions, what is the
next question to ask, and what to recommend when chatting with a human user.
  Normally a descent conversational agent for a particular domain requires tens
of thousands of hand labeled conversational data or hand written rules. This is
a major barrier when launching a conversation agent for a new domain. We will
explore and demonstrate the effectiveness of the learning solution even when
there is no hand written rules or hand labeled training data.
Bibliographic Analysis with the Citation Network Topic Model
Authors: Kar Wai Lim, Wray Buntine
Date: 2016-09-22 00:00:00
Tasks: Clustering, model

Bibliographic analysis considers author's research areas, the citation
network and paper content among other things. In this paper, we combine these
three in a topic model that produces a bibliographic model of authors, topics
and documents using a non-parametric extension of a combination of the Poisson
mixed-topic link model and the author-topic model. We propose a novel and
efficient inference algorithm for the model to explore subsets of research
publications from CiteSeerX. Our model demonstrates improved performance in
both model fitting and a clustering task compared to several baselines.
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
Authors: Yueming Sun, Ye Yang, He Zhang, Wen Zhang, Qing Wang
Date: 2016-09-22 00:00:00
Tasks: Systematic Literature Review

[Background]: Systematic Literature Review (SLR) has become an important
software engineering research method but costs tremendous efforts. [Aim]: This
paper proposes an approach to leverage on empirically evolved ontology to
support automating key SLR activities. [Method]: First, we propose an ontology,
SLRONT, built on SLR experiences and best practices as a groundwork to capture
common terminologies and their relationships during SLR processes; second, we
present an extended version of SLRONT, the COSONT and instantiate it with the
knowledge and concepts extracted from structured abstracts. Case studies
illustrate the details of applying it for supporting SLR steps. [Results]:
Results show that through using COSONT, we acquire the same conclusion compared
with sheer manual works, but the efforts involved is significantly reduced.
[Conclusions]: The approach of using ontology could effectively and efficiently
support the conducting of systematic literature review.
Response Selection with Topic Clues for Retrieval-based Chatbots
Authors: Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou
Date: 2016-04-30 00:00:00
Tasks: Retrieval

We consider incorporating topic information into message-response matching to
boost responses with rich content in retrieval-based chatbots. To this end, we
propose a topic-aware convolutional neural tensor network (TACNTN). In TACNTN,
matching between a message and a response is not only conducted between a
message vector and a response vector generated by convolutional neural
networks, but also leverages extra topic information encoded in two topic
vectors. The two topic vectors are linear combinations of topic words of the
message and the response respectively, where the topic words are obtained from
a pre-trained LDA model and their weights are determined by themselves as well
as the message vector and the response vector. The message vector, the response
vector, and the two topic vectors are fed to neural tensors to calculate a
matching score. Empirical study on a public data set and a human annotated data
set shows that TACNTN can significantly outperform state-of-the-art methods for
message-response matching.
Sequence-Level Knowledge Distillation
Authors: Yoon Kim, Alexander M. Rush
Date: 2016-06-25 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Knowledge Distillation, Machine Translation, NMT, Translation

Neural machine translation (NMT) offers a novel alternative formulation of
translation that is potentially simpler than statistical approaches. However to
reach competitive performance, NMT models need to be exceedingly large. In this
paper we consider applying knowledge distillation approaches (Bucila et al.,
2006; Hinton et al., 2015) that have proven successful for reducing the size of
neural models in other domains to the problem of NMT. We demonstrate that
standard knowledge distillation applied to word-level prediction can be
effective for NMT, and also introduce two novel sequence-level versions of
knowledge distillation that further improve performance, and somewhat
surprisingly, seem to eliminate the need for beam search (even when applied on
the original teacher model). Our best student model runs 10 times faster than
its state-of-the-art teacher with little loss in performance. It is also
significantly better than a baseline model trained without knowledge
distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight
pruning on top of knowledge distillation results in a student model that has 13
times fewer parameters than the original teacher model, with a decrease of 0.4
BLEU.
Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling
Authors: Kar Wai Lim, Changyou Chen, Wray Buntine
Date: 2016-09-22 00:00:00
Tasks: Topic Models

Twitter data is extremely noisy -- each tweet is short, unstructured and with
informal language, a challenge for current topic modeling. On the other hand,
tweets are accompanied by extra information such as authorship, hashtags and
the user-follower network. Exploiting this additional information, we propose
the Twitter-Network (TN) topic model to jointly model the text and the social
network in a full Bayesian nonparametric way. The TN topic model employs the
hierarchical Poisson-Dirichlet processes (PDP) for text modeling and a Gaussian
process random function model for social network modeling. We show that the TN
topic model significantly outperforms several existing nonparametric models due
to its flexibility. Moreover, the TN topic model enables additional informative
inference such as authors' interests, hashtag analysis, as well as leading to
further applications such as author recommendation, automatic topic labeling
and hashtag suggestion. Note our general inference framework can readily be
applied to other topic models with embedded PDP nodes.
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Authors: Hui Ding, Shaohua Kevin Zhou, Rama Chellappa
Date: 2016-09-21 00:00:00
Tasks: Face Recognition, Facial Expression Recognition (FER), Small Data Image Classification

