pwc-archive/papers-with-abstracts huggingface dataset:
Improving Information Extraction from Images with Learned Semantic Models
Authors: Stephan Baier, Yunpu Ma, Volker Tresp
Date: 2018-08-27 00:00:00
Tasks: General Classification, Relationship Detection, Visual Relationship Detection

Many applications require an understanding of an image that goes beyond the
simple detection and classification of its objects. In particular, a great deal
of semantic information is carried in the relationships between objects. We
have previously shown that the combination of a visual model and a statistical
semantic prior model can improve on the task of mapping images to their
associated scene description. In this paper, we review the model and compare it
to a novel conditional multi-way model for visual relationship detection, which
does not include an explicitly trained visual prior model. We also discuss
potential relationships between the proposed methods and memory models of the
human brain.
Unsupervised Multilingual Word Embeddings
Authors: Xilun Chen, Claire Cardie
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multilingual Word Embeddings, Translation, Word Embeddings, Word Similarity, Word Translation

Multilingual Word Embeddings (MWEs) represent words from multiple languages
in a single distributional vector space. Unsupervised MWE (UMWE) methods
acquire multilingual embeddings without cross-lingual supervision, which is a
significant advantage over traditional supervised approaches and opens many new
possibilities for low-resource languages. Prior art for learning UMWEs,
however, merely relies on a number of independently trained Unsupervised
Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings. These
methods fail to leverage the interdependencies that exist among many languages.
To address this shortcoming, we propose a fully unsupervised framework for
learning MWEs that directly exploits the relations between all language pairs.
Our model substantially outperforms previous approaches in the experiments on
multilingual word translation and cross-lingual word similarity. In addition,
our model even beats supervised approaches trained with cross-lingual
resources.
Extracting Sentiment Attitudes From Analytical Texts
Authors: Natalia Loukachevitch, Nicolay Rusnachenko
Date: 2018-08-27 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we present the RuSentRel corpus including analytical texts in
the sphere of international relations. For each document we annotated
sentiments from the author to mentioned named entities, and sentiments of
relations between mentioned entities. In the current experiments, we considered
the problem of extracting sentiment relations between entities for the whole
documents as a three-class machine learning task. We experimented with
conventional machine-learning methods (Naive Bayes, SVM, Random Forest).
Smoothed Dilated Convolutions for Improved Dense Prediction
Authors: Zhengyang Wang, Shuiwang Ji
Date: 2018-08-27 00:00:00
Tasks: Audio Generation, Machine Translation, Object Detection, Prediction, Semantic Segmentation

Dilated convolutions, also known as atrous convolutions, have been widely
explored in deep convolutional neural networks (DCNNs) for various dense
prediction tasks. However, dilated convolutions suffer from the gridding
artifacts, which hampers the performance. In this work, we propose two simple
yet effective degridding methods by studying a decomposition of dilated
convolutions. Unlike existing models, which explore solutions by focusing on a
block of cascaded dilated convolutional layers, our methods address the
gridding artifacts by smoothing the dilated convolution itself. In addition, we
point out that the two degridding approaches are intrinsically related and
define separable and shared (SS) operations, which generalize the proposed
methods. We further explore SS operations in view of operations on graphs and
propose the SS output layer, which is able to smooth the entire DCNNs by only
replacing the output layer. We evaluate our degridding methods and the SS
output layer thoroughly, and visualize the smoothing effect through effective
receptive field analysis. Results show that our methods degridding yield
consistent improvements on the performance of dense prediction tasks, while
adding negligible amounts of extra training parameters. And the SS output layer
improves the performance significantly and is very efficient in terms of number
of training parameters.
Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
Authors: Zhenguo Nie, Haoliang Jiang, Levent Burak Kara
Date: 2018-08-27 00:00:00

The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNN). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multi-channel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available at https://github.com/zhenguonie/stress_net
Which Emoji Talks Best for My Picture?
Authors: Anurag Illendula, Kv Manohar, Manish Reddy Yedulla
Date: 2018-08-27 00:00:00

