pwc-archive/papers-with-abstracts huggingface dataset:
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions
Authors: Stephan Baier, Yunpu Ma, Volker Tresp
Date: 2018-09-01 00:00:00
Tasks: Link Prediction, object-detection, Object Detection, Prediction, Relationship Detection, Visual Relationship Detection

Structured scene descriptions of images are useful for the automatic
processing and querying of large image databases. We show how the combination
of a semantic and a visual statistical model can improve on the task of mapping
images to their associated scene description. In this paper we consider scene
descriptions which are represented as a set of triples (subject, predicate,
object), where each triple consists of a pair of visual objects, which appear
in the image, and the relationship between them (e.g. man-riding-elephant,
man-wearing-hat). We combine a standard visual model for object detection,
based on convolutional neural networks, with a latent variable model for link
prediction. We apply multiple state-of-the-art link prediction methods and
compare their capability for visual relationship detection. One of the main
advantages of link prediction methods is that they can also generalize to
triples, which have never been observed in the training data. Our experimental
results on the recently published Stanford Visual Relationship dataset, a
challenging real world dataset, show that the integration of a semantic model
using link prediction methods can significantly improve the results for visual
relationship detection. Our combined approach achieves superior performance
compared to the state-of-the-art method from the Stanford computer vision
group.
Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora
Authors: Marcin Junczys-Dowmunt
Date: 2018-09-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Translation

In this work we introduce dual conditional cross-entropy filtering for noisy
parallel data. For each sentence pair of the noisy parallel corpus we compute
cross-entropy scores according to two inverse translation models trained on
clean data. We penalize divergent cross-entropies and weigh the penalty by the
cross-entropy average of both models. Sorting or thresholding according to
these scores results in better subsets of parallel data. We achieve higher BLEU
scores with models trained on parallel data filtered only from Paracrawl than
with models trained on clean WMT data. We further evaluate our method in the
context of the WMT2018 shared task on parallel corpus filtering and achieve the
overall highest ranking scores of the shared task, scoring top in three out of
four subtasks.
Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data
Authors: Marcin Junczys-Dowmunt
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sentence, Translation

This paper describes the Microsoft submission to the WMT2018 news translation
shared task. We participated in one language direction -- English-German. Our
system follows current best-practice and combines state-of-the-art models with
new data filtering (dual conditional cross-entropy filtering) and sentence
weighting methods. We trained fairly standard Transformer-big models with an
updated version of Edinburgh's training scheme for WMT2017 and experimented
with different filtering schemes for Paracrawl. According to automatic metrics
(BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point
margin over the next strongest system. Based on human evaluation we ranked
first among constrained systems. We believe this is mostly caused by our data
filtering/weighting regime.
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Authors: Tianyang Wang, Jun Huan, Bo Li
Date: 2018-09-01 00:00:00
Tasks: Denoising, image-classification, Image Classification, Image Denoising

Deep learning models learn to fit training data while they are highly
expected to generalize well to testing data. Most works aim at finding such
models by creatively designing architectures and fine-tuning parameters. To
adapt to particular tasks, hand-crafted information such as image prior has
also been incorporated into end-to-end learning. However, very little progress
has been made on investigating how an individual training sample will influence
the generalization ability of a model. In other words, to achieve high
generalization accuracy, do we really need all the samples in a training
dataset? In this paper, we demonstrate that deep learning models such as
convolutional neural networks may not favor all training samples, and
generalization accuracy can be further improved by dropping those unfavorable
samples. Specifically, the influence of removing a training sample is
quantifiable, and we propose a Two-Round Training approach, aiming to achieve
higher generalization accuracy. We locate unfavorable samples after the first
round of training, and then retrain the model from scratch with the reduced
training dataset in the second round. Since our approach is essentially
different from fine-tuning or further training, the computational cost should
not be a concern. Our extensive experimental results indicate that, with
identical settings, the proposed approach can boost performance of the
well-known networks on both high-level computer vision problems such as image
classification, and low-level vision problems such as image denoising.
MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Post-Editing, NMT

This paper describes the Microsoft and University of Edinburgh submission to
the Automatic Post-editing shared task at WMT2018. Based on training data and
systems from the WMT2017 shared task, we re-implement our own models from the
last shared task and introduce improvements based on extensive parameter
sharing. Next we experiment with our implementation of dual-source transformer
models and data selection for the IT domain. Our submissions decisively wins
the SMT post-editing sub-task establishing the new state-of-the-art and is a
very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on
the rather weak results in the NMT sub-task, we hypothesize that
neural-on-neural APE might not be actually useful.
Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition, Pupil Dilation

