pwc-archive/papers-with-abstracts huggingface dataset:
Land Use Classification using Convolutional Neural Networks Applied to Ground-Level Images
Authors: Yi Zhu, Shawn Newsam
Date: 2016-09-21 00:00:00
Tasks: General Classification

Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.
Minimally Supervised Written-to-Spoken Text Normalization
Authors: Ke Wu, Kyle Gorman, Richard Sproat
Date: 2016-09-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Text Normalization, text-to-speech, Text to Speech

In speech-applications such as text-to-speech (TTS) or automatic speech
recognition (ASR), \emph{text normalization} refers to the task of converting
from a \emph{written} representation into a representation of how the text is
to be \emph{spoken}. In all real-world speech applications, the text
normalization engine is developed---in large part---by hand. For example, a
hand-built grammar may be used to enumerate the possible ways of saying a given
token in a given language, and a statistical model used to select the most
appropriate pronunciation in context. In this study we examine the tradeoffs
associated with using more or less language-specific domain knowledge in a text
normalization engine. In the most data-rich scenario, we have access to a
carefully constructed hand-built normalization grammar that for any given token
will produce a set of all possible verbalizations for that token. We also
assume a corpus of aligned written-spoken utterances, from which we can train a
ranking model that selects the appropriate verbalization for the given context.
As a substitute for the carefully constructed grammar, we also consider a
scenario with a language-universal normalization \emph{covering grammar}, where
the developer merely needs to provide a set of lexical items particular to the
language. As a substitute for the aligned corpus, we also consider a scenario
where one only has the spoken side, and the corresponding written side is
"hallucinated" by composing the spoken side with the inverted normalization
grammar. We investigate the accuracy of a text normalization engine under each
of these scenarios. We report the results of experiments on English and
Russian.
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Authors: Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
Date: 2016-09-21 00:00:00
Tasks: Image Captioning, Sentence, Translation

Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. Finally, given the recent
surge of interest in this task, a competition was organized in 2015 using the
newly released COCO dataset. We describe and analyze the various improvements
we applied to our own baseline and show the resulting performance in the
competition, which we won ex-aequo with a team from Microsoft Research, and
provide an open source implementation in TensorFlow.
Length bias in Encoder Decoder Models and a Case for Global Conditioning
Authors: Pavel Sountsov, Sunita Sarawagi
Date: 2016-06-10 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Decoder

Encoder-decoder networks are popular for modeling sequences probabilistically
in many applications. These models use the power of the Long Short-Term Memory
(LSTM) architecture to capture the full dependence among variables, unlike
earlier models like CRFs that typically assumed conditional independence among
non-adjacent variables. However in practice encoder-decoder models exhibit a
bias towards short sequences that surprisingly gets worse with increasing beam
size.
  In this paper we show that such phenomenon is due to a discrepancy between
the full sequence margin and the per-element margin enforced by the locally
conditioned training objective of a encoder-decoder model. The discrepancy more
adversely impacts long sequences, explaining the bias towards predicting short
sequences.
  For the case where the predicted sequences come from a closed set, we show
that a globally conditioned model alleviates the above problems of
encoder-decoder models. From a practical point of view, our proposed model also
eliminates the need for a beam-search during inference, which reduces to an
efficient dot-product based search in a vector-space.
Improving analytical tomographic reconstructions through consistency conditions
Authors: Filippo Arcadu, Jakob Vogel, Marco Stampanoni, Federica Marone
Date: 2016-09-21 00:00:00
Tasks: Tomographic Reconstructions

This work introduces and characterizes a fast parameterless filter based on
the Helgason-Ludwig consistency conditions, used to improve the accuracy of
analytical reconstructions of tomographic undersampled datasets. The filter,
acting in the Radon domain, extrapolates intermediate projections between those
existing. The resulting sinogram, doubled in views, is then reconstructed by a
standard analytical method. Experiments with simulated data prove that the
peak-signal-to-noise ratio of the results computed by filtered backprojection
is improved up to 5-6 dB, if the filter is used prior to reconstruction.
Image Denoising via Multi-scale Nonlinear Diffusion Models
Authors: Wensen Feng, Peng Qiao, Xuanyang Xi, Yunjin Chen
Date: 2016-09-21 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising

Image denoising is a fundamental operation in image processing and holds
considerable practical importance for various real-world applications. Arguably
several thousands of papers are dedicated to image denoising. In the past
decade, sate-of-the-art denoising algorithm have been clearly dominated by
non-local patch-based methods, which explicitly exploit patch self-similarity
within image. However, in recent two years, discriminatively trained local
approaches have started to outperform previous non-local models and have been
attracting increasing attentions due to the additional advantage of
computational efficiency. Successful approaches include cascade of shrinkage
fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two
methods are built on filter response of linear filters of small size using feed
forward architectures. Due to the locality inherent in local approaches, the
CSF and TNRD model become less effective when noise level is high and
consequently introduces some noise artifacts. In order to overcome this
problem, in this paper we introduce a multi-scale strategy. To be specific, we
build on our newly-developed TNRD model, adopting the multi-scale pyramid image
representation to devise a multi-scale nonlinear diffusion process. As
expected, all the parameters in the proposed multi-scale diffusion model,
including the filters and the influence functions across scales, are learned
from training data through a loss based approach. Numerical results on Gaussian
and Poisson denoising substantiate that the exploited multi-scale strategy can
successfully boost the performance of the original TNRD model with single
scale. As a consequence, the resulting multi-scale diffusion models can
significantly suppress the typical incorrect features for those noisy images
with heavy noise.
Autoregressive Moving Average Graph Filtering
Authors: Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus
Date: 2016-02-14 00:00:00
Tasks: Denoising, Philosophy

One of the cornerstones of the field of signal processing on graphs are graph
filters, direct analogues of classical filters, but intended for signals
defined on graphs. This work brings forth new insights on the distributed graph
filtering problem. We design a family of autoregressive moving average (ARMA)
recursions, which (i) are able to approximate any desired graph frequency
response, and (ii) give exact solutions for tasks such as graph signal
denoising and interpolation. The design philosophy, which allows us to design
the ARMA coefficients independently from the underlying graph, renders the ARMA
graph filters suitable in static and, particularly, time-varying settings. The
latter occur when the graph signal and/or graph are changing over time. We show
that in case of a time-varying graph signal our approach extends naturally to a
two-dimensional filter, operating concurrently in the graph and regular time
domains. We also derive sufficient conditions for filter stability when the
graph and signal are time-varying. The analytical and numerical results
presented in this paper illustrate that ARMA graph filters are practically
appealing for static and time-varying settings, as predicted by theoretical
derivations.
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition
Authors: Marcello Pelillo, Ismail Elezi, Marco Fiorucci
Date: 2016-09-21 00:00:00
Tasks: LEMMA

Introduced in the mid-1970's as an intermediate step in proving a
long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity
lemma has emerged over time as a fundamental tool in different branches of
graph theory, combinatorics and theoretical computer science. Roughly, it
states that every graph can be approximated by the union of a small number of
random-like bipartite graphs called regular pairs. In other words, the result
provides us a way to obtain a good description of a large graph using a small
amount of data, and can be regarded as a manifestation of the all-pervading
dichotomy between structure and randomness. In this paper we will provide an
overview of the regularity lemma and its algorithmic aspects, and will discuss
its relevance in the context of pattern recognition research.
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
Authors: Kar Wai Lim, Wray Buntine
Date: 2016-09-21 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Aspect-based opinion mining is widely applied to review data to aggregate or
summarize opinions of a product, and the current state-of-the-art is achieved
with Latent Dirichlet Allocation (LDA)-based model. Although social media data
like tweets are laden with opinions, their "dirty" nature (as natural language)
has discouraged researchers from applying LDA-based opinion model for product
review mining. Tweets are often informal, unstructured and lacking labeled data
such as categories and ratings, making it challenging for product opinion
mining. In this paper, we propose an LDA-based opinion model named Twitter
Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM
leverages hashtags, mentions, emoticons and strong sentiment words that are
present in tweets in its discovery process. It improves opinion prediction by
modeling the target-opinion interaction directly, thus discovering target
specific opinion words, neglected in existing approaches. Moreover, we propose
a new formulation of incorporating sentiment prior information into a topic
model, by utilizing an existing public sentiment lexicon. This is novel in that
it learns and updates with the data. We conduct experiments on 9 million tweets
on electronic products, and demonstrate the improved performance of TOTM in
both quantitative evaluations and qualitative analysis. We show that
aspect-based opinion analysis on massive volume of tweets provides useful
opinions on products.
Semi-supervised knowledge extraction for detection of drugs and their effects
Authors: Fabio Del Vigna, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari, Maurizio Tesconi
Date: 2016-09-21 00:00:00