Relatively small data sets available for expression recognition research make
the training of deep networks for expression recognition very challenging.
Although fine-tuning can partially alleviate the issue, the performance is
still below acceptable levels as the deep features probably contain redun- dant
information from the pre-trained domain. In this paper, we present
FaceNet2ExpNet, a novel idea to train an expression recognition network based
on static images. We first propose a new distribution function to model the
high-level neurons of the expression network. Based on this, a two-stage
training algorithm is carefully designed. In the pre-training stage, we train
the convolutional layers of the expression net, regularized by the face net; In
the refining stage, we append fully- connected layers to the pre-trained
convolutional layers and train the whole network jointly. Visualization shows
that the model trained with our method captures improved high-level expression
semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA,
TFD, and SFEW demonstrate that our method achieves better results than
state-of-the-art.
Clinical Tagging with Joint Probabilistic Models
Authors: Yoni Halpern, Steven Horng, David Sontag
Date: 2016-08-02 00:00:00
Tasks: parameter estimation

We describe a method for parameter estimation in bipartite probabilistic
graphical models for joint prediction of clinical conditions from the
electronic medical record. The method does not rely on the availability of
gold-standard labels, but rather uses noisy labels, called anchors, for
learning. We provide a likelihood-based objective and a moments-based
initialization that are effective at learning the model parameters. The learned
model is evaluated in a task of assigning a heldout clinical condition to
patients based on retrospective analysis of the records, and outperforms
baselines which do not account for the noisiness in the labels or do not model
the conditions jointly.
Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
Authors: Kar Wai Lim, Wray Buntine, Changyou Chen, Lan Du
Date: 2016-09-22 00:00:00
Tasks: Topic Models

The Dirichlet process and its extension, the Pitman-Yor process, are
stochastic processes that take probability distributions as a parameter. These
processes can be stacked up to form a hierarchical nonparametric Bayesian
model. In this article, we present efficient methods for the use of these
processes in this hierarchical context, and apply them to latent variable
models for text analytics. In particular, we propose a general framework for
designing these Bayesian models, which are called topic models in the computer
science community. We then propose a specific nonparametric Bayesian topic
model for modelling text from social media. We focus on tweets (posts on
Twitter) in this article due to their ease of access. We find that our
nonparametric model performs better than existing parametric models in both
goodness of fit and real world applications.
Identification of relevant subtypes via preweighted sparse clustering
Authors: Sheila Gaynor, Eric Bair
Date: 2013-04-13 00:00:00
Tasks: Clustering

Cluster analysis methods are used to identify homogeneous subgroups in a data
set. In biomedical applications, one frequently applies cluster analysis in
order to identify biologically interesting subgroups. In particular, one may
wish to identify subgroups that are associated with a particular outcome of
interest. Conventional clustering methods generally do not identify such
subgroups, particularly when there are a large number of high-variance features
in the data set. Conventional methods may identify clusters associated with
these high-variance features when one wishes to obtain secondary clusters that
are more interesting biologically or more strongly associated with a particular
outcome of interest. A modification of sparse clustering can be used to
identify such secondary clusters or clusters associated with an outcome of
interest. This method correctly identifies such clusters of interest in several
simulation scenarios. The method is also applied to a large prospective cohort
study of temporomandibular disorders and a leukemia microarray data set.
Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos
Authors: Yi Zhu, Shawn Newsam
Date: 2016-09-21 00:00:00

We perform spatio-temporal analysis of public sentiment using geotagged photo
collections. We develop a deep learning-based classifier that predicts the
emotion conveyed by an image. This allows us to associate sentiment with place.
We perform spatial hotspot detection and show that different emotions have
distinct spatial distributions that match expectations. We also perform
temporal analysis using the capture time of the photos. Our spatio-temporal
hotspot detection correctly identifies emerging concentrations of specific
emotions and year-by-year analyses of select locations show there are strong
temporal correlations between the predicted emotions and known events.
Anchoring and Agreement in Syntactic Annotations
Authors: Yevgeni Berzak, Yan Huang, Andrei Barbu, Anna Korhonen, Boris Katz
Date: 2016-05-15 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Decision Making, Dependency Parsing

We present a study on two key characteristics of human syntactic annotations:
anchoring and agreement. Anchoring is a well known cognitive bias in human
decision making, where judgments are drawn towards pre-existing values. We
study the influence of anchoring on a standard approach to creation of
syntactic resources where syntactic annotations are obtained via human editing
of tagger and parser output. Our experiments demonstrate a clear anchoring
effect and reveal unwanted consequences, including overestimation of parsing
performance and lower quality of annotations in comparison with human-based
annotations. Using sentences from the Penn Treebank WSJ, we also report
systematically obtained inter-annotator agreement estimates for English
dependency parsing. Our agreement results control for parser bias, and are
consequential in that they are on par with state of the art parsing performance
for English newswire. We discuss the impact of our findings on strategies for
future annotation efforts and parser evaluations.
PixelNet: Towards a General Pixel-level Architecture
Authors: Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan
Date: 2016-09-21 00:00:00
Tasks: Diversity, Edge Detection, Semantic Segmentation, Surface Normal Estimation

