pwc-archive/papers-with-abstracts huggingface dataset:
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
Authors: Prakhar Ganesh, Puneet Rakheja
Date: 2018-09-05 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Vocal Bursts Intensity Prediction

Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in financial trading should be able to incorporate them together. One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data. LSTMs are one of the most capable variants of the RNN family that can handle long-term dependencies, but even they are not equipped to handle such long sequences of the order of thousands of data points like in HFT. We propose very-long short term memory networks, or VLSTMs, to deal with such extreme length sequences. We explore the importance of VLSTMs in the context of HFT. We compare our model on publicly available dataset and got a 3.14\% increase in F1-score over the existing state-of-the-art time-series forecasting models. We also show that our model has great parallelization potential, which is essential for practical purposes when trading on such markets.
Blind Community Detection from Low-rank Excitations of a Graph Filter
Authors: Hoi-To Wai, Santiago Segarra, Asuman E. Ozdaglar, Anna Scaglione, Ali Jadbabaie
Date: 2018-09-05 00:00:00
Tasks: Community Detection

This paper considers a new framework to detect communities in a graph from
the observation of signals at its nodes. We model the observed signals as noisy
outputs of an unknown network process, represented as a graph filter that is
excited by a set of unknown low-rank inputs/excitations. Application scenarios
of this model include diffusion dynamics, pricing experiments, and opinion
dynamics. Rather than learning the precise parameters of the graph itself, we
aim at retrieving the community structure directly. The paper shows that
communities can be detected by applying a spectral method to the covariance
matrix of graph signals. Our analysis indicates that the community detection
performance depends on a `low-pass' property of the graph filter. We also show
that the performance can be improved via a low-rank matrix plus sparse
decomposition method when the latent parameter vectors are known. Numerical
experiments demonstrate that our approach is effective.
Deep Bilevel Learning
Authors: Simon Jenni, Paolo Favaro
Date: 2018-09-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Bilevel Optimization

We present a novel regularization approach to train neural networks that
enjoys better generalization and test error than standard stochastic gradient
descent. Our approach is based on the principles of cross-validation, where a
validation set is used to limit the model overfitting. We formulate such
principles as a bilevel optimization problem. This formulation allows us to
define the optimization of a cost on the validation set subject to another
optimization on the training set. The overfitting is controlled by introducing
weights on each mini-batch in the training set and by choosing their values so
that they minimize the error on the validation set. In practice, these weights
define mini-batch learning rates in a gradient descent update equation that
favor gradients with better generalization capabilities. Because of its
simplicity, this approach can be integrated with other regularization methods
and training schemes. We evaluate extensively our proposed algorithm on several
neural network architectures and datasets, and find that it consistently
improves the generalization of the model, especially when labels are noisy.
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry
Authors: Chao Zhang, Xuequan Lu, Takuya Akashi
Date: 2018-09-05 00:00:00
Tasks: Keypoint Detection, valid

Well-known corner or local extrema feature based detectors such as FAST and
DoG have achieved noticeable successes. However, detecting keypoints in the
presence of blur has remained to be an unresolved issue. As a matter of fact,
various kinds of blur (e.g., motion blur, out-of-focus, and space-variant)
remarkably increase challenges for keypoint detection. As a result, those
methods have limited performance. To settle this issue, we propose a
blur-countering method for detecting valid keypoints for various types and
degrees of blurred images. Specifically, we first present a distance metric for
derivative distributions, which preserves the distinctiveness of patch pairs
well under blur. We then model the asymmetry by utilizing the difference of
squared eigenvalues based on the distance metric. To make it scale-robust, we
also extend it to scale space. The proposed detector is efficient as the main
computational cost is the square of derivatives at each pixel. Extensive visual
and quantitative results show that our method outperforms current approaches
under different types and degrees of blur. Without any parallelization, our
implementation\footnote{We will make our code publicly available upon the
acceptance.} achieves real-time performance for low-resolution images (e.g.,
$320\times240$ pixel).
IEST: WASSA-2018 Implicit Emotions Shared Task
Authors: Roman Klinger, Orphée De Clercq, Saif M. Mohammad, Alexandra Balahur
Date: 2018-09-04 00:00:00
Proceeding: WS 2018 10

