pwc-archive/papers-with-abstracts huggingface dataset:
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
Authors: Kangxue Yin, Hui Huang, Daniel Cohen-Or, Hao Zhang
Date: 2018-03-25 00:00:00

We introduce P2P-NET, a general-purpose deep neural network which learns
geometric transformations between point-based shape representations from two
domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc.
The architecture of the P2P-NET is that of a bi-directional point displacement
network, which transforms a source point set to a target point set with the
same cardinality, and vice versa, by applying point-wise displacement vectors
learned from data. P2P-NET is trained on paired shapes from the source and
target domains, but without relying on point-to-point correspondences between
the source and target point sets. The training loss combines two
uni-directional geometric losses, each enforcing a shape-wise similarity
between the predicted and the target point sets, and a cross-regularization
term to encourage consistency between displacement vectors going in opposite
directions. We develop and present several different applications enabled by
our general-purpose bidirectional P2P-NET to highlight the effectiveness,
versatility, and potential of our network in solving a variety of point-based
shape transformation problems.
Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization
Authors: Yiyun Pan, Junwei Zhou, Yongsheng Gao, Shengwu Xiong
Date: 2018-05-15 00:00:00
Tasks: Face Alignment, regression

Robust facial landmark localization remains a challenging task when faces are
partially occluded. Recently, the cascaded pose regression has attracted
increasing attentions, due to it's superior performance in facial landmark
localization and occlusion detection. However, such an approach is sensitive to
initialization, where an improper initialization can severly degrade the
performance. In this paper, we propose a Robust Initialization for Cascaded
Pose Regression (RICPR) by providing texture and pose correlated initial shapes
for the testing face. By examining the correlation of local binary patterns
histograms between the testing face and the training faces, the shapes of the
training faces that are most correlated with the testing face are selected as
the texture correlated initialization. To make the initialization more robust
to various poses, we estimate the rough pose of the testing face according to
five fiducial landmarks located by multitask cascaded convolutional networks.
Then the pose correlated initial shapes are constructed by the mean face's
shape and the rough testing face pose. Finally, the texture correlated and the
pose correlated initial shapes are joined together as the robust
initialization. We evaluate RICPR on the challenging dataset of COFW. The
experimental results demonstrate that the proposed scheme achieves better
performances than the state-of-the-art methods in facial landmark localization
and occlusion detection.
Image Co-segmentation via Multi-scale Local Shape Transfer
Authors: Wei Teng, Yu Zhang, Xiaowu Chen, Jia Li, Zhiqiang He
Date: 2018-05-15 00:00:00

Image co-segmentation is a challenging task in computer vision that aims to
segment all pixels of the objects from a predefined semantic category. In
real-world cases, however, common foreground objects often vary greatly in
appearance, making their global shapes highly inconsistent across images and
difficult to be segmented. To address this problem, this paper proposes a novel
co-segmentation approach that transfers patch-level local object shapes which
appear more consistent across different images. In our framework, a multi-scale
patch neighbourhood system is first generated using proposal flow on arbitrary
image-pair, which is further refined by Locally Linear Embedding. Based on the
patch relationships, we propose an efficient algorithm to jointly segment the
objects in each image while transferring their local shapes across different
images. Extensive experiments demonstrate that the proposed method can robustly
and effectively segment common objects from an image set. On iCoseg, MSRC and
Coseg-Rep dataset, the proposed approach performs comparable or better than the
state-of-thearts, while on a more challenging benchmark Fashionista dataset,
our method achieves significant improvements.
Nonparametric Bayesian volatility learning under microstructure noise
Authors: Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij
Date: 2018-05-15 00:00:00

In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we \emph{a priori} model the volatility function as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs sampler. Good performance of the method is demonstrated on two representative synthetic data examples. We also apply the method on a EUR/USD exchange rate dataset. Finally we present a limit result on the prior distribution.
Neural Classification of Malicious Scripts: A study with JavaScript and VBScript
Authors: Jack W. Stokes, Rakshit Agrawal, Geoff McDonald
Date: 2018-05-15 00:00:00
Tasks: General Classification