This paper presents the most comprehensive analysis of iris recognition
reliability in the occurrence of various biological processes happening
naturally and pathologically in the human body, including aging, illnesses, and
post-mortem changes to date. Insightful conclusions are offered in relation to
all three of these aspects. Extensive regression analysis of the template aging
phenomenon shows that differences in pupil dilation, combined with certain
quality factors of the sample image and the progression of time itself can
significantly degrade recognition accuracy. Impactful effects can also be
observed when iris recognition is employed with eyes affected by certain eye
pathologies or (even more) with eyes of the deceased subjects. Notably,
appropriate databases are delivered to the biometric community to stimulate
further research in these utterly important areas of iris biometrics studies.
Finally, some open questions are stated to inspire further discussions and
research on these important topics. To Authors' best knowledge, this is the
only scientific study of iris recognition reliability of such a broad scope and
novelty.
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
Authors: Kelvin Hsu, Richard Nock, Fabio Ramos
Date: 2018-09-01 00:00:00

Conditional kernel mean embeddings are nonparametric models that encode
conditional expectations in a reproducing kernel Hilbert space. While they
provide a flexible and powerful framework for probabilistic inference, their
performance is highly dependent on the choice of kernel and regularization
hyperparameters. Nevertheless, current hyperparameter tuning methods
predominantly rely on expensive cross validation or heuristics that is not
optimized for the inference task. For conditional kernel mean embeddings with
categorical targets and arbitrary inputs, we propose a hyperparameter learning
framework based on Rademacher complexity bounds to prevent overfitting by
balancing data fit against model complexity. Our approach only requires batch
updates, allowing scalable kernel hyperparameter tuning without invoking kernel
approximations. Experiments demonstrate that our learning framework outperforms
competing methods, and can be further extended to incorporate and learn deep
neural network weights to improve generalization.
Human Iris Recognition in Post-mortem Subjects: Study and Database
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition

This paper presents a unique study of post-mortem human iris recognition and
the first known to us database of near-infrared and visible-light iris images
of deceased humans collected up to almost 17 days after death. We used four
different iris recognition methods to analyze the dynamics of iris quality
decay in short-term comparisons (samples collected up to 60 hours after death)
and long-term comparisons (for samples acquired up to 407 hours after demise).
This study shows that post-mortem iris recognition is possible and occasionally
works even 17 days after death. These conclusions contradict a promulgated
rumor that iris is unusable shortly after decease. We make this dataset
publicly available to let others verify our findings and to research new
aspects of this important and unfamiliar topic. We are not aware of any earlier
papers offering post-mortem human iris images and such comprehensive analysis
employing four different matchers.
Linear regression analysis of template aging in iris biometrics
Authors: Mateusz Trokielewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition, regression

The aim of this work is to determine how vulnerable different iris coding
methods are in relation to biometric template aging phenomenon. This is
considered to be particularly important when the time lapse between gallery and
probe samples extends significantly, to more than a few years.
  Our experiments employ iris aging analysis conducted using three different
iris recognition algorithms and a database of 583 samples from 58 irises
collected up to nine years apart. To determine the degradation rates of
similarity scores with extending time lapse and also in relation to multiple
image quality and geometrical factors of sample images, a linear regression
analysis was performed. 29 regression models have been tested with both the
time parameter and geometrical factors being statistically significant in every
model. Quality measures that showed statistically significant influence on the
predicted variable were, depending on the method, image sharpness and local
contrast or their mutual relations.
  To our best knowledge, this is the first paper describing aging analysis
using multiple regression models with data covering such a wide time period.
Results presented suggest that template aging effect occurs in iris biometrics
to a statistically significant extent. Image quality and geometrical factors
may contribute to the degradation of similarity score. However, the estimate of
time parameter showed statistical significance and similar value in each of the
tested models. This reveals that the aging phenomenon may as well be unrelated
to quality and geometrical measures of the image.
Implications of Ocular Pathologies for Iris Recognition Reliability
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition

