pwc-archive/papers-with-abstracts huggingface dataset:
Neural Machine Translation by Jointly Learning to Align and Translate
Authors: Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
Date: 2014-09-01 00:00:00
Tasks: Bangla Spelling Error Correction, Decoder, Dialogue Generation, Machine Translation, Sentence, Translation

Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural machine translation often belong to a family of
encoder-decoders and consists of an encoder that encodes a source sentence into
a fixed-length vector from which a decoder generates a translation. In this
paper, we conjecture that the use of a fixed-length vector is a bottleneck in
improving the performance of this basic encoder-decoder architecture, and
propose to extend this by allowing a model to automatically (soft-)search for
parts of a source sentence that are relevant to predicting a target word,
without having to form these parts as a hard segment explicitly. With this new
approach, we achieve a translation performance comparable to the existing
state-of-the-art phrase-based system on the task of English-to-French
translation. Furthermore, qualitative analysis reveals that the
(soft-)alignments found by the model agree well with our intuition.
Development of a 3D tongue motion visualization platform based on ultrasound image sequences
Authors: Kele Xu, Yin Yang, Aurore Jaumard-Hakoun, Clemence Leboullenger, Gerard Dreyfus, Pierre Roussel, Maureen Stone, Bruce Denby
Date: 2016-05-19 00:00:00

This article describes the development of a platform designed to visualize
the 3D motion of the tongue using ultrasound image sequences. An overview of
the system design is given and promising results are presented. Compared to the
analysis of motion in 2D image sequences, such a system can provide additional
visual information and a quantitative description of the tongue 3D motion. The
platform can be useful in a variety of fields, such as speech production,
articulation training, etc.
Automatic Selection of the Optimal Local Feature Detector
Authors: Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman, Ales Leonardis, Klaus D. McDonald-Maier
Date: 2016-05-19 00:00:00

A large number of different feature detectors has been proposed so far. Any
existing approach presents strengths and weaknesses, which make a detector
optimal only for a limited range of applications. A tool capable of selecting
the optimal feature detector in relation to the operating conditions is
presented in this paper. The input images are quickly analyzed to determine
what type of image transformation is applied to them and at which amount.
Finally, the detector that is expected to obtain the highest repeatability
under such conditions, is chosen to extract features from the input images. The
efficiency and the good accuracy in determining the optimal feature detector
for any operating condition, make the proposed tool suitable to be utilized in
real visual applications. %A large number of different feature detectors has
been proposed so far. Any existing approach presents strengths and weaknesses,
which make a detector optimal only for a limited range of applications. A large
number of different local feature detectors have been proposed in the last few
years. However, each feature detector has its own strengths ad weaknesses that
limit its use to a specific range of applications. In this paper is presented a
tool capable of quickly analysing input images to determine which type and
amount of transformation is applied to them and then selecting the optimal
feature detector, which is expected to perform the best. The results show that
the performance and the fast execution time render the proposed tool suitable
for real-world vision applications.
Low-rank passthrough neural networks
Authors: Antonio Valerio Miceli Barone
Date: 2016-03-10 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling, Permuted-MNIST

Various common deep learning architectures, such as LSTMs, GRUs, Resnets and
Highway Networks, employ state passthrough connections that support training
with high feed-forward depth or recurrence over many time steps. These
"Passthrough Networks" architectures also enable the decoupling of the network
state size from the number of parameters of the network, a possibility has been
studied by \newcite{Sak2014} with their low-rank parametrization of the LSTM.
In this work we extend this line of research, proposing effective, low-rank and
low-rank plus diagonal matrix parametrizations for Passthrough Networks which
exploit this decoupling property, reducing the data complexity and memory
requirements of the network while preserving its memory capacity. This is
particularly beneficial in low-resource settings as it supports expressive
models with a compact parametrization less susceptible to overfitting. We
present competitive experimental results on several tasks, including language
modeling and a near state of the art result on sequential randomly-permuted
MNIST classification, a hard task on natural data.
Stereotyping and Bias in the Flickr30K Dataset
Authors: Emiel van Miltenburg
Date: 2016-05-19 00:00:00

An untested assumption behind the crowdsourced descriptions of the images in
the Flickr30K dataset (Young et al., 2014) is that they "focus only on the
information that can be obtained from the image alone" (Hodosh et al., 2013, p.
859). This paper presents some evidence against this assumption, and provides a
list of biases and unwarranted inferences that can be found in the Flickr30K
dataset. Finally, it considers methods to find examples of these, and discusses
how we should deal with stereotype-driven descriptions in future applications.
How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels
Authors: Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins
Date: 2014-11-01 00:00:00
Tasks: Multiple-choice