Malicious scripts are an important computer infection threat vector. Our
analysis reveals that the two most prevalent types of malicious scripts include
JavaScript and VBScript. The percentage of detected JavaScript attacks are on
the rise. To address these threats, we investigate two deep recurrent models,
LaMP (LSTM and Max Pooling) and CPoLS (Convoluted Partitioning of Long
Sequences), which process JavaScript and VBScript as byte sequences. Lower
layers capture the sequential nature of these byte sequences while higher
layers classify the resulting embedding as malicious or benign. Unlike
previously proposed solutions, our models are trained in an end-to-end fashion
allowing discriminative training even for the sequential processing layers.
Evaluating these models on a large corpus of 296,274 JavaScript files indicates
that the best performing LaMP model has a 65.9% true positive rate (TPR) at a
false positive rate (FPR) of 1.0%. Similarly, the best CPoLS model has a TPR of
45.3% at an FPR of 1.0%. LaMP and CPoLS yield a TPR of 69.3% and 67.9%,
respectively, at an FPR of 1.0% on a collection of 240,504 VBScript files.
Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]
Authors: Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas
Date: 2018-05-15 00:00:00
Tasks: Articles, BIG-bench Machine Learning

Over the past few decades, the amount of scientific articles and technical
literature has increased exponentially in size. Consequently, there is a great
need for systems that can ingest these documents at scale and make their
content discoverable. Unfortunately, both the format of these documents (e.g.
the PDF format or bitmap images) as well as the presentation of the data (e.g.
complex tables) make the extraction of qualitative and quantitive data
extremely challenging. We present a platform to ingest documents at scale which
is powered by Machine Learning techniques and allows the user to train custom
models on document collections. We show precision/recall results greater than
97% with regard to conversion to structured formats, as well as scaling
evidence for each of the microservices constituting the platform.
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Authors: Masaki Asada, Makoto Miwa, Yutaka Sasaki
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Drug–drug Interaction Extraction

We propose a novel neural method to extract drug-drug interactions (DDIs)
from texts using external drug molecular structure information. We encode
textual drug pairs with convolutional neural networks and their molecular pairs
with graph convolutional networks (GCNs), and then we concatenate the outputs
of these two networks. In the experiments, we show that GCNs can predict DDIs
from the molecular structures of drugs in high accuracy and the molecular
information can enhance text-based DDI extraction by 2.39 percent points in the
F-score on the DDIExtraction 2013 shared task data set.
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
Authors: Yudong Guo, Juyong Zhang, Jianfei Cai, Boyi Jiang, Jianmin Zheng
Date: 2017-08-03 00:00:00
Tasks: 3D Face Reconstruction, Face Model, Face Reconstruction, Inverse Rendering

With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Authors: Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Intent Detection, slot-filling, Slot Filling, Spoken Language Understanding

The success of many natural language processing (NLP) tasks is bound by the
number and quality of annotated data, but there is often a shortage of such
training data. In this paper, we ask the question: "Can we combine a neural
network (NN) with regular expressions (RE) to improve supervised learning for
NLP?". In answer, we develop novel methods to exploit the rich expressiveness
of REs at different levels within a NN, showing that the combination
significantly enhances the learning effectiveness when a small number of
training examples are available. We evaluate our approach by applying it to
spoken language understanding for intent detection and slot filling.
Experimental results show that our approach is highly effective in exploiting
the available training data, giving a clear boost to the RE-unaware NN.
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
Authors: Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, Jingming Liu
Date: 2018-03-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Knowledge, Reading Comprehension

This paper describes our system for SemEval-2018 Task 11: Machine
Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks
(TriAN) to model interactions between the passage, question and answers. To
incorporate commonsense knowledge, we augment the input with relation embedding
from the graph of general knowledge ConceptNet (Speer et al., 2017). As a
result, our system achieves state-of-the-art performance with 83.95% accuracy
on the official test data. Code is publicly available at
https://github.com/intfloat/commonsense-rc
Unsupervised Learning of Style-sensitive Word Vectors
Authors: Reina Akama, Kento Watanabe, Sho Yokoi, Sosuke Kobayashi, Kentaro Inui
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Word Embeddings

This paper presents the first study aimed at capturing stylistic similarity
between words in an unsupervised manner. We propose extending the continuous
bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word
vectors using a wider context window under the assumption that the style of all
the words in an utterance is consistent. In addition, we introduce a novel task
to predict lexical stylistic similarity and to create a benchmark dataset for
this task. Our experiment with this dataset supports our assumption and
demonstrates that the proposed extensions contribute to the acquisition of
style-sensitive word embeddings.
Prediction of Facebook Post Metrics using Machine Learning
Authors: Emmanuel Sam, Sergey Yarushev, Sebastián Basterrech, Alexey Averkin
Date: 2018-05-15 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, regression

