pwc-archive/papers-with-abstracts huggingface dataset:
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
Authors: Xiaowei Zhang, Chi Xu, Yu Zhang, Tingshao Zhu, Li Cheng
Date: 2017-01-11 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation, regression

This paper studies the problem of multivariate linear regression where a
portion of the observations is grossly corrupted or is missing, and the
magnitudes and locations of such occurrences are unknown in priori. To deal
with this problem, we propose a new approach by explicitly consider the error
source as well as its sparseness nature. An interesting property of our
approach lies in its ability of allowing individual regression output elements
or tasks to possess their unique noise levels. Moreover, despite working with a
non-smooth optimization problem, our approach still guarantees to converge to
its optimal solution. Experiments on synthetic data demonstrate the
competitiveness of our approach compared with existing multivariate regression
models. In addition, empirically our approach has been validated with very
promising results on two exemplar real-world applications: The first concerns
the prediction of \textit{Big-Five} personality based on user behaviors at
social network sites (SNSs), while the second is 3D human hand pose estimation
from depth images. The implementation of our approach and comparison methods as
well as the involved datasets are made publicly available in support of the
open-source and reproducible research initiatives.
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Authors: Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha
Date: 2016-05-13 00:00:00
Tasks: Few-Shot Learning, Generalized Zero-Shot Learning, Object Recognition, Zero-Shot Learning

Zero-shot learning (ZSL) methods have been studied in the unrealistic setting
where test data are assumed to come from unseen classes only. In this paper, we
advocate studying the problem of generalized zero-shot learning (GZSL) where
the test data's class memberships are unconstrained. We show empirically that
naively using the classifiers constructed by ZSL approaches does not perform
well in the generalized setting. Motivated by this, we propose a simple but
effective calibration method that can be used to balance two conflicting
forces: recognizing data from seen classes versus those from unseen ones. We
develop a performance metric to characterize such a trade-off and examine the
utility of this metric in evaluating various ZSL approaches. Our analysis
further shows that there is a large gap between the performance of existing
approaches and an upper bound established via idealized semantic embeddings,
suggesting that improving class semantic embeddings is vital to GZSL.
A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach
Authors: Chenglong Li, Guizhao Wang, Yunpeng Ma, Aihua Zheng, Bin Luo, Jin Tang
Date: 2017-01-11 00:00:00
Tasks: Saliency Detection

Despite significant progress, image saliency detection still remains a
challenging task in complex scenes and environments. Integrating multiple
different but complementary cues, like RGB and Thermal (RGB-T), may be an
effective way for boosting saliency detection performance. The current research
in this direction, however, is limited by the lack of a comprehensive
benchmark. This work contributes such a RGB-T image dataset, which includes 821
spatially aligned RGB-T image pairs and their ground truth annotations for
saliency detection purpose. The image pairs are with high diversity recorded
under different scenes and environmental conditions, and we annotate 11
challenges on these image pairs for performing the challenge-sensitive analysis
for different saliency detection algorithms. We also implement 3 kinds of
baseline methods with different modality inputs to provide a comprehensive
comparison platform.
  With this benchmark, we propose a novel approach, multi-task manifold ranking
with cross-modality consistency, for RGB-T saliency detection. In particular,
we introduce a weight for each modality to describe the reliability, and
integrate them into the graph-based manifold ranking algorithm to achieve
adaptive fusion of different source data. Moreover, we incorporate the
cross-modality consistent constraints to integrate different modalities
collaboratively. For the optimization, we design an efficient algorithm to
iteratively solve several subproblems with closed-form solutions. Extensive
experiments against other baseline methods on the newly created benchmark
demonstrate the effectiveness of the proposed approach, and we also provide
basic insights and potential future research directions for RGB-T saliency
detection.
Bidirectional American Sign Language to English Translation
Authors: Hardie Cate, Zeshan Hussain
Date: 2017-01-10 00:00:00
Tasks: Machine Translation, Translation, Word Alignment