This paper presents an analysis of how iris recognition is influenced by eye
disease and an appropriate dataset comprising 2996 images of irises taken from
230 distinct eyes (including 184 affected by more than 20 different eye
conditions). The images were collected in near infrared and visible light
during routine ophthalmological examination. The experimental study carried out
utilizing four independent iris recognition algorithms (MIRLIN, VeriEye, OSIRIS
and IriCore) renders four valuable results. First, the enrollment process is
highly sensitive to those eye conditions that obstruct the iris or cause
geometrical distortions. Second, even those conditions that do not produce
visible changes to the structure of the iris may increase the dissimilarity
between samples of the same eyes. Third, eye conditions affecting the geometry
or the tissue structure of the iris or otherwise producing obstructions
significantly decrease same-eye similarity and have a lower, yet still
statistically significant, influence on impostor comparison scores. Fourth, for
unhealthy eyes, the most prominent effect of disease on iris recognition is to
cause segmentation errors. To our knowledge this paper describes the largest
database of iris images for disease-affected eyes made publicly available to
researchers and offers the most comprehensive study of what we can expect when
iris recognition is employed for diseased eyes.
LIUM-CVC Submissions for WMT18 Multimodal Translation Task
Authors: Ozan Caglayan, Adrien Bardet, Fethi Bougares, Loïc Barrault, Kai Wang, Marc Masana, Luis Herranz, Joost Van de Weijer
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the multimodal Neural Machine Translation systems
developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This
year we propose several modifications to our previous multimodal attention
architecture in order to better integrate convolutional features and refine
them using encoder-side information. Our final constrained submissions ranked
first for English-French and second for English-German language pairs among the
constrained submissions according to the automatic evaluation metric METEOR.
Why is unsupervised alignment of English embeddings from different algorithms so hard?
Authors: Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard
Date: 2018-09-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Word Embeddings

This paper presents a challenge to the community: Generative adversarial
networks (GANs) can perfectly align independent English word embeddings induced
using the same algorithm, based on distributional information alone; but fails
to do so, for two different embeddings algorithms. Why is that? We believe
understanding why, is key to understand both modern word embedding algorithms
and the limitations and instability dynamics of GANs. This paper shows that (a)
in all these cases, where alignment fails, there exists a linear transform
between the two embeddings (so algorithm biases do not lead to non-linear
differences), and (b) similar effects can not easily be obtained by varying
hyper-parameters. One plausible suggestion based on our initial experiments is
that the differences in the inductive biases of the embedding algorithms lead
to an optimization landscape that is riddled with local optima, leading to a
very small basin of convergence, but we present this more as a challenge paper
than a technical contribution.
Semi-supervised Learning on Graphs with Generative Adversarial Nets
Authors: Ming Ding, Jie Tang, Jie Zhang
Date: 2018-09-01 00:00:00

We investigate how generative adversarial nets (GANs) can help
semi-supervised learning on graphs. We first provide insights on working
principles of adversarial learning over graphs and then present GraphSGAN, a
novel approach to semi-supervised learning on graphs. In GraphSGAN, generator
and classifier networks play a novel competitive game. At equilibrium,
generator generates fake samples in low-density areas between subgraphs. In
order to discriminate fake samples from the real, classifier implicitly takes
the density property of subgraph into consideration. An efficient adversarial
learning algorithm has been developed to improve traditional normalized graph
Laplacian regularization with a theoretical guarantee. Experimental results on
several different genres of datasets show that the proposed GraphSGAN
significantly outperforms several state-of-the-art methods. GraphSGAN can be
also trained using mini-batch, thus enjoys the scalability advantage.
Contextual Encoding for Translation Quality Estimation
Authors: Junjie Hu, Wei-Cheng Chang, Yuexin Wu, Graham Neubig
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sentence, Translation

The task of word-level quality estimation (QE) consists of taking a source
sentence and machine-generated translation, and predicting which words in the
output are correct and which are wrong.
  In this paper, propose a method to effectively encode the local and global
contextual information for each target word using a three-part neural network
approach.
  The first part uses an embedding layer to represent words and their
part-of-speech tags in both languages. The second part leverages a
one-dimensional convolution layer to integrate local context information for
each target word. The third part applies a stack of feed-forward and recurrent
neural networks to further encode the global context in the sentence before
making the predictions. This model was submitted as the CMU entry to the
WMT2018 shared task on QE, and achieves strong results, ranking first in three
of the six tracks.
Simple Fusion: Return of the Language Model
Authors: Felix Stahlberg, James Cross, Veselin Stoyanov
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Language Modeling, Language Modelling, Machine Translation, model, NMT, Sentence, Translation