In this short paper, we evaluate the performance of three well-known Machine
Learning techniques for predicting the impact of a post in Facebook. Social
medias have a huge influence in the social behaviour. Therefore to develop an
automatic model for predicting the impact of posts in social medias can be
useful to the society. In this article, we analyze the efficiency for
predicting the post impact of three popular techniques: Support Vector
Regression (SVR), Echo State Network (ESN) and Adaptive Network Fuzzy Inject
System (ANFIS). The evaluation was done over a public and well-known benchmark
dataset.
The Future of Prosody: It's about Time
Authors: Dafydd Gibbon
Date: 2018-04-23 00:00:00

Prosody is usually defined in terms of the three distinct but interacting
domains of pitch, intensity and duration patterning, or, more generally, as
phonological and phonetic properties of 'suprasegmentals', speech segments
which are larger than consonants and vowels. Rather than taking this approach,
the concept of multiple time domains for prosody processing is taken up, and
methods of time domain analysis are discussed: annotation mining with timing
dispersion measures, time tree induction, oscillator models in phonology and
phonetics, and finally the use of the Amplitude Envelope Modulation Spectrum
(AEMS). While frequency demodulation (in the form of pitch tracking) is a
central issue in prosodic analysis, in the present context it is amplitude
envelope demodulation and frequency zones in the long time-domain spectra of
the demodulated envelope which are focused. A generalised view is taken of
oscillation as iteration in abstract prosodic models and as modulation and
demodulation of a variety of rhythms in the speech signal.
Stochastic Answer Networks for Machine Reading Comprehension
Authors: Xiaodong Liu, Yelong Shen, Kevin Duh, Jianfeng Gao
Date: 2017-12-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a simple yet robust stochastic answer network (SAN) that simulates
multi-step reasoning in machine reading comprehension. Compared to previous
work such as ReasoNet which used reinforcement learning to determine the number
of steps, the unique feature is the use of a kind of stochastic prediction
dropout on the answer module (final layer) of the neural network during the
training. We show that this simple trick improves robustness and achieves
results competitive to the state-of-the-art on the Stanford Question Answering
Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading
COmprehension Dataset (MS MARCO).
Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks
Authors: Di He, Boon Pang Lim, Xuesong Yang, Mark Hasegawa-Johnson, Deming Chen
Date: 2018-05-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Multi-Task Learning, speech-recognition, Speech Recognition

Furui first demonstrated that the identity of both consonant and vowel can be
perceived from the C-V transition; later, Stevens proposed that acoustic
landmarks are the primary cues for speech perception, and that steady-state
regions are secondary or supplemental. Acoustic landmarks are perceptually
salient, even in a language one doesn't speak, and it has been demonstrated
that non-speakers of the language can identify features such as the primary
articulator of the landmark. These factors suggest a strategy for developing
language-independent automatic speech recognition: landmarks can potentially be
learned once from a suitably labeled corpus and rapidly applied to many other
languages. This paper proposes enhancing the cross-lingual portability of a
neural network by using landmarks as the secondary task in multi-task learning
(MTL). The network is trained in a well-resourced source language with both
phone and landmark labels (English), then adapted to an under-resourced target
language with only word labels (Iban). Landmark-tasked MTL reduces
source-language phone error rate by 2.9% relative, and reduces target-language
word error rate by 1.9%-5.9% depending on the amount of target-language
training data. These results suggest that landmark-tasked MTL causes the DNN to
learn hidden-node features that are useful for cross-lingual adaptation.
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
Authors: Ryota Yoshihashi, Tu Tuan Trinh, Rei Kawakami, ShaoDi You, Makoto Iida, Takeshi Naemura
Date: 2017-09-14 00:00:00
Tasks: Object, object-detection, Object Detection, Open-Ended Question Answering, Small Object Detection