We outline a bidirectional translation system that converts sentences from
American Sign Language (ASL) to English, and vice versa. To perform machine
translation between ASL and English, we utilize a generative approach.
Specifically, we employ an adjustment to the IBM word-alignment model 1 (IBM
WAM1), where we define language models for English and ASL, as well as a
translation model, and attempt to generate a translation that maximizes the
posterior distribution defined by these models. Then, using these models, we
are able to quantify the concepts of fluency and faithfulness of a translation
between languages.
Learning to Translate in Real-time with Neural Machine Translation
Authors: Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O. K. Li
Date: 2016-10-03 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, NMT, Sentence, Translation

Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.
Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
Authors: Shyam Upadhyay, Ming-Wei Chang
Date: 2016-09-23 00:00:00
Proceeding: EACL 2017 4

We propose a new evaluation for automatic solvers for algebra word problems,
which can identify mistakes that existing evaluations overlook. Our proposal is
to evaluate such solvers using derivations, which reflect how an equation
system was constructed from the word problem. To accomplish this, we develop an
algorithm for checking the equivalence between two derivations, and show how
derivation an- notations can be semi-automatically added to existing datasets.
To make our experiments more comprehensive, we include the derivation
annotation for DRAW-1K, a new dataset containing 1000 general algebra word
problems. In our experiments, we found that the annotated derivations enable a
more accurate evaluation of automatic solvers than previously used metrics. We
release derivation annotations for over 2300 algebra word problems for future
evaluations.
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach
Authors: Xiaowei Zhou, Menglong Zhu, Spyridon Leonardos, Kostas Daniilidis
Date: 2015-09-14 00:00:00
Tasks: 3D Human Pose Estimation

We investigate the problem of estimating the 3D shape of an object defined by
a set of 3D landmarks, given their 2D correspondences in a single image. A
successful approach to alleviating the reconstruction ambiguity is the 3D
deformable shape model and a sparse representation is often used to capture
complex shape variability. But the model inference is still a challenge due to
the nonconvexity in optimization resulted from joint estimation of shape and
viewpoint. In contrast to prior work that relies on a alternating scheme with
solutions depending on initialization, we propose a convex approach to
addressing this challenge and develop an efficient algorithm to solve the
proposed convex program. Moreover, we propose a robust model to handle gross
errors in the 2D correspondences. We demonstrate the exact recovery property of
the proposed method, the advantage compared to the nonconvex baseline methods
and the applicability to recover 3D human poses and car models from single
images.
What Do Recurrent Neural Network Grammars Learn About Syntax?
Authors: Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, Noah A. Smith
Date: 2016-11-17 00:00:00
Proceeding: EACL 2017 4
Tasks: Constituency Parsing, Dependency Parsing, Language Modeling, Language Modelling

Recurrent neural network grammars (RNNG) are a recently proposed
probabilistic generative modeling family for natural language. They show
state-of-the-art language modeling and parsing performance. We investigate what
information they learn, from a linguistic perspective, through various
ablations to the model and the data, and by augmenting the model with an
attention mechanism (GA-RNNG) to enable closer inspection. We find that
explicit modeling of composition is crucial for achieving the best performance.
Through the attention mechanism, we find that headedness plays a central role
in phrasal representation (with the model's latent attention largely agreeing
with predictions made by hand-crafted head rules, albeit with some important
differences). By training grammars without nonterminal labels, we find that
phrasal representations depend minimally on nonterminals, providing support for
the endocentricity hypothesis.
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images
Authors: Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz
Date: 2016-12-20 00:00:00
Tasks: Attribute, General Classification

Classifying the various shapes and attributes of a glioma cell nucleus is
crucial for diagnosis and understanding the disease. We investigate automated
classification of glioma nuclear shapes and visual attributes using
Convolutional Neural Networks (CNNs) on pathology images of automatically
segmented nuclei. We propose three methods that improve the performance of a
previously-developed semi-supervised CNN. First, we propose a method that
allows the CNN to focus on the most important part of an image- the image's
center containing the nucleus. Second, we inject (concatenate) pre-extracted
VGG features into an intermediate layer of our Semi-Supervised CNN so that
during training, the CNN can learn a set of complementary features. Third, we
separate the losses of the two groups of target classes (nuclear shapes and
attributes) into a single-label loss and a multi-label loss so that the prior
knowledge of inter-label exclusiveness can be incorporated. On a dataset of
2078 images, the proposed methods combined reduce the error rate of attribute
and shape classification by 21.54% and 15.07% respectively compared to the
existing state-of-the-art method on the same dataset.
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Authors: Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, Cesar Laurent Yoshua Bengio, Aaron Courville
Date: 2017-01-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, speech-recognition, Speech Recognition