Neural Machine Translation (NMT) typically leverages monolingual data in
training through backtranslation. We investigate an alternative simple method
to use monolingual data for NMT training: We combine the scores of a
pre-trained and fixed language model (LM) with the scores of a translation
model (TM) while the TM is trained from scratch. To achieve that, we train the
translation model to predict the residual probability of the training data
added to the prediction of the LM. This enables the TM to focus its capacity on
modeling the source sentence since it can rely on the LM for fluency. We show
that our method outperforms previous approaches to integrate LMs into NMT while
the architecture is simpler as it does not require gating networks to balance
TM and LM. We observe gains of between +0.24 and +2.36 BLEU on all four test
sets (English-Turkish, Turkish-English, Estonian-English, Xhosa-English) on top
of ensembles without LM. We compare our method with alternative ways to utilize
monolingual data such as backtranslation, shallow fusion, and cold fusion.
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter
Authors: Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu
Date: 2018-09-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Sentence, Text Summarization, Translation

Neural machine translation usually adopts autoregressive models and suffers
from exposure bias as well as the consequent error propagation problem. Many
previous works have discussed the relationship between error propagation and
the \emph{accuracy drop} (i.e., the left part of the translated sentence is
often better than its right part in left-to-right decoding models) problem. In
this paper, we conduct a series of analyses to deeply understand this problem
and get several interesting findings. (1) The role of error propagation on
accuracy drop is overstated in the literature, although it indeed contributes
to the accuracy drop problem. (2) Characteristics of a language play a more
important role in causing the accuracy drop: the left part of the translation
result in a right-branching language (e.g., English) is more likely to be more
accurate than its right part, while the right part is more accurate for a
left-branching language (e.g., Japanese). Our discoveries are confirmed on
different model structures including Transformer and RNN, and in other sequence
generation tasks such as text summarization.
DAC-SDC Low Power Object Detection Challenge for UAV Applications
Authors: Xiaowei Xu, Xinyi Zhang, Bei Yu, X. Sharon Hu, Christopher Rowen, Jingtong Hu, Yiyu Shi
Date: 2018-09-01 00:00:00
Tasks: Object, object-detection, Object Detection

The 55th Design Automation Conference (DAC) held its first System Design
Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge
(LPODC) on designing and implementing novel algorithms based object detection
in images taken from unmanned aerial vehicles (UAV). The dataset includes 95
categories and 150k images, and the hardware platforms include Nvidia's TX2 and
Xilinx's PYNQ Z1. DAC-SDC'18 attracted more than 110 entries from 12 countries.
This paper presents in detail the dataset and evaluation procedure. It further
discusses the methods developed by some of the entries as well as
representative results. The paper concludes with directions for future
improvements.
Dependency-based Hybrid Trees for Semantic Parsing
Authors: Zhanming Jie, Wei Lu
Date: 2018-09-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Parsing

We propose a novel dependency-based hybrid tree model for semantic parsing,
which converts natural language utterance into machine interpretable meaning
representations. Unlike previous state-of-the-art models, the semantic
information is interpreted as the latent dependency between the natural
language words in our joint representation. Such dependency information can
capture the interactions between the semantics and natural language words. We
integrate a neural component into our model and propose an efficient
dynamic-programming algorithm to perform tractable inference. Through extensive
experiments on the standard multilingual GeoQuery dataset with eight languages,
we demonstrate that our proposed approach is able to achieve state-of-the-art
performance across several languages. Analysis also justifies the effectiveness
of using our new dependency-based representation.
Attentive Crowd Flow Machines
Authors: Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin
Date: 2018-09-01 00:00:00
Tasks: Management, Prediction

Traffic flow prediction is crucial for urban traffic management and public
safety. Its key challenges lie in how to adaptively integrate the various
factors that affect the flow changes. In this paper, we propose a unified
neural network module to address this problem, called Attentive Crowd Flow
Machine~(ACFM), which is able to infer the evolution of the crowd flow by
learning dynamic representations of temporally-varying data with an attention
mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units
connected with a convolutional layer for spatial weight prediction. The first
LSTM takes the sequential flow density representation as input and generates a
hidden state at each time-step for attention map inference, while the second
LSTM aims at learning the effective spatial-temporal feature expression from
attentionally weighted crowd flow features. Based on the ACFM, we further build
a deep architecture with the application to citywide crowd flow prediction,
which naturally incorporates the sequential and periodic data as well as other
external influences. Extensive experiments on two standard benchmarks (i.e.,
crowd flow in Beijing and New York City) show that the proposed method achieves
significant improvements over the state-of-the-art methods.