pwc-archive/papers-with-abstracts huggingface dataset:
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
Authors: Aditya Joshi, Samarth Agrawal, Pushpak Bhattacharyya, Mark Carman
Date: 2017-07-19 00:00:00
Tasks: Sarcasm Detection, Sentence, Sentence Completion

The trigram `I love being' is expected to be followed by positive words such
as `happy'. In a sarcastic sentence, however, the word `ignored' may be
observed. The expected and the observed words are, thus, incongruous. We model
sarcasm detection as the task of detecting incongruity between an observed and
an expected word. In order to obtain the expected word, we use Context2Vec, a
sentence completion library based on Bidirectional LSTM. However, since the
exact word where such an incongruity occurs may not be known in advance, we
present two approaches: an All-words approach (which consults sentence
completion for every content word) and an Incongruous words-only approach
(which consults sentence completion for the 50% most incongruous content
words). The approaches outperform reported values for tweets but not for
discussion forum posts. This is likely to be because of redundant consultation
of sentence completion for discussion forum posts. Therefore, we consider an
oracle case where the exact incongruous word is manually labeled in a corpus
reported in past work. In this case, the performance is higher than the
all-words approach. This sets up the promise for using sentence completion for
sarcasm detection.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Authors: Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li
Date: 2017-07-19 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Lesion Detection, Medical Image Analysis

Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.
Dynamic Steerable Blocks in Deep Residual Networks
Authors: Jörn-Henrik Jacobsen, Bert de Brabandere, Arnold W. M. Smeulders
Date: 2017-06-02 00:00:00
Tasks: Contour Detection

Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and Densenets can improve performance on Cifar-10+ consistently, while
having additional pleasant properties like steerability. By exploiting these
transformation properties explicitly, we arrive at dynamic steerable blocks.
They are an extension of residual blocks, that are able to seamlessly transform
filters under pre-defined transformations, conditioned on the input at training
and inference time. Dynamic steerable blocks learn the degree of invariance
from data and locally adapt filters, allowing them to apply a different
geometrical variant of the same filter to each location of the feature map.
When evaluated on the Berkeley Segmentation contour detection dataset, our
approach outperforms all competing approaches that do not utilize pre-training.
Our results highlight the benefits of image-based regularization to deep
networks.
Naive Bayes Classification for Subset Selection
Authors: Luca Mossina, Emmanuel Rachelson
Date: 2017-07-19 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This article focuses on the question of learning how to automatically select
a subset of items among a bigger set. We introduce a methodology for the
inference of ensembles of discrete values, based on the Naive Bayes assumption.
Our motivation stems from practical use cases where one wishes to predict an
unordered set of (possibly interdependent) values from a set of observed
features. This problem can be considered in the context of Multi-label
Classification (MLC) where such values are seen as labels associated to
continuous or discrete features. We introduce the \nbx algorithm, an extension
of Naive Bayes classification into the multi-label domain, discuss its
properties and evaluate our approach on real-world problems.
Algorithms for Approximate Subtropical Matrix Factorization
Authors: Sanjar Karaev, Pauli Miettinen
Date: 2017-07-19 00:00:00
Tasks: Dimensionality Reduction

Matrix factorization methods are important tools in data mining and analysis.
They can be used for many tasks, ranging from dimensionality reduction to
visualization. In this paper we concentrate on the use of matrix factorizations
for finding patterns from the data. Rather than using the standard algebra --
and the summation of the rank-1 components to build the approximation of the
original matrix -- we use the subtropical algebra, which is an algebra over the
nonnegative real values with the summation replaced by the maximum operator.
Subtropical matrix factorizations allow "winner-takes-it-all" interpretations
of the rank-1 components, revealing different structure than the normal
(nonnegative) factorizations. We study the complexity and sparsity of the
factorizations, and present a framework for finding low-rank subtropical
factorizations. We present two specific algorithms, called Capricorn and
Cancer, that are part of our framework. They can be used with data that has
been corrupted with different types of noise, and with different error metrics,
including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon
divergence. Our experiments show that the algorithms perform well on data that
has subtropical structure, and that they can find factorizations that are both
sparse and easy to interpret.
Solving Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with FSS Algorithm
Authors: Joao Batista Monteiro Filho, Isabela Maria Carneiro de Albuquerque, Fernando Buarque de Lima Neto
Date: 2017-07-19 00:00:00
Tasks: Heuristic Search