Past shared tasks on emotions use data with both overt expressions of
emotions (I am so happy to see you!) as well as subtle expressions where the
emotions have to be inferred, for instance from event descriptions. Further,
most datasets do not focus on the cause or the stimulus of the emotion. Here,
for the first time, we propose a shared task where systems have to predict the
emotions in a large automatically labeled dataset of tweets without access to
words denoting emotions. Based on this intention, we call this the Implicit
Emotion Shared Task (IEST) because the systems have to infer the emotion mostly
from the context. Every tweet has an occurrence of an explicit emotion word
that is masked. The tweets are collected in a manner such that they are likely
to include a description of the cause of the emotion - the stimulus.
Altogether, 30 teams submitted results which range from macro F1 scores of 21 %
to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of
60 % which was available to the participants during the development phase. A
study with human annotators suggests that automatic methods outperform human
predictions, possibly by honing into subtle textual clues not used by humans.
Corpora, resources, and results are available at the shared task website at
http://implicitemotions.wassa2018.com.
Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection
Authors: Prabod Rathnayaka, Supun Abeysinghe, Chamod Samarajeewa, Isura Manchanayake, Malaka Walpola
Date: 2018-09-05 00:00:00
Proceeding: WS 2018 10
Tasks: Word Embeddings

In this paper, we present the system we have used for the Implicit WASSA 2018
Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of
which the explicit mentions of emotion terms have been removed. The idea is to
come up with a model which has the ability to implicitly identify the emotion
expressed given the context words. We have used a Gated Recurrent Neural
Network (GRU) and a Capsule Network based model for the task. Pre-trained word
embeddings have been utilized to incorporate contextual knowledge about words
into the model. GRU layer learns latent representations using the input word
embeddings. Subsequent Capsule Network layer learns high-level features from
that hidden representation. The proposed model managed to achieve a macro-F1
score of 0.692.
Appendix - Recommended Statistical Significance Tests for NLP Tasks
Authors: Rotem Dror, Roi Reichart
Date: 2018-09-05 00:00:00
Tasks: valid

Statistical significance testing plays an important role when drawing
conclusions from experimental results in NLP papers. Particularly, it is a
valuable tool when one would like to establish the superiority of one algorithm
over another. This appendix complements the guide for testing statistical
significance in NLP presented in \cite{dror2018hitchhiker} by proposing valid
statistical tests for the common tasks and evaluation measures in the field.
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Authors: Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Morphological Tagging, Segmentation, Sentence, Structured Prediction

The configurational information in sentences of a free word order language
such as Sanskrit is of limited use. Thus, the context of the entire sentence
will be desirable even for basic processing tasks such as word segmentation. We
propose a structured prediction framework that jointly solves the word
segmentation and morphological tagging tasks in Sanskrit. We build an energy
based model where we adopt approaches generally employed in graph based parsing
techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the
state of the art with an F-Score of 96.92 (percentage improvement of 7.06%)
while using less than one-tenth of the task-specific training data. We find
that the use of a graph based ap- proach instead of a traditional lattice-based
sequential labelling approach leads to a percentage gain of 12.6% in F-Score
for the segmentation task.
Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery
Authors: Clint Sebastian, Ries Uittenbogaard, Julien Vijverberg, Bas Boom, Peter H. N. de With
Date: 2018-09-05 00:00:00
Tasks: Asset Management, Autonomous Driving, Classification, General Classification, Management, object-detection, Object Detection

Object detection and classification of traffic signs in street-view imagery
is an essential element for asset management, map making and autonomous
driving. However, some traffic signs occur rarely and consequently, they are
difficult to recognize automatically. To improve the detection and
classification rates, we propose to generate images of traffic signs, which are
then used to train a detector/classifier. In this research, we present an
end-to-end framework that generates a realistic image of a traffic sign from a
given image of a traffic sign and a pictogram of the target class. We propose a
residual attention mechanism with dense concatenation called Dense Residual
Attention, that preserves the background information while transferring the
object information. We also propose to utilize multi-scale discriminators, so
that the smaller scales of the output guide the higher resolution output. We
have performed detection and classification tests across a large number of
traffic sign classes, by training the detector using the combination of real
and generated data. The newly trained model reduces the number of false
positives by 1.2 - 1.5% at 99% recall in the detection tests and an absolute
improvement of 4.65% (top-1 accuracy) in the classification tests.
Data Augmentation for Skin Lesion Analysis
Authors: Fábio Perez, Cristina Vasconcelos, Sandra Avila, Eduardo Valle
Date: 2018-09-05 00:00:00
Tasks: Data Augmentation, General Classification, Skin Cancer Classification, Skin Lesion Classification