Emojis have evolved as complementary sources for expressing emotion in
social-media platforms where posts are mostly composed of texts and images. In
order to increase the expressiveness of the social media posts, users associate
relevant emojis with their posts. Incorporating domain knowledge has improved
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emoji recommendation task in
case of multimedia posts composed of image and text. Our emoji recommendation
can suggest accurate emojis by exploiting both visual and textual content from
social media posts as well as domain knowledge from Emojinet. Experimental
results using pre-trained image classifiers and pre-trained word embedding
models on Twitter dataset show that our results outperform the current
state-of-the-art by 9.6\%. We also present a user study evaluation of our
recommendation system on a set of images chosen from MSCOCO dataset.
Realizing quantum linear regression with auxiliary qumodes
Authors: Dan-Bo Zhang, Zheng-Yuan Xue, Shi-Liang Zhu, Z. D. Wang
Date: 2018-08-27 00:00:00
Tasks: regression

In order to exploit quantum advantages, quantum algorithms are indispensable
for operating machine learning with quantum computers. We here propose an
intriguing hybrid approach of quantum information processing for quantum linear
regression, which utilizes both discrete and continuous quantum variables, in
contrast to existing wisdoms based solely upon discrete qubits. In our
framework, data information is encoded in a qubit system, while information
processing is tackled using auxiliary continuous qumodes via qubit-qumode
interactions. Moreover, it is also elaborated that finite squeezing is quite
helpful for efficiently running the quantum algorithms in realistic setup.
Comparing with an all-qubit approach, the present hybrid approach is more
efficient and feasible for implementing quantum algorithms, still retaining
exponential quantum speed-up.
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
Authors: Heyi Li, Dong-Dong Chen, Bill Nailon, Mike Davies, Dave Laurenson
Date: 2018-08-27 00:00:00
Tasks: Segmentation

We explore the use of deep learning for breast mass segmentation in
mammograms. By integrating the merits of residual learning and probabilistic
graphical modelling with standard U-Net, we propose a new deep network,
Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation
performance. Benefiting from the advantage of probabilistic graphical modelling
in the pixel-level labelling, and the structure insights of a deep residual
network in the feature extraction, the CRU-Net provides excellent mass
segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets
demonstrate that the CRU-Net achieves the best mass segmentation performance
compared to the state-of-art methodologies. Moreover, neither tedious
pre-processing nor post-processing techniques are not required in our
algorithm.
BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters
Authors: Wei Chen, Mark Fuge
Date: 2018-08-27 00:00:00

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B\'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.
Facial Information Recovery from Heavily Damaged Images using Generative Adversarial Network- PART 1
Authors: Pushparaja Murugan
Date: 2018-08-27 00:00:00
Tasks: Generative Adversarial Network

Over the past decades, a large number of techniques have emerged in modern
imaging systems to capture the exact information of the original scene
regardless of shake, motion, lighting conditions and etc., These developments
have progressively addressed the acquisition of images in high speed and high
resolutions. However, the various ineradicable real-time factors cause the
degradation of the information and the quality of the acquired images. The
available techniques are not intelligent enough to generalize this complex
phenomenon. Hence, it is necessary to develop an intellectual framework to
recover the possible information presented in the original scene. In this
article, we propose a kernel free framework based on conditional-GAN to recover
the information from the heavily damaged images. The degradation of images is
assumed to be occurred by the combination of a various blur. Learning parameter
of the cGAN is optimized by multi-component loss function that includes
improved wasserstein loss with regression loss function. The generator module
of this network is developed by using U-Net architecture with local Residual
connections and global skip connection. Local connections and a global skip
connection are implemented for the utilization of all stages of features.
Generated images show that the network has the potential to recover the
probable information of blurred images from the learned features. This research
work is carried out as a part of our IOP studio software 'Facial recognition
module'.
A Study of Reinforcement Learning for Neural Machine Translation
Authors: Lijun Wu, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Recent studies have shown that reinforcement learning (RL) is an effective
approach for improving the performance of neural machine translation (NMT)
system. However, due to its instability, successfully RL training is
challenging, especially in real-world systems where deep models and large
datasets are leveraged. In this paper, taking several large-scale translation
tasks as testbeds, we conduct a systematic study on how to train better NMT
models using reinforcement learning. We provide a comprehensive comparison of
several important factors (e.g., baseline reward, reward shaping) in RL
training. Furthermore, to fill in the gap that it remains unclear whether RL is
still beneficial when monolingual data is used, we propose a new method to
leverage RL to further boost the performance of NMT systems trained with
source/target monolingual data. By integrating all our findings, we obtain
competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17
Chinese-English translation tasks, especially setting a state-of-the-art
performance on WMT17 Chinese-English translation task.
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation
Authors: Nikolay Bogoychev, Marcin Junczys-Dowmunt, Kenneth Heafield, Alham Fikri Aji
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