We explore architectures for general pixel-level prediction problems, from
low-level edge detection to mid-level surface normal estimation to high-level
semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy of neighboring pixels through convolutional
processing. Though computationally efficient, we point out that such approaches
are not statistically efficient during learning precisely because spatial
redundancy limits the information learned from neighboring pixels. We
demonstrate that (1) stratified sampling allows us to add diversity during
batch updates and (2) sampled multi-scale features allow us to explore more
nonlinear predictors (multiple fully-connected layers followed by ReLU) that
improve overall accuracy. Finally, our objective is to show how a architecture
can get performance better than (or comparable to) the architectures designed
for a particular task. Interestingly, our single architecture produces
state-of-the-art results for semantic segmentation on PASCAL-Context, surface
normal estimation on NYUDv2 dataset, and edge detection on BSDS without
contextual post-processing.
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Authors: Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Date: 2016-09-21 00:00:00
Tasks: Authorship Attribution, Sentence, Sentence Classification

Convolutional neural networks (CNNs) have demonstrated superior capability
for extracting information from raw signals in computer vision. Recently,
character-level and multi-channel CNNs have exhibited excellent performance for
sentence classification tasks. We apply CNNs to large-scale authorship
attribution, which aims to determine an unknown text's author among many
candidate authors, motivated by their ability to process character-level
signals and to differentiate between a large number of classes, while making
fast predictions in comparison to state-of-the-art approaches. We extensively
evaluate CNN-based approaches that leverage word and character channels and
compare them against state-of-the-art methods for a large range of author
numbers, shedding new light on traditional approaches. We show that
character-level CNNs outperform the state-of-the-art on four out of five
datasets in different domains. Additionally, we present the first application
of authorship attribution to reddit.
Fast and reliable stereopsis measurement at multiple distances with iPad
Authors: Manuel Rodriguez-Vallejo, Clara Llorens-Quintana, Diego Montagud, Walter D. Furlan, Juan A. Monsoriu
Date: 2016-09-21 00:00:00

Purpose: To present a new fast and reliable application for iPad (ST) for
screening stereopsis at multiple distances.
  Methods: A new iPad application (app) based on a random dot stereogram was
designed for screening stereopsis at multiple distances. Sixty-five subjects
with no ocular diseases and wearing their habitual correction were tested at
two different distances: 3 m and at 0.4 m. Results were compared with other
commercial tests: TNO (at near) and Howard Dolman (at distance) Subjects were
cited one week later in order to repeat the same procedures for assessing
reproducibility of the tests.
  Results: Stereopsis at near was better with ST (40 arcsec) than with TNO (60
arcsec), but not significantly (p = 0.36). The agreement was good (k = 0.604)
and the reproducibility was better with ST (k = 0.801) than with TNO (k =
0.715), in fact median difference between days was significant only with TNO (p
= 0.02). On the other hand, poor agreement was obtained between HD and ST at
far distance (k=0.04), obtaining significant differences in medians (p = 0.001)
and poorer reliability with HD (k = 0.374) than with ST (k = 0.502).
  Conclusions: Screening stereopsis at near with a new iPad app demonstrated to
be a fast and realiable. Results were in a good agreement with conventional
tests as TNO, but it could not be compared at far vision with HD due to the
limited resolution of the iPad.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Authors: Mario Buty, Ziyue Xu, Mingchen Gao, Ulas Bagci, Aaron Wu, Daniel J. Mollura
Date: 2016-09-21 00:00:00
Tasks: Lung Cancer Diagnosis

Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagnostics is challenging due to their subjective nature and
the difficulties in gaining a complete description. In this paper, we propose a
computerized approach to quantitatively evaluate both appearance distinctions
and 3D surface variations. Nodule shape was modeled and parameterized using
spherical harmonics, and appearance features were extracted using deep
convolutional neural networks. Both sets of features were combined to estimate
the nodule malignancy using a random forest classifier. The proposed algorithm
was tested on the publicly available Lung Image Database Consortium dataset,
achieving high accuracy. By providing lung nodule characterization, this method
can provide a robust alternative reference opinion for lung cancer diagnosis.
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Authors: Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada
Date: 2016-09-21 00:00:00
Tasks: Question Answering, Sentence, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) task has showcased a new stage of interaction
between language and vision, two of the most pivotal components of artificial
intelligence. However, it has mostly focused on generating short and repetitive
answers, mostly single words, which fall short of rich linguistic capabilities
of humans. We introduce Full-Sentence Visual Question Answering (FSVQA)
dataset, consisting of nearly 1 million pairs of questions and full-sentence
answers for images, built by applying a number of rule-based natural language
processing techniques to original VQA dataset and captions in the MS COCO
dataset. This poses many additional complexities to conventional VQA task, and
we provide a baseline for approaching and evaluating the task, on top of which
we invite the research community to build further improvements.