Deep learning models show remarkable results in automated skin lesion
analysis. However, these models demand considerable amounts of data, while the
availability of annotated skin lesion images is often limited. Data
augmentation can expand the training dataset by transforming input images. In
this work, we investigate the impact of 13 data augmentation scenarios for
melanoma classification trained on three CNNs (Inception-v4, ResNet, and
DenseNet). Scenarios include traditional color and geometric transforms, and
more unusual augmentations such as elastic transforms, random erasing and a
novel augmentation that mixes different lesions. We also explore the use of
data augmentation at test-time and the impact of data augmentation on various
dataset sizes. Our results confirm the importance of data augmentation in both
training and testing and show that it can lead to more performance gains than
obtaining new images. The best scenario results in an AUC of 0.882 for melanoma
classification without using external data, outperforming the top-ranked
submission (0.874) for the ISIC Challenge 2017, which was trained with
additional data.
How is Contrast Encoded in Deep Neural Networks?
Authors: Arash Akbarinia, Karl R. Gegenfurtner
Date: 2018-09-05 00:00:00

Contrast is a crucial factor in visual information processing. It is desired
for a visual system - irrespective of being biological or artificial - to
"perceive" the world robustly under large potential changes in illumination. In
this work, we studied the responses of deep neural networks (DNN) to identical
images at different levels of contrast. We analysed the activation of kernels
in the convolutional layers of eight prominent networks with distinct
architectures (e.g. VGG and Inception). The results of our experiments indicate
that those networks with a higher tolerance to alteration of contrast have more
than one convolutional layer prior to the first max-pooling operator. It
appears that the last convolutional layer before the first max-pooling acts as
a mitigator of contrast variation in input images. In our investigation,
interestingly, we observed many similarities between the mechanisms of these
DNNs and biological visual systems. These comparisons allow us to understand
more profoundly the underlying mechanisms of a visual system that is grounded
on the basis of "data-analysis".
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Authors: Yan Ju, Lingling Li, Licheng Jiao, Zhongle Ren, Biao Hou, Shuyuan Yang
Date: 2018-09-05 00:00:00
Tasks: Classification, Clustering, Diversity, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Due to the limited amount and imbalanced classes of labeled training data,
the conventional supervised learning can not ensure the discrimination of the
learned feature for hyperspectral image (HSI) classification. In this paper, we
propose a modified diversity of class probability estimation (MDCPE) with two
deep neural networks to learn spectral-spatial feature for HSI classification.
In co-training phase, recurrent neural network (RNN) and convolutional neural
network (CNN) are utilized as two learners to extract features from labeled and
unlabeled data. Based on the extracted features, MDCPE selects most credible
samples to update initial labeled data by combining k-means clustering with the
traditional diversity of class probability estimation (DCPE) co-training. In
this way, MDCPE can keep new labeled data class-balanced and extract
discriminative features for both the minority and majority classes. During
testing process, classification results are acquired by co-decision of the two
learners. Experimental results demonstrate that the proposed semi-supervised
co-training method can make full use of unlabeled information to enhance
generality of the learners and achieve favorable accuracies on all three widely
used data sets: Salinas, Pavia University and Pavia Center.
Stellar Cluster Detection using GMM with Deep Variational Autoencoder
Authors: Arnab Karmakar, Deepak Mishra, Anandmayee Tej
Date: 2018-09-05 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning

Detecting stellar clusters have always been an important research problem in
Astronomy. Although images do not convey very detailed information in detecting
stellar density enhancements, we attempt to understand if new machine learning
techniques can reveal patterns that would assist in drawing better inferences
from the available image data. This paper describes an unsupervised approach in
detecting star clusters using Deep Variational Autoencoder combined with a
Gaussian Mixture Model. We show that our method works significantly well in
comparison with state-of-the-art detection algorithm in recognizing a variety
of star clusters even in the presence of noise and distortion.
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
Authors: Sameer Bansal, Herman Kamper, Karen Livescu, Adam Lopez, Sharon Goldwater
Date: 2018-09-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