While generic object detection has achieved large improvements with rich
feature hierarchies from deep nets, detecting small objects with poor visual
cues remains challenging. Motion cues from multiple frames may be more
informative for detecting such hard-to-distinguish objects in each frame.
However, how to encode discriminative motion patterns, such as deformations and
pose changes that characterize objects, has remained an open question. To learn
them and thereby realize small object detection, we present a neural model
called the Recurrent Correlational Network, where detection and tracking are
jointly performed over a multi-frame representation learned through a single,
trainable, and end-to-end network. A convolutional long short-term memory
network is utilized for learning informative appearance change for detection,
while learned representation is shared in tracking for enhancing its
performance. In experiments with datasets containing images of scenes with
small flying objects, such as birds and unmanned aerial vehicles, the proposed
method yielded consistent improvements in detection performance over deep
single-frame detectors and existing motion-based detectors. Furthermore, our
network performs as well as state-of-the-art generic object trackers when it
was evaluated as a tracker on the bird dataset.
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
Authors: Seiichiro Fukuda, Ryota Yoshihashi, Rei Kawakami, ShaoDi You, Makoto Iida, Takeshi Naemura
Date: 2018-05-15 00:00:00
Tasks: Multi-Task Learning, Segmentation

Multi-task learning improves generalization performance by sharing knowledge
among related tasks. Existing models are for task combinations annotated on the
same dataset, while there are cases where multiple datasets are available for
each task. How to utilize knowledge of successful single-task CNNs that are
trained on each dataset has been explored less than multi-task learning with a
single dataset. We propose a cross-connected CNN, a new architecture that
connects single-task CNNs through convolutional layers, which transfer useful
information for the counterpart. We evaluated our proposed architecture on a
combination of detection and segmentation using two datasets. Experiments on
pedestrians show our CNN achieved a higher detection performance compared to
baseline CNNs, while maintaining high quality for segmentation. It is the first
known attempt to tackle multi-task learning with different training datasets
between detection and segmentation. Experiments with wild birds demonstrate how
our CNN learns general representations from limited datasets.
Facial Landmark Detection: a Literature Survey
Authors: Yue Wu, Qiang Ji
Date: 2018-05-15 00:00:00
Tasks: Facial Landmark Detection, regression, Survey

The locations of the fiducial facial landmark points around facial components
and facial contour capture the rigid and non-rigid facial deformations due to
head movements and facial expressions. They are hence important for various
facial analysis tasks. Many facial landmark detection algorithms have been
developed to automatically detect those key points over the years, and in this
paper, we perform an extensive review of them. We classify the facial landmark
detection algorithms into three major categories: holistic methods, Constrained
Local Model (CLM) methods, and the regression-based methods. They differ in the
ways to utilize the facial appearance and shape information. The holistic
methods explicitly build models to represent the global facial appearance and
shape information. The CLMs explicitly leverage the global shape model but
build the local appearance models. The regression-based methods implicitly
capture facial shape and appearance information. For algorithms within each
category, we discuss their underlying theories as well as their differences. We
also compare their performances on both controlled and in the wild benchmark
datasets, under varying facial expressions, head poses, and occlusion. Based on
the evaluations, we point out their respective strengths and weaknesses. There
is also a separate section to review the latest deep learning-based algorithms.
  The survey also includes a listing of the benchmark databases and existing
software. Finally, we identify future research directions, including combining
methods in different categories to leverage their respective strengths to solve
landmark detection "in-the-wild".
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Authors: Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang
Date: 2018-03-20 00:00:00
Tasks: Adversarial Attack, Deep Learning, Defect Detection

Deep learning (DL) defines a new data-driven programming paradigm that
constructs the internal system logic of a crafted neuron network through a set
of training data. We have seen wide adoption of DL in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems suffer from various vulnerabilities which can lead to severe
consequences when applied to real-world applications. Currently, the testing
adequacy of a DL system is usually measured by the accuracy of test data.
Considering the limitation of accessible high quality test data, good accuracy
performance on test data can hardly provide confidence to the testing adequacy
and generality of DL systems. Unlike traditional software systems that have
clear and controllable logic and functionality, the lack of interpretability in
a DL system makes system analysis and defect detection difficult, which could
potentially hinder its real-world deployment. In this paper, we propose
DeepGauge, a set of multi-granularity testing criteria for DL systems, which
aims at rendering a multi-faceted portrayal of the testbed. The in-depth
evaluation of our proposed testing criteria is demonstrated on two well-known
datasets, five DL systems, and with four state-of-the-art adversarial attack
techniques against DL. The potential usefulness of DeepGauge sheds light on the
construction of more generic and robust DL systems.