Balancing assembly lines, a family of optimization problems commonly known as
Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of
problems of enormous practical interest to manufacturing industry due to the
relevant frequency of this type of production paradigm. For this reason, many
researchers on Computational Intelligence and Industrial Engineering have been
conceiving algorithms for tackling different versions of assembly line
balancing problems utilizing different methodologies. In this article, it was
proposed a problem version referred as Mixed Model Workplace Time-dependent
Assembly Line Balancing Problem with the intention of including pressing issues
of real assembly lines in the optimization problem, to which four versions were
conceived. Heuristic search procedures were used, namely two Swarm Intelligence
algorithms from the Fish School Search family: the original version, named
"vanilla", and a special variation including a stagnation avoidance routine.
Either approaches solved the newly posed problem achieving good results when
compared to Particle Swarm Optimization algorithm.
Improving Language Modeling using Densely Connected Recurrent Neural Networks
Authors: Fréderic Godin, Joni Dambre, Wesley De Neve
Date: 2017-07-19 00:00:00
Proceeding: WS 2017 8
Tasks: Language Modeling, Language Modelling

In this paper, we introduce the novel concept of densely connected layers
into recurrent neural networks. We evaluate our proposed architecture on the
Penn Treebank language modeling task. We show that we can obtain similar
perplexity scores with six times fewer parameters compared to a standard
stacked 2-layer LSTM model trained with dropout (Zaremba et al. 2014). In
contrast with the current usage of skip connections, we show that densely
connecting only a few stacked layers with skip connections already yields
significant perplexity reductions.
Discriminative convolutional Fisher vector network for action recognition
Authors: Petar Palasek, Ioannis Patras
Date: 2017-07-19 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Dimensionality Reduction, Temporal Action Localization

In this work we propose a novel neural network architecture for the problem
of human action recognition in videos. The proposed architecture expresses the
processing steps of classical Fisher vector approaches, that is dimensionality
reduction by principal component analysis (PCA) projection, Gaussian mixture
model (GMM) and Fisher vector descriptor extraction, as network layers. By
contrast to other methods where these steps are performed consecutively and the
corresponding parameters are learned in an unsupervised manner, having them
defined as a single neural network allows us to refine the whole model
discriminatively in an end to end fashion. Furthermore, we show that the
proposed architecture can be used as a replacement for the fully connected
layers in popular convolutional networks achieving a comparable classification
performance, or even significantly surpassing the performance of similar
architectures while reducing the total number of trainable parameters by a
factor of 5. We show that our method achieves significant improvements in
comparison to the classical chain.
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Authors: M. Saquib Sarfraz, Arne Schumann, Yan Wang, Rainer Stiefelhagen
Date: 2017-07-19 00:00:00
Tasks: Attribute, image-classification, Image Classification, Multi-Label Image Classification, Person Retrieval, Retrieval

Pedestrian attribute inference is a demanding problem in visual surveillance
that can facilitate person retrieval, search and indexing. To exploit semantic
relations between attributes, recent research treats it as a multi-label image
classification task. The visual cues hinting at attributes can be strongly
localized and inference of person attributes such as hair, backpack, shorts,
etc., are highly dependent on the acquired view of the pedestrian. In this
paper we assert this dependence in an end-to-end learning framework and show
that a view-sensitive attribute inference is able to learn better attribute
predictions. Our proposed model jointly predicts the coarse pose (view) of the
pedestrian and learns specialized view-specific multi-label attribute
predictions. We show in an extensive evaluation on three challenging datasets
(PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute
prediction model provides competitive performance and improves on the published
state-of-the-art on these datasets.
Stock Prediction: a method based on extraction of news features and recurrent neural networks
Authors: Zeya Zhang, Weizheng Chen, Hongfei Yan
Date: 2017-07-19 00:00:00
Tasks: Stock Prediction

This paper proposed a method for stock prediction. In terms of feature
extraction, we extract the features of stock-related news besides stock prices.
We first select some seed words based on experience which are the symbols of
good news and bad news. Then we propose an optimization method and calculate
the positive polar of all words. After that, we construct the features of news
based on the positive polar of their words. In consideration of sequential
stock prices and continuous news effects, we propose a recurrent neural network
model to help predict stock prices. Compared to SVM classifier with price
features, we find our proposed method has an over 5% improvement on stock
prediction accuracy in experiments.
MML is not consistent for Neyman-Scott
Authors: Michael Brand
Date: 2016-10-14 00:00:00