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism
to obtain labels for system development and evaluation. Successful deployment
of crowdsourcing at scale involves adjusting many variables, a very important
one being the number of workers needed per human intelligence task (HIT). We
consider the crowdsourcing task of learning the answer to simple
multiple-choice HITs, which are representative of many relevance experiments.
In order to provide statistically significant results, one often needs to ask
multiple workers to answer the same HIT. A stopping rule is an algorithm that,
given a HIT, decides for any given set of worker answers if the system should
stop and output an answer or iterate and ask one more worker. Knowing the
historic performance of a worker in the form of a quality score can be
beneficial in such a scenario. In this paper we investigate how to devise
better stopping rules given such quality scores. We also suggest adaptive
exploration as a promising approach for scalable and automatic creation of
ground truth. We conduct a data analysis on an industrial crowdsourcing
platform, and use the observations from this analysis to design new stopping
rules that use the workers' quality scores in a non-trivial manner. We then
perform a simulation based on a real-world workload, showing that our algorithm
performs better than the more naive approaches.
On a convergent off -policy temporal difference learning algorithm in on-line learning environment
Authors: Prasenjit Karmakar, Rajkumar Maity, Shalabh Bhatnagar
Date: 2016-05-19 00:00:00

In this paper we provide a rigorous convergence analysis of a "off"-policy
temporal difference learning algorithm with linear function approximation and
per time-step linear computational complexity in "online" learning environment.
The algorithm considered here is TDC with importance weighting introduced by
Maei et al. We support our theoretical results by providing suitable empirical
results for standard off-policy counterexamples.
One-shot Learning with Memory-Augmented Neural Networks
Authors: Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
Date: 2016-05-19 00:00:00
Tasks: One-Shot Learning

Despite recent breakthroughs in the applications of deep neural networks, one
setting that presents a persistent challenge is that of "one-shot learning."
Traditional gradient-based networks require a lot of data to learn, often
through extensive iterative training. When new data is encountered, the models
must inefficiently relearn their parameters to adequately incorporate the new
information without catastrophic interference. Architectures with augmented
memory capacities, such as Neural Turing Machines (NTMs), offer the ability to
quickly encode and retrieve new information, and hence can potentially obviate
the downsides of conventional models. Here, we demonstrate the ability of a
memory-augmented neural network to rapidly assimilate new data, and leverage
this data to make accurate predictions after only a few samples. We also
introduce a new method for accessing an external memory that focuses on memory
content, unlike previous methods that additionally use memory location-based
focusing mechanisms.
Hierarchical Clustering in Face Similarity Score Space
Authors: Jason Grant, Patrick Flynn
Date: 2016-05-19 00:00:00
Tasks: Clustering, Face Recognition

Similarity scores in face recognition represent the proximity between pairs
of images as computed by a matching algorithm. Given a large set of images and
the proximities between all pairs, a similarity score space is defined. Cluster
analysis was applied to the similarity score space to develop various
taxonomies. Given the number of subjects in the dataset, we used hierarchical
methods to aggregate images of the same subject. We also explored the hierarchy
above and below the subject level, including clusters that reflect gender and
ethnicity. Evidence supports the existence of clustering by race, gender,
subject, and illumination condition.
Philosophy in the Face of Artificial Intelligence
Authors: Vincent Conitzer
Date: 2016-05-19 00:00:00
Tasks: Philosophy

In this article, I discuss how the AI community views concerns about the
emergence of superintelligent AI and related philosophical issues.
AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization
Authors: Gerasimos Spanakis, Gerhard Weiss
Date: 2016-05-19 00:00:00
Tasks: Clustering

Self-Organizing Map (SOM) is a neural network model which is used to obtain a
topology-preserving mapping from the (usually high dimensional) input/feature
space to an output/map space of fewer dimensions (usually two or three in order
to facilitate visualization). Neurons in the output space are connected with
each other but this structure remains fixed throughout training and learning is
achieved through the updating of neuron reference vectors in feature space.
Despite the fact that growing variants of SOM overcome the fixed structure
limitation they increase computational cost and also do not allow the removal
of a neuron after its introduction. In this paper, a variant of SOM is proposed
called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates
a more flexible structure where neuron positions are dynamically altered during
training and on the other hand tackles the drawback of having a predefined grid
by allowing neuron addition and/or removal during training. Experiments using
multiple literature datasets show that the proposed method improves training
performance of SOM, leads to a better visualization of the input dataset and
provides a framework for determining the optimal number and structure of
neurons.
One model, two languages: training bilingual parsers with harmonized treebanks
Authors: David Vilares, Carlos Gómez-Rodríguez, Miguel A. Alonso
Date: 2015-07-30 00:00:00
Proceeding: ACL 2016 8
Tasks: Vocal Bursts Valence Prediction