In order to extract the best possible performance from asynchronous
stochastic gradient descent one must increase the mini-batch size and scale the
learning rate accordingly. In order to achieve further speedup we introduce a
technique that delays gradient updates effectively increasing the mini-batch
size. Unfortunately with the increase of mini-batch size we worsen the stale
gradient problem in asynchronous stochastic gradient descent (SGD) which makes
the model convergence poor. We introduce local optimizers which mitigate the
stale gradient problem and together with fine tuning our momentum we are able
to train a shallow machine translation system 27% faster than an optimized
baseline with negligible penalty in BLEU.
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
Authors: Stefanos Angelidis, Mirella Lapata
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Aspect Extraction, Form, Multiple Instance Learning, Opinion Summarization

We present a neural framework for opinion summarization from online product
reviews which is knowledge-lean and only requires light supervision (e.g., in
the form of product domain labels and user-provided ratings). Our method
combines two weakly supervised components to identify salient opinions and form
extractive summaries from multiple reviews: an aspect extractor trained under a
multi-task objective, and a sentiment predictor based on multiple instance
learning. We introduce an opinion summarization dataset that includes a
training set of product reviews from six diverse domains and human-annotated
development and test sets with gold standard aspect annotations, salience
labels, and opinion summaries. Automatic evaluation shows significant
improvements over baselines, and a large-scale study indicates that our opinion
summaries are preferred by human judges according to multiple criteria.
WiSeBE: Window-based Sentence Boundary Evaluation
Authors: Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno
Date: 2018-08-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Boundary Detection, Part-Of-Speech Tagging, Question Answering, Sentence, speech-recognition, Speech Recognition

Sentence Boundary Detection (SBD) has been a major research topic since
Automatic Speech Recognition transcripts have been used for further Natural
Language Processing tasks like Part of Speech Tagging, Question Answering or
Automatic Summarization. But what about evaluation? Do standard evaluation
metrics like precision, recall, F-score or classification error; and more
important, evaluating an automatic system against a unique reference is enough
to conclude how well a SBD system is performing given the final application of
the transcript? In this paper we propose Window-based Sentence Boundary
Evaluation (WiSeBE), a semi-supervised metric for evaluating Sentence Boundary
Detection systems based on multi-reference (dis)agreement. We evaluate and
compare the performance of different SBD systems over a set of Youtube
transcripts using WiSeBE and standard metrics. This double evaluation gives an
understanding of how WiSeBE is a more reliable metric for the SBD task.
A strong baseline for question relevancy ranking
Authors: Ana V. González-Garduño, Isabelle Augenstein, Anders Søgaard
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Community Question Answering, Feature Engineering, Question Answering

The best systems at the SemEval-16 and SemEval-17 community question
answering shared tasks -- a task that amounts to question relevancy ranking --
involve complex pipelines and manual feature engineering. Despite this, many of
these still fail at beating the IR baseline, i.e., the rankings provided by
Google's search engine. We present a strong baseline for question relevancy
ranking by training a simple multi-task feed forward network on a bag of 14
distance measures for the input question pair. This baseline model, which is
fast to train and uses only language-independent features, outperforms the best
shared task systems on the task of retrieving relevant previously asked
questions.
Real-Time MDNet
Authors: Ilchae Jung, Jeany Son, Mooyeol Baek, Bohyung Han
Date: 2018-08-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Visual Tracking