Strict Minimum Message Length (SMML) is an information-theoretic statistical inference method widely cited (but only with informal arguments) as providing estimations that are consistent for general estimation problems. It is, however, almost invariably intractable to compute, for which reason only approximations of it (known as MML algorithms) are ever used in practice. Using novel techniques that allow for the first time direct, non-approximated analysis of SMML solutions, we investigate the Neyman-Scott estimation problem, an oft-cited showcase for the consistency of MML, and show that even with a natural choice of prior neither SMML nor its popular approximations are consistent for it, thereby providing a counterexample to the general claim. This is the first known explicit construction of an SMML solution for a natural, high-dimensional problem.
On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum
Authors: Anton V. Eremeev, Alexander V. Kelmanov, Artem V. Pyatkin, Igor A. Ziegler
Date: 2017-07-19 00:00:00

In this paper, we consider the problem of finding a maximum cardinality
subset of vectors, given a constraint on the normalized squared length of
vectors sum. This problem is closely related to Problem 1 from (Eremeev,
Kel'manov, Pyatkin, 2016). The main difference consists in swapping the
constraint with the optimization criterion.
  We prove that the problem is NP-hard even in terms of finding a feasible
solution. An exact algorithm for solving this problem is proposed. The
algorithm has a pseudo-polynomial time complexity in the special case of the
problem, where the dimension of the space is bounded from above by a constant
and the input data are integer. A computational experiment is carried out,
where the proposed algorithm is compared to COINBONMIN solver, applied to a
mixed integer quadratic programming formulation of the problem. The results of
the experiment indicate superiority of the proposed algorithm when the
dimension of Euclidean space is low, while the COINBONMIN has an advantage for
larger dimensions.
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition
Authors: Taesup Kim, Inchul Song, Yoshua Bengio
Date: 2017-07-19 00:00:00
Tasks: speech-recognition, Speech Recognition

Layer normalization is a recently introduced technique for normalizing the
activities of neurons in deep neural networks to improve the training speed and
stability. In this paper, we introduce a new layer normalization technique
called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling
in speech recognition. By dynamically generating the scaling and shifting
parameters in layer normalization, DLN adapts neural acoustic models to the
acoustic variability arising from various factors such as speakers, channel
noises, and environments. Unlike other adaptive acoustic models, our proposed
approach does not require additional adaptation data or speaker information
such as i-vectors. Moreover, the model size is fixed as it dynamically
generates adaptation parameters. We apply our proposed DLN to deep
bidirectional LSTM acoustic models and evaluate them on two benchmark datasets
for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The
experimental results show that our DLN improves neural acoustic models in terms
of transcription accuracy by dynamically adapting to various speakers and
environments.
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
Authors: Youngjae Yu, Jongwook Choi, Yeonhwa Kim, Kyung Yoo, Sang-Hun Lee, Gunhee Kim
Date: 2017-07-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Descriptive, Gaze Prediction, Sentence, Video Captioning

The attention mechanisms in deep neural networks are inspired by human's
attention that sequentially focuses on the most relevant parts of the
information over time to generate prediction output. The attention parameters
in those models are implicitly trained in an end-to-end manner, yet there have
been few trials to explicitly incorporate human gaze tracking to supervise the
attention models. In this paper, we investigate whether attention models can
benefit from explicit human gaze labels, especially for the task of video
captioning. We collect a new dataset called VAS, consisting of movie clips, and
corresponding multiple descriptive sentences along with human gaze tracking
data. We propose a video captioning model named Gaze Encoding Attention Network
(GEAN) that can leverage gaze tracking information to provide the spatial and
temporal attention for sentence generation. Through evaluation of language
similarity metrics and human assessment via Amazon mechanical Turk, we
demonstrate that spatial attentions guided by human gaze data indeed improve
the performance of multiple captioning methods. Moreover, we show that the
proposed approach achieves the state-of-the-art performance for both gaze
prediction and video captioning not only in our VAS dataset but also in
standard datasets (e.g. LSMDC and Hollywood2).
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
Authors: Afshine Amidi, Shervine Amidi, Dimitrios Vlachakis, Vasileios Megalooikonomou, Nikos Paragios, Evangelia I. Zacharaki
Date: 2017-07-19 00:00:00
Tasks: General Classification