We introduce an approach to train lexicalized parsers using bilingual corpora
obtained by merging harmonized treebanks of different languages, producing
parsers that can analyze sentences in either of the learned languages, or even
sentences that mix both. We test the approach on the Universal Dependency
Treebanks, training with MaltParser and MaltOptimizer. The results show that
these bilingual parsers are more than competitive, as most combinations not
only preserve accuracy, but some even achieve significant improvements over the
corresponding monolingual parsers. Preliminary experiments also show the
approach to be promising on texts with code-switching and when more languages
are added.
A Geometric Approach to Color Image Regularization
Authors: Freddie Åström, Christoph Schnörr
Date: 2016-05-19 00:00:00
Tasks: Deblurring, Denoising, Image Restoration

We present a new vectorial total variation method that addresses the problem
of color consistent image filtering. Our approach is inspired from the
double-opponent cell representation in the human visual cortex. Existing
methods of vectorial total variation regularizers have insufficient (or no)
coupling between the color channels and thus may introduce color artifacts. We
address this problem by introducing a novel coupling between the color channels
related to a pullback-metric from the opponent space to the data (RGB color)
space. Our energy is a non-convex, non-smooth higher-order vectorial total
variation approach and promotes color consistent image filtering via a coupling
term. For a convex variant, we show well-posedness and existence of a solution
in the space of vectorial bounded variation. For the higher-order scheme we
employ a half-quadratic strategy, which model the non-convex energy terms as
the infimum of a sequence of quadratic functions. In experiments, we elaborate
on traditional image restoration applications of inpainting, deblurring and
denoising. Regarding the latter, we demonstrate state of the art restoration
quality with respect to structure coherence and color consistency.
Associative Long Short-Term Memory
Authors: Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves
Date: 2016-02-09 00:00:00
Tasks: Memorization, Retrieval

We investigate a new method to augment recurrent neural networks with extra
memory without increasing the number of network parameters. The system has an
associative memory based on complex-valued vectors and is closely related to
Holographic Reduced Representations and Long Short-Term Memory networks.
Holographic Reduced Representations have limited capacity: as they store more
information, each retrieval becomes noisier due to interference. Our system in
contrast creates redundant copies of stored information, which enables
retrieval with reduced noise. Experiments demonstrate faster learning on
multiple memorization tasks.
Contour-based 3d tongue motion visualization using ultrasound image sequences
Authors: Kele Xu, Yin Yang, Clémence Leboullenger, Pierre Roussel, Bruce Denby
Date: 2016-05-19 00:00:00
Tasks: Silent Speech Recognition, speech-recognition, Speech Recognition

This article describes a contour-based 3D tongue deformation visualization
framework using B-mode ultrasound image sequences. A robust, automatic tracking
algorithm characterizes tongue motion via a contour, which is then used to
drive a generic 3D Finite Element Model (FEM). A novel contour-based 3D dynamic
modeling method is presented. Modal reduction and modal warping techniques are
applied to model the deformation of the tongue physically and efficiently. This
work can be helpful in a variety of fields, such as speech production, silent
speech recognition, articulation training, speech disorder study, etc.
Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality
Authors: Khadija Tijani, Stephane Ploix, Benjamin Haas, Julie Dugdale, Quoc Dung Ngo
Date: 2016-05-19 00:00:00

This paper proposes a new general approach based on Bayesian networks to
model the human behaviour. This approach represents human behaviour with
probabilistic cause-effect relations based on knowledge, but also with
conditional probabilities coming either from knowledge or deduced from
observations. This approach has been applied to the co-simulation of the CO2
concentration in an office coupled with human behaviour.
Interactive Debugging of Knowledge Bases
Authors: Patrick Rodler
Date: 2016-05-19 00:00:00

Many AI applications rely on knowledge about a relevant real-world domain
that is encoded by means of some logical knowledge base (KB). The most
essential benefit of logical KBs is the opportunity to perform automatic
reasoning to derive implicit knowledge or to answer complex queries about the
modeled domain. The feasibility of meaningful reasoning requires KBs to meet
some minimal quality criteria such as logical consistency. Without adequate
tool assistance, the task of resolving violated quality criteria in KBs can be
extremely tough even for domain experts, especially when the problematic KB
includes a large number of logical formulas or comprises complicated logical
formalisms.
  Published non-interactive debugging systems often cannot localize all
possible faults (incompleteness), suggest the deletion or modification of
unnecessarily large parts of the KB (non-minimality), return incorrect
solutions which lead to a repaired KB not satisfying the imposed quality
requirements (unsoundness) or suffer from poor scalability due to the inherent
complexity of the KB debugging problem. Even if a system is complete and sound
and considers only minimal solutions, there are generally exponentially many
solution candidates to select one from. However, any two repaired KBs obtained
from these candidates differ in their semantics in terms of entailments and
non-entailments. Selection of just any of these repaired KBs might result in
unexpected entailments, the loss of desired entailments or unwanted changes to
the KB.
  This work proposes complete, sound and optimal methods for the interactive
debugging of KBs that suggest the one (minimally invasive) error correction of
the faulty KB that yields a repaired KB with exactly the intended semantics.
Users, e.g. domain experts, are involved in the debugging process by answering
automatically generated queries about the intended domain.
Hierarchical Piecewise-Constant Super-regions
Authors: Imanol Luengo, Mark Basham, Andrew P. French
Date: 2016-05-19 00:00:00
Tasks: Denoising, Form, Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation, Superpixels