New Psychoactive Substances (NPS) are drugs that lay in a grey area of
legislation, since they are not internationally and officially banned, possibly
leading to their not prosecutable trade. The exacerbation of the phenomenon is
that NPS can be easily sold and bought online. Here, we consider large corpora
of textual posts, published on online forums specialized on drug discussions,
plus a small set of known substances and associated effects, which we call
seeds. We propose a semi-supervised approach to knowledge extraction, applied
to the detection of drugs (comprising NPS) and effects from the corpora under
investigation. Based on the very small set of initial seeds, the work
highlights how a contrastive approach and context deduction are effective in
detecting substances and effects from the corpora. Our promising results, which
feature a F1 score close to 0.9, pave the way for shortening the detection time
of new psychoactive substances, once these are discussed and advertised on the
Internet.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Authors: Guillaume Lemaitre, Fernando Nogueira, Christos K. Aridas
Date: 2016-09-21 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Ensemble Learning

Imbalanced-learn is an open-source python toolbox aiming at providing a wide
range of methods to cope with the problem of imbalanced dataset frequently
encountered in machine learning and pattern recognition. The implemented
state-of-the-art methods can be categorized into 4 groups: (i) under-sampling,
(ii) over-sampling, (iii) combination of over- and under-sampling, and (iv)
ensemble learning methods. The proposed toolbox only depends on numpy, scipy,
and scikit-learn and is distributed under MIT license. Furthermore, it is fully
compatible with scikit-learn and is part of the scikit-learn-contrib supported
project. Documentation, unit tests as well as integration tests are provided to
ease usage and contribution. The toolbox is publicly available in GitHub:
https://github.com/scikit-learn-contrib/imbalanced-learn.
On Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering
Authors: Kajsa Møllersen, Subhra S. Dhar, Fred Godtliebsen
Date: 2016-09-21 00:00:00
Tasks: Clustering

Hybrid clustering combines partitional and hierarchical clustering for
computational effectiveness and versatility in cluster shape. In such
clustering, a dissimilarity measure plays a crucial role in the hierarchical
merging. The dissimilarity measure has great impact on the final clustering,
and data-independent properties are needed to choose the right dissimilarity
measure for the problem at hand. Properties for distance-based dissimilarity
measures have been studied for decades, but properties for density-based
dissimilarity measures have so far received little attention. Here, we propose
six data-independent properties to evaluate density-based dissimilarity
measures associated with hybrid clustering, regarding equality, orthogonality,
symmetry, outlier and noise observations, and light-tailed models for
heavy-tailed clusters. The significance of the properties is investigated, and
we study some well-known dissimilarity measures based on Shannon entropy,
misclassification rate, Bhattacharyya distance and Kullback-Leibler divergence
with respect to the proposed properties. As none of them satisfy all the
proposed properties, we introduce a new dissimilarity measure based on the
Kullback-Leibler information and show that it satisfies all proposed
properties. The effect of the proposed properties is also illustrated on
several real and simulated data sets.
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
Authors: Kar Wai Lim, Wray Buntine
Date: 2016-09-21 00:00:00
Tasks: Clustering

Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.
Weakly supervised spoken term discovery using cross-lingual side information
Authors: Sameer Bansal, Herman Kamper, Sharon Goldwater, Adam Lopez
Date: 2016-09-21 00:00:00