During the past decade, with the significant progress of computational power
as well as ever-rising data availability, deep learning techniques became
increasingly popular due to their excellent performance on computer vision
problems. The size of the Protein Data Bank has increased more than 15 fold
since 1999, which enabled the expansion of models that aim at predicting
enzymatic function via their amino acid composition. Amino acid sequence
however is less conserved in nature than protein structure and therefore
considered a less reliable predictor of protein function. This paper presents
EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the
Enzyme Commission number of enzymes based only on their voxel-based spatial
structure. The spatial distribution of biochemical properties was also examined
as complementary information. The 2-layer architecture was investigated on a
large dataset of 63,558 enzymes from the Protein Data Bank and achieved an
accuracy of 78.4% by exploiting only the binary representation of the protein
shape. Code and datasets are available at https://github.com/shervinea/enzynet.
Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
Authors: Chia-Hao Shen, Janet Y. Sung, Hung-Yi Lee
Date: 2017-07-19 00:00:00

Audio Word2Vec offers vector representations of fixed dimensionality for
variable-length audio segments using Sequence-to-sequence Autoencoder (SA).
These vector representations are shown to describe the sequential phonetic
structures of the audio segments to a good degree, with real world applications
such as query-by-example Spoken Term Detection (STD). This paper examines the
capability of language transfer of Audio Word2Vec. We train SA from one
language (source language) and use it to extract the vector representation of
the audio segments of another language (target language). We found that SA can
still catch phonetic structure from the audio segments of the target language
if the source and target languages are similar. In query-by-example STD, we
obtain the vector representations from the SA learned from a large amount of
source language data, and found them surpass the representations from naive
encoder and SA directly learned from a small amount of target language data.
The result shows that it is possible to learn Audio Word2Vec model from
high-resource languages and use it on low-resource languages. This further
expands the usability of Audio Word2Vec.
Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle
Authors: George Terzakis, Riccardo Polvara, Sanjay Sharma, Phil Culverhouse, Robert Sutton
Date: 2017-07-14 00:00:00
Tasks: Monocular Visual Odometry, Simultaneous Localization and Mapping, Visual Localization, Visual Odometry

We tackle the problem of localizing an autonomous sea-surface vehicle in
river estuarine areas using monocular camera and angular velocity input from an
inertial sensor. Our method is challenged by two prominent drawbacks associated
with the environment, which are typically not present in standard visual
simultaneous localization and mapping (SLAM) applications on land (or air): a)
Scene depth varies significantly (from a few meters to several kilometers) and,
b) In conjunction to the latter, there exists no ground plane to provide
features with enough disparity based on which to reliably detect motion. To
that end, we use the IMU orientation feedback in order to re-cast the problem
of visual localization without the mapping component, although the map can be
implicitly obtained from the camera pose estimates. We find that our method
produces reliable odometry estimates for trajectories several hundred meters
long in the water. To compare the visual odometry estimates with GPS based
ground truth, we interpolate the trajectory with splines on a common parameter
and obtain position error in meters recovering an optimal affine transformation
between the two splines.
Procedural Generation of Videos to Train Deep Action Recognition Networks
Authors: César Roberto de Souza, Adrien Gaidon, Yohann Cabon, Antonio Manuel López Peña
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Action Recognition In Videos, Representation Learning, Temporal Action Localization

Deep learning for human action recognition in videos is making significant
progress, but is slowed down by its dependency on expensive manual labeling of
large video collections. In this work, we investigate the generation of
synthetic training data for action recognition, as it has recently shown
promising results for a variety of other computer vision tasks. We propose an
interpretable parametric generative model of human action videos that relies on
procedural generation and other computer graphics techniques of modern game
engines. We generate a diverse, realistic, and physically plausible dataset of
human action videos, called PHAV for "Procedural Human Action Videos". It
contains a total of 39,982 videos, with more than 1,000 examples for each
action of 35 categories. Our approach is not limited to existing motion capture
sequences, and we procedurally define 14 synthetic actions. We introduce a deep
multi-task representation learning architecture to mix synthetic and real
videos, even if the action categories differ. Our experiments on the UCF101 and
HMDB51 benchmarks suggest that combining our large set of synthetic videos with
small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of
videos.