Recent applications in computer vision have come to heavily rely on
superpixel over-segmentation as a pre-processing step for higher level vision
tasks, such as object recognition, image labelling or image segmentation. Here
we present a new superpixel algorithm called Hierarchical Piecewise-Constant
Super-regions (HPCS), which not only obtains superpixels comparable to the
state-of-the-art, but can also be applied hierarchically to form what we call
n-th order super-regions. In essence, a Markov Random Field (MRF)-based
anisotropic denoising formulation over the quantized feature space is adopted
to form piecewise-constant image regions, which are then combined with a
graph-based split & merge post-processing step to form superpixels. The graph
and quantized feature based formulation of the problem allows us to generalize
it hierarchically to preserve boundary adherence with fewer superpixels.
Experimental results show that, despite the simplicity of our framework, it is
able to provide high quality superpixels, and to hierarchically apply them to
form layers of over-segmentation, each with a decreasing number of superpixels,
while maintaining the same desired properties (such as adherence to strong
image edges). The algorithm is also memory efficient and has a low
computational cost.
Matching Handwritten Document Images
Authors: Praveen Krishnan, C. V. Jawahar
Date: 2016-05-19 00:00:00

We address the problem of predicting similarity between a pair of handwritten
document images written by different individuals. This has applications related
to matching and mining in image collections containing handwritten content. A
similarity score is computed by detecting patterns of text re-usages between
document images irrespective of the minor variations in word morphology, word
ordering, layout and paraphrasing of the content. Our method does not depend on
an accurate segmentation of words and lines. We formulate the document matching
problem as a structured comparison of the word distributions across two
document images. To match two word images, we propose a convolutional neural
network (CNN) based feature descriptor. Performance of this representation
surpasses the state-of-the-art on handwritten word spotting. Finally, we
demonstrate the applicability of our method on a practical problem of matching
handwritten assignments.
Tongue contour extraction from ultrasound images based on deep neural network
Authors: Aurore Jaumard-Hakoun, Kele Xu, Pierre Roussel-Ragot, Gérard Dreyfus, Bruce Denby
Date: 2016-05-19 00:00:00

Studying tongue motion during speech using ultrasound is a standard
procedure, but automatic ultrasound image labelling remains a challenge, as
standard tongue shape extraction methods typically require human intervention.
This article presents a method based on deep neural networks to automatically
extract tongue contour from ultrasound images on a speech dataset. We use a
deep autoencoder trained to learn the relationship between an image and its
related contour, so that the model is able to automatically reconstruct
contours from the ultrasound image alone. In this paper, we use an automatic
labelling algorithm instead of time-consuming hand-labelling during the
training process, and estimate the performances of both automatic labelling and
contour extraction as compared to hand-labelling. Observed results show quality
scores comparable to the state of the art.
Automatic TM Cleaning through MT and POS Tagging: Autodesk's Submission to the NLP4TM 2016 Shared Task
Authors: Alena Zwahlen, Olivier Carnal, Samuel Läubli
Date: 2016-05-19 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, General Classification, Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging, Translation

We describe a machine learning based method to identify incorrect entries in
translation memories. It extends previous work by Barbu (2015) through
incorporating recall-based machine translation and part-of-speech-tagging
features. Our system ranked first in the Binary Classification (II) task for
two out of three language pairs: English-Italian and English-Spanish.
COCO: The Experimental Procedure
Authors: Nikolaus Hansen, Tea Tusar, Olaf Mersmann, Anne Auger, Dimo Brockhoff
Date: 2016-03-29 00:00:00
Tasks: Benchmarking

We present a budget-free experimental setup and procedure for benchmarking
numericaloptimization algorithms in a black-box scenario. This procedure can be
applied with the COCO benchmarking platform. We describe initialization of and
input to the algorithm and touch upon therelevance of termination and restarts.