Convolutional Neural Networks (CNNs) are effective models for reducing
spectral variations and modeling spectral correlations in acoustic features for
automatic speech recognition (ASR). Hybrid speech recognition systems
incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models
(HMMs/GMMs) have achieved the state-of-the-art in various benchmarks.
Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural
Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it
feasible to train an end-to-end speech recognition system instead of hybrid
settings. However, RNNs are computationally expensive and sometimes difficult
to train. In this paper, inspired by the advantages of both CNNs and the CTC
approach, we propose an end-to-end speech framework for sequence labeling, by
combining hierarchical CNNs with CTC directly without recurrent connections. By
evaluating the approach on the TIMIT phoneme recognition task, we show that the
proposed model is not only computationally efficient, but also competitive with
the existing baseline systems. Moreover, we argue that CNNs have the capability
to model temporal correlations with appropriate context information.
When is multitask learning effective? Semantic sequence prediction under varying data conditions
Authors: Héctor Martínez Alonso, Barbara Plank
Date: 2016-12-07 00:00:00
Proceeding: EACL 2017 4

Multitask learning has been applied successfully to a range of tasks, mostly
morphosyntactic. However, little is known on when MTL works and whether there
are data characteristics that help to determine its success. In this paper we
evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine
different auxiliary tasks, amongst which a novel setup, and correlate their
impact to data-dependent conditions. Our results show that MTL is not always
effective, significant improvements are obtained only for 1 out of 5 tasks.
When successful, auxiliary tasks with compact and more uniform label
distributions are preferable.
CNN Architectures for Large-Scale Audio Classification
Authors: Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, Kevin Wilson
Date: 2016-09-29 00:00:00
Tasks: Audio Classification, Event Detection, General Classification, image-classification

Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We
investigate varying the size of both training set and label vocabulary, finding
that analogs of the CNNs used in image classification do well on our audio
classification task, and larger training and label sets help up to a point. A
model using embeddings from these classifiers does much better than raw
features on the Audio Set [5] Acoustic Event Detection (AED) classification
task.
A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems
Authors: Arthur Kramer, Anand Subramanian
Date: 2015-09-08 00:00:00
Tasks: Scheduling

This work proposes a unified heuristic algorithm for a large class of
earliness-tardiness (E-T) scheduling problems. We consider single/parallel
machine E-T problems that may or may not consider some additional features such
as idle time, setup times and release dates. In addition, we also consider
those problems whose objective is to minimize either the total (average)
weighted completion time or the total (average) weighted flow time, which arise
as particular cases when the due dates of all jobs are either set to zero or to
their associated release dates, respectively. The developed local search based
metaheuristic framework is quite simple, but at the same time relies on
sophisticated procedures for efficiently performing local search according to
the characteristics of the problem. We present efficient move evaluation
approaches for some parallel machine problems that generalize the existing ones
for single machine problems. The algorithm was tested in hundreds of instances
of several E-T problems and particular cases. The results obtained show that
our unified heuristic is capable of producing high quality solutions when
compared to the best ones available in the literature that were obtained by
specific methods. Moreover, we provide an extensive annotated bibliography on
the problems related to those considered in this work, where we not only
indicate the approach(es) used in each publication, but we also point out the
characteristics of the problem(s) considered. Beyond that, we classify the
existing methods in different categories so as to have a better idea of the
popularity of each type of solution procedure.
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
Authors: Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo
Date: 2017-01-10 00:00:00
Tasks: Form, Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

It's useful to automatically transform an image from its original form to
some synthetic form (style, partial contents, etc.), while keeping the original
structure or semantics. We define this requirement as the "image-to-image
translation" problem, and propose a general approach to achieve it, based on
deep convolutional and conditional generative adversarial networks (GANs),
which has gained a phenomenal success to learn mapping images from noise input
since 2014. In this work, we develop a two step (unsupervised) learning method
to translate images between different domains by using unlabeled images without
specifying any correspondence between them, so that to avoid the cost of
acquiring labeled data. Compared with prior works, we demonstrated the capacity
of generality in our model, by which variance of translations can be conduct by
a single type of model. Such capability is desirable in applications like
bidirectional translation
Noise Mitigation for Neural Entity Typing and Relation Extraction
Authors: Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze
Date: 2016-12-22 00:00:00
Proceeding: EACL 2017 4
Tasks: Entity Typing, Multi-Label Learning, Relation, Relation Extraction, Vocal Bursts Type Prediction

