pwc-archive/papers-with-abstracts huggingface dataset:
Deep Generative Adversarial Compression Artifact Removal
Authors: Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, Alberto del Bimbo
Date: 2017-04-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: object-detection, Object Detection, SSIM

Compression artifacts arise in images whenever a lossy compression algorithm
is applied. These artifacts eliminate details present in the original image, or
add noise and small structures; because of these effects they make images less
pleasant for the human eye, and may also lead to decreased performance of
computer vision algorithms such as object detectors. To eliminate such
artifacts, when decompressing an image, it is required to recover the original
image from a disturbed version. To this end, we present a feed-forward fully
convolutional residual network model trained using a generative adversarial
framework. To provide a baseline, we show that our model can be also trained
optimizing the Structural Similarity (SSIM), which is a better loss with
respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce
images with more photorealistic details than MSE or SSIM based networks.
Moreover we show that our approach can be used as a pre-processing step for
object detection in case images are degraded by compression to a point that
state-of-the art detectors fail. In this task, our GAN method obtains better
performance than MSE or SSIM trained networks.
Named Entity Sequence Classification
Authors: Mahdi Namazifar
Date: 2017-12-06 00:00:00
Tasks: Binary Classification, Classification, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) aims at locating and classifying named
entities in text. In some use cases of NER, including cases where detected
named entities are used in creating content recommendations, it is crucial to
have a reliable confidence level for the detected named entities. In this work
we study the problem of finding confidence levels for detected named entities.
We refer to this problem as Named Entity Sequence Classification (NESC). We
frame NESC as a binary classification problem and we use NER as well as
recurrent neural networks to find the probability of candidate named entity is
a real named entity. We apply this approach to Tweet texts and we show how we
could find named entities with high confidence levels from Tweets.
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
Authors: Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
Date: 2016-10-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: image-classification, Image Classification, Quantization, speech-recognition, Speech Recognition

Parallel implementations of stochastic gradient descent (SGD) have received
significant research attention, thanks to excellent scalability properties of
this algorithm, and to its efficiency in the context of training deep neural
networks. A fundamental barrier for parallelizing large-scale SGD is the fact
that the cost of communicating the gradient updates between nodes can be very
large. Consequently, lossy compression heuristics have been proposed, by which
nodes only communicate quantized gradients. Although effective in practice,
these heuristics do not always provably converge, and it is not clear whether
they are optimal.
  In this paper, we propose Quantized SGD (QSGD), a family of compression
schemes which allow the compression of gradient updates at each node, while
guaranteeing convergence under standard assumptions. QSGD allows the user to
trade off compression and convergence time: it can communicate a sublinear
number of bits per iteration in the model dimension, and can achieve
asymptotically optimal communication cost. We complement our theoretical
results with empirical data, showing that QSGD can significantly reduce
communication cost, while being competitive with standard uncompressed
techniques on a variety of real tasks.
  In particular, experiments show that gradient quantization applied to
training of deep neural networks for image classification and automated speech
recognition can lead to significant reductions in communication cost, and
end-to-end training time. For instance, on 16 GPUs, we are able to train a
ResNet-152 network on ImageNet 1.8x faster to full accuracy. Of note, we show
that there exist generic parameter settings under which all known network
architectures preserve or slightly improve their full accuracy when using
quantization.
Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization
Authors: Victor Veitch, Ekansh Sharma, Zacharie Naulet, Daniel M. Roy
Date: 2017-12-06 00:00:00
Tasks: Variational Inference

A variety of machine learning tasks---e.g., matrix factorization, topic
modelling, and feature allocation---can be viewed as learning the parameters of
a probability distribution over bipartite graphs. Recently, a new class of
models for networks, the sparse exchangeable graphs, have been introduced to
resolve some important pathologies of traditional approaches to statistical
network modelling; most notably, the inability to model sparsity (in the
asymptotic sense). The present paper explains some practical insights arising
from this work. We first show how to check if sparsity is relevant for
modelling a given (fixed size) dataset by using network subsampling to identify
a simple signature of sparsity. We discuss the implications of the (sparse)
exchangeable subsampling theory for test-train dataset splitting; we argue
common approaches can lead to biased results, and we propose a principled
alternative. Finally, we study sparse exchangeable Poisson matrix factorization
as a worked example. In particular, we show how to adapt mean field variational
inference to the sparse exchangeable setting, allowing us to scale inference to
huge datasets.
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data
Authors: Jamie R. Nuñez, Christopher R. Anderton, Ryan S. Renslow
Date: 2017-11-29 00:00:00

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVDoptimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with redgreen colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.
From Lifestyle Vlogs to Everyday Interactions
Authors: David F. Fouhey, Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik
Date: 2017-12-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Diversity, Future prediction

A major stumbling block to progress in understanding basic human
interactions, such as getting out of bed or opening a refrigerator, is lack of
good training data. Most past efforts have gathered this data explicitly:
starting with a laundry list of action labels, and then querying search engines
for videos tagged with each label. In this work, we do the reverse and search
implicitly: we start with a large collection of interaction-rich video data and
then annotate and analyze it. We use Internet Lifestyle Vlogs as the source of
surprisingly large and diverse interaction data. We show that by collecting the
data first, we are able to achieve greater scale and far greater diversity in
terms of actions and actors. Additionally, our data exposes biases built into
common explicitly gathered data. We make sense of our data by analyzing the
central component of interaction -- hands. We benchmark two tasks: identifying
semantic object contact at the video level and non-semantic contact state at
the frame level. We additionally demonstrate future prediction of hands.
An Efficient Evolutionary Based Method For Image Segmentation
Authors: Roohollah Aslanzadeh, Kazem Qazanfari, Mohammad Rahmati
Date: 2017-09-13 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The goal of this paper is to present a new efficient image segmentation
method based on evolutionary computation which is a model inspired from human
behavior. Based on this model, a four layer process for image segmentation is
proposed using the split/merge approach. In the first layer, an image is split
into numerous regions using the watershed algorithm. In the second layer, a
co-evolutionary process is applied to form centers of finals segments by
merging similar primary regions. In the third layer, a meta-heuristic process
uses two operators to connect the residual regions to their corresponding
determined centers. In the final layer, an evolutionary algorithm is used to
combine the resulted similar and neighbor regions. Different layers of the
algorithm are totally independent, therefore for certain applications a
specific layer can be changed without constraint of changing other layers. Some
properties of this algorithm like the flexibility of its method, the ability to
use different feature vectors for segmentation (grayscale, color, texture,
etc), the ability to control uniformity and the number of final segments using
free parameters and also maintaining small regions, makes it possible to apply
the algorithm to different applications. Moreover, the independence of each
region from other regions in the second layer, and the independence of centers
in the third layer, makes parallel implementation possible. As a result the
algorithm speed will increase. The presented algorithm was tested on a standard
dataset (BSDS 300) of images, and the region boundaries were compared with
different people segmentation contours. Results show the efficiency of the
algorithm and its improvement to similar methods. As an instance, in 70% of
tested images, results are better than ACT algorithm, besides in 100% of tested
images, we had better results in comparison with VSP algorithm.
Real-time On-Demand Crowd-powered Entity Extraction
Authors: Ting-Hao 'Kenneth' Huang, Yun-Nung Chen, Jeffrey P. Bigham
Date: 2017-04-12 00:00:00
Tasks: Entity Extraction using GAN

Output-agreement mechanisms such as ESP Game have been widely used in human
computation to obtain reliable human-generated labels. In this paper, we argue
that a "time-limited" output-agreement mechanism can be used to create a fast
and robust crowd-powered component in interactive systems, particularly
dialogue systems, to extract key information from user utterances on the fly.
Our experiments on Amazon Mechanical Turk using the Airline Travel Information
System (ATIS) dataset showed that the proposed approach achieves high-quality
results with an average response time shorter than 9 seconds.
Stretching Domain Adaptation: How far is too far?
Authors: Yunhan Zhao, Haider Ali, Rene Vidal
Date: 2017-12-06 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

While deep learning has led to significant advances in visual recognition
over the past few years, such advances often require a lot of annotated data.
Unsupervised domain adaptation has emerged as an alternative approach that does
not require as much annotated data, prior evaluations of domain adaptation
approaches have been limited to relatively similar datasets, e.g source and
target domains are samples captured by different cameras. A new data suite is
proposed that comprehensively evaluates cross-modality domain adaptation
problems. This work pushes the limit of unsupervised domain adaptation through
an in-depth evaluation of several state of the art methods on benchmark
datasets and the new dataset suite. We also propose a new domain adaptation
network called "Deep MagNet" that effectively transfers knowledge for
cross-modality domain adaptation problems. Deep Magnet achieves state of the
art performance on two benchmark datasets. More importantly, the proposed
method shows consistent improvements in performance on the newly proposed
dataset suite.
Kernel clustering: density biases and solutions
Authors: Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov
Date: 2017-05-16 00:00:00
Tasks: Clustering

Kernel methods are popular in clustering due to their generality and
discriminating power. However, we show that many kernel clustering criteria
have density biases theoretically explaining some practically significant
artifacts empirically observed in the past. For example, we provide conditions
and formally prove the density mode isolation bias in kernel K-means for a
common class of kernels. We call it Breiman's bias due to its similarity to the
histogram mode isolation previously discovered by Breiman in decision tree
learning with Gini impurity. We also extend our analysis to other popular
kernel clustering methods, e.g. average/normalized cut or dominant sets, where
density biases can take different forms. For example, splitting isolated points
by cut-based criteria is essentially the sparsest subset bias, which is the
opposite of the density mode bias. Our findings suggest that a principled
solution for density biases in kernel clustering should directly address data
inhomogeneity. We show that density equalization can be implicitly achieved
using either locally adaptive weights or locally adaptive kernels. Moreover,
density equalization makes many popular kernel clustering objectives
equivalent. Our synthetic and real data experiments illustrate density biases
and proposed solutions. We anticipate that theoretical understanding of kernel
clustering limitations and their principled solutions will be important for a
broad spectrum of data analysis applications across the disciplines.
Attention based convolutional neural network for predicting RNA-protein binding sites
Authors: Xiaoyong Pan, Junchi Yan
Date: 2017-12-06 00:00:00

RNA-binding proteins (RBPs) play crucial roles in many biological processes,
e.g. gene regulation. Computational identification of RBP binding sites on RNAs
are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence
motifs. Thus, fast locating those motifs on RNA sequences is crucial and
time-efficient for determining whether the RNAs interact with the RBPs or not.
In this study, we present an attention based convolutional neural network,
iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first
encode RNA sequences into one-hot encoding. Next, we design a deep learning
model with a convolutional neural network (CNN) and an attention mechanism,
which automatically search for important positions, e.g. binding motifs, to
learn discriminant high-level features for predicting RBP binding sites. We
evaluate iDeepA on publicly gold-standard RBP binding sites derived from
CLIP-seq data. The results demonstrate iDeepA achieves comparable performance
with other state-of-the-art methods.
An innovative solution for breast cancer textual big data analysis
Authors: Nicolas Thiebaut, Antoine Simoulin, Karl Neuberger, Issam Ibnouhsein, Nicolas Bousquet, Nathalie Reix, Sébastien Molière, Carole Mathelin
Date: 2017-12-06 00:00:00
Tasks: Epidemiology, Prognosis, Retrieval

The digitalization of stored information in hospitals now allows for the
exploitation of medical data in text format, as electronic health records
(EHRs), initially gathered for other purposes than epidemiology. Manual search
and analysis operations on such data become tedious. In recent years, the use
of natural language processing (NLP) tools was highlighted to automatize the
extraction of