Recent work on unsupervised term discovery (UTD) aims to identify and cluster
repeated word-like units from audio alone. These systems are promising for some
very low-resource languages where transcribed audio is unavailable, or where no
written form of the language exists. However, in some cases it may still be
feasible (e.g., through crowdsourcing) to obtain (possibly noisy) text
translations of the audio. If so, this information could be used as a source of
side information to improve UTD. Here, we present a simple method for rescoring
the output of a UTD system using text translations, and test it on a corpus of
Spanish audio with English translations. We show that it greatly improves the
average precision of the results over a wide range of system configurations and
data preprocessing methods.
Wavelet-Based Segmentation on the Sphere
Authors: Xiaohao Cai, Christopher G. R. Wallis, Jennifer Y. H. Chan, Jason D. McEwen
Date: 2016-09-21 00:00:00
Tasks: Geophysics, Image Restoration, Segmentation, Super-Resolution

Segmentation, a useful/powerful technique in pattern recognition, is the process of identifying object outlines within images. There are a number of efficient algorithms for segmentation in Euclidean space that depend on the variational approach and partial differential equation modelling. Wavelets have been used successfully in various problems in image processing, including segmentation, inpainting, noise removal, super-resolution image restoration, and many others. Wavelets on the sphere have been developed to solve such problems for data defined on the sphere, which arise in numerous fields such as cosmology and geophysics. In this work, we propose a wavelet-based method to segment images on the sphere, accounting for the underlying geometry of spherical data. Our method is a direct extension of the tight-frame based segmentation method used to automatically identify tube-like structures such as blood vessels in medical imaging. It is compatible with any arbitrary type of wavelet frame defined on the sphere, such as axisymmetric wavelets, directional wavelets, curvelets, and hybrid wavelet constructions. Such an approach allows the desirable properties of wavelets to be naturally inherited in the segmentation process. In particular, directional wavelets and curvelets, which were designed to efficiently capture directional signal content, provide additional advantages in segmenting images containing prominent directional and curvilinear features. We present several numerical experiments, applying our wavelet-based segmentation method, as well as the common K-means method, on real-world spherical images. These experiments demonstrate the superiority of our method and show that it is capable of segmenting different kinds of spherical images, including those with prominent directional features. Moreover, our algorithm is efficient with convergence usually within a few iterations.
Document Image Coding and Clustering for Script Discrimination
Authors: Darko Brodic, Alessia Amelio, Zoran N. Milivojevic, Milena Jevtic
Date: 2016-09-21 00:00:00
Tasks: Clustering, Texture Classification

The paper introduces a new method for discrimination of documents given in
different scripts. The document is mapped into a uniformly coded text of
numerical values. It is derived from the position of the letters in the text
line, based on their typographical characteristics. Each code is considered as
a gray level. Accordingly, the coded text determines a 1-D image, on which
texture analysis by run-length statistics and local binary pattern is
performed. It defines feature vectors representing the script content of the
document. A modified clustering approach employed on document feature vector
groups documents written in the same script. Experimentation performed on two
custom oriented databases of historical documents in old Cyrillic, angular and
round Glagolitic as well as Antiqua and Fraktur scripts demonstrates the
superiority of the proposed method with respect to well-known methods in the
state-of-the-art.
One Sentence One Model for Neural Machine Translation
Authors: Xiao-Qing Li, Jiajun Zhang, Cheng-qing Zong
Date: 2016-09-21 00:00:00
Proceeding: LREC 2018 5
Tasks: Decoder, Machine Translation, model, NMT, Sentence, Translation

Neural machine translation (NMT) becomes a new state-of-the-art and achieves
promising translation results using a simple encoder-decoder neural network.
This neural network is trained once on the parallel corpus and the fixed
network is used to translate all the test sentences. We argue that the general
fixed network cannot best fit the specific test sentences. In this paper, we
propose the dynamic NMT which learns a general network as usual, and then
fine-tunes the network for each test sentence. The fine-tune work is done on a
small set of the bilingual training data that is obtained through similarity
search according to the test sentence. Extensive experiments demonstrate that
this method can significantly improve the translation performance, especially
when highly similar sentences are available.