In this paper, we address two different types of noise in information
extraction models: noise from distant supervision and noise from pipeline input
features. Our target tasks are entity typing and relation extraction. For the
first noise type, we introduce multi-instance multi-label learning algorithms
using neural network models, and apply them to fine-grained entity typing for
the first time. This gives our models comparable performance with the
state-of-the-art supervised approach which uses global embeddings of entities.
For the second noise type, we propose ways to improve the integration of noisy
entity type predictions into relation extraction. Our experiments show that
probabilistic predictions are more robust than discrete predictions and that
joint training of the two tasks performs best.
Exploring Different Dimensions of Attention for Uncertainty Detection
Authors: Heike Adel, Hinrich Schütze
Date: 2016-12-20 00:00:00
Proceeding: EACL 2017 4

Neural networks with attention have proven effective for many natural
language processing tasks. In this paper, we develop attention mechanisms for
uncertainty detection. In particular, we generalize standardly used attention
mechanisms by introducing external attention and sequence-preserving attention.
These novel architectures differ from standard approaches in that they use
external resources to compute attention weights and preserve sequence
information. We compare them to other configurations along different dimensions
of attention. Our novel architectures set the new state of the art on a
Wikipedia benchmark dataset and perform similar to the state-of-the-art model
on a biomedical benchmark which uses a large set of linguistic features.
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
Authors: Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schlüter, Hermann Ney
Date: 2016-08-02 00:00:00

In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academic research purposes and can be used as a framework or as a
standalone tool which supports a flexible configuration. The software allows to
train state-of-the-art deep bidirectional long short-term memory (LSTM) models
on both one dimensional data like speech or two dimensional data like
handwritten text and was used to develop successful submission systems in
several evaluation campaigns.
IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions
Authors: Daniel Meana-Llorián, Cristian González García, B. Cristina Pelayo G-Bustelo, Juan Manuel Cueva Lovelle, Nestor Garcia-Fernandez
Date: 2017-01-10 00:00:00

The Internet of Things is arriving to our homes or cities through fields
already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of
environmental conditions of cities can help to adapt the indoor locations of
the cities in order to be more comfortable for people who stay there. A way to
improve the indoor conditions is an efficient temperature control, however, it
depends on many factors like the different combinations of outdoor temperature
and humidity. Therefore, adjusting the indoor temperature is not setting a
value according to other value. There are many more factors to take into
consideration, hence the traditional logic based in binary states cannot be
used. Many problems cannot be solved with a set of binary solutions and we need
a new way of development. Fuzzy logic is able to interpret many states, more
than two states, giving to computers the capacity to react in a similar way to
people. In this paper we will propose a new approach to control the temperature
using the Internet of Things together its platforms and fuzzy logic regarding
not only the indoor temperature but also the outdoor temperature and humidity
in order to save energy and to set a more comfortable environment for their
users. Finally, we will conclude that the fuzzy approach allows us to achieve
an energy saving around 40% and thus, save money.
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
Authors: Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li
Date: 2017-01-10 00:00:00
Tasks: Management

Forecasting the flow of crowds is of great importance to traffic management
and public safety, and very challenging as it is affected by many complex
factors, including spatial dependencies (nearby and distant), temporal
dependencies (closeness, period, trend), and external conditions (e.g., weather
and events). We propose a deep-learning-based approach, called ST-ResNet, to
collectively forecast two types of crowd flows (i.e. inflow and outflow) in
each and every region of a city. We design an end-to-end structure of ST-ResNet
based on unique properties of spatio-temporal data. More specifically, we
employ the residual neural network framework to model the temporal closeness,
period, and trend properties of crowd traffic. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We have developed a real-time
system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd
flow monitoring and forecasting in Guiyang City of China. In addition, we
present an extensive experimental evaluation using two types of crowd flows in
Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known
baselines.