We present a fast and accurate visual tracking algorithm based on the
multi-domain convolutional neural network (MDNet). The proposed approach
accelerates feature extraction procedure and learns more discriminative models
for instance classification; it enhances representation quality of target and
background by maintaining a high resolution feature map with a large receptive
field per activation. We also introduce a novel loss term to differentiate
foreground instances across multiple domains and learn a more discriminative
embedding of target objects with similar semantics. The proposed techniques are
integrated into the pipeline of a well known CNN-based visual tracking
algorithm, MDNet. We accomplish approximately 25 times speed-up with almost
identical accuracy compared to MDNet. Our algorithm is evaluated in multiple
popular tracking benchmark datasets including OTB2015, UAV123, and TempleColor,
and outperforms the state-of-the-art real-time tracking methods consistently
even without dataset-specific parameter tuning.
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
Authors: Merlin Schüler, Hlynur Davíð Hlynsson, Laurenz Wiskott
Date: 2018-08-27 00:00:00

We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction. We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) ego-visual data, and also for (c) a general dataset for which symmetric non-temporal similarities between points can be defined.
A new Taxonomy of Continuous Global Optimization Algorithms
Authors: Jörg Stork, A. E. Eiben, Thomas Bartz-Beielstein
Date: 2018-08-27 00:00:00
Tasks: global-optimization

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by design optimization. The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.
Exponential inequalities for nonstationary Markov Chains
Authors: Pierre Alquier, Paul Doukhan, Xiequan Fan
Date: 2018-08-27 00:00:00
Tasks: Learning Theory, Time Series, Time Series Analysis

Exponential inequalities are main tools in machine learning theory. To prove exponential inequalities for non i.i.d random variables allows to extend many learning techniques to these variables. Indeed, much work has been done both on inequalities and learning theory for time series, in the past 15 years. However, for the non independent case, almost all the results concern stationary time series. This excludes many important applications: for example any series with a periodic behavior is non-stationary. In this paper, we extend the basic tools of Dedecker and Fan (2015) to nonstationary Markov chains. As an application, we provide a Bernstein-type inequality, and we deduce risk bounds for the prediction of periodic autoregressive processes with an unknown period.
Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions
Authors: Ke Ning, Linchao Zhu, Ming Cai, Yi Yang, Di Xie, Fei Wu
Date: 2018-08-27 00:00:00
Tasks: Translation, Video Description

We propose a novel attentive sequence to sequence translator (ASST) for clip
localization in videos by natural language descriptions. We make two
contributions. First, we propose a bi-directional Recurrent Neural Network
(RNN) with a finely calibrated vision-language attentive mechanism to
comprehensively understand the free-formed natural language descriptions. The
RNN parses natural language descriptions in two directions, and the attentive
model attends every meaningful word or phrase to each frame, thereby resulting
in a more detailed understanding of video content and description semantics.
Second, we design a hierarchical architecture for the network to jointly model
language descriptions and video content. Given a video-description pair, the
network generates a matrix representation, i.e., a sequence of vectors. Each
vector in the matrix represents a video frame conditioned by the description.
The 2D representation not only preserves the temporal dependencies of frames
but also provides an effective way to perform frame-level video-language
matching. The hierarchical architecture exploits video content with multiple
granularities, ranging from subtle details to global context. Integration of
the multiple granularities yields a robust representation for multi-level
video-language abstraction. We validate the effectiveness of our ASST on two
large-scale datasets. Our ASST outperforms the state-of-the-art by $4.28\%$ in
Rank$@1$ on the DiDeMo dataset. On the Charades-STA dataset, we significantly
improve the state-of-the-art by $13.41\%$ in Rank$@1,IoU=0.5$.