We present a simple approach to improve direct speech-to-text translation
(ST) when the source language is low-resource: we pre-train the model on a
high-resource automatic speech recognition (ASR) task, and then fine-tune its
parameters for ST. We demonstrate that our approach is effective by
pre-training on 300 hours of English ASR data to improve Spanish-English ST
from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data
are available. Through an ablation study, we find that the pre-trained encoder
(acoustic model) accounts for most of the improvement, despite the fact that
the shared language in these tasks is the target language text, not the source
language audio. Applying this insight, we show that pre-training on ASR helps
ST even when the ASR language differs from both source and target ST languages:
pre-training on French ASR also improves Spanish-English ST. Finally, we show
that the approach improves performance on a true low-resource task:
pre-training on a combination of English ASR and French ASR improves
Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1
BLEU.
Generating Highly Realistic Images of Skin Lesions with GANs
Authors: Christoph Baur, Shadi Albarqouni, Nassir Navab
Date: 2018-09-05 00:00:00
Tasks: BIG-bench Machine Learning, Lesion Segmentation, Medical Image Analysis, Skin Lesion Segmentation

As many other machine learning driven medical image analysis tasks, skin
image analysis suffers from a chronic lack of labeled data and skewed class
distributions, which poses problems for the training of robust and
well-generalizing models. The ability to synthesize realistic looking images of
skin lesions could act as a reliever for the aforementioned problems.
Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking medical images, however limited to low
resolution, whereas machine learning models for challenging tasks such as skin
lesion segmentation or classification benefit from much higher resolution data.
In this work, we successfully synthesize realistically looking images of skin
lesions with GANs at such high resolution. Therefore, we utilize the concept of
progressive growing, which we both quantitatively and qualitatively compare to
other GAN architectures such as the DCGAN and the LAPGAN. Our results show that
with the help of progressive growing, we can synthesize highly realistic
dermoscopic images of skin lesions that even expert dermatologists find hard to
distinguish from real ones.
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Authors: Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy
Date: 2018-09-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition

Face recognition has witnessed great progress in recent years, mainly
attributed to the high-capacity model designed and the abundant labeled data
collected. However, it becomes more and more prohibitive to scale up the
current million-level identity annotations. In this work, we show that
unlabeled face data can be as effective as the labeled ones. Here, we consider
a setting closely mimicking the real-world scenario, where the unlabeled data
are collected from unconstrained environments and their identities are
exclusive from the labeled ones. Our main insight is that although the class
information is not available, we can still faithfully approximate these
semantic relationships by constructing a relational graph in a bottom-up
manner. We propose Consensus-Driven Propagation (CDP) to tackle this
challenging problem with two modules, the "committee" and the "mediator", which
select positive face pairs robustly by carefully aggregating multi-view
information. Extensive experiments validate the effectiveness of both modules
to discard outliers and mine hard positives. With CDP, we achieve a compelling
accuracy of 78.18% on MegaFace identification challenge by using only 9% of the
labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all
labels are employed.
Image Manipulation with Perceptual Discriminators
Authors: Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov, Victor Lempitsky
Date: 2018-09-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Manipulation, Translation

Systems that perform image manipulation using deep convolutional networks
have achieved remarkable realism. Perceptual losses and losses based on
adversarial discriminators are the two main classes of learning objectives
behind these advances. In this work, we show how these two ideas can be
combined in a principled and non-additive manner for unaligned image
translation tasks. This is accomplished through a special architecture of the
discriminator network inside generative adversarial learning framework. The new
architecture, that we call a perceptual discriminator, embeds the convolutional
parts of a pre-trained deep classification network inside the discriminator
network. The resulting architecture can be trained on unaligned image datasets
while benefiting from the robustness and efficiency of perceptual losses. We
demonstrate the merits of the new architecture in a series of qualitative and
quantitative comparisons with baseline approaches and state-of-the-art
frameworks for unaligned image translation.
On the optimality of the Hedge algorithm in the stochastic regime
Authors: Jaouad Mourtada, Stéphane Gaïffas
Date: 2018-09-05 00:00:00

In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simplest algorithm for the problem of prediction with expert advice, is surprisingly both worst-case optimal and adaptive to the easier stochastic and adversarial with a gap problems. This shows that, in spite of its small, non-adaptive learning rate, Hedge possesses the same optimal regret guarantee in the stochastic case as recently introduced adaptive algorithms. Moreover, our analysis exhibits qualitative differences with other variants of the Hedge algorithm, such as the fixed-horizon version (with constant learning rate) and the one based on the so-called "doubling trick", both of which fail to adapt to the easier stochastic setting. Finally, we discuss the limitations of anytime Hedge and the improvements provided by second-order regret bounds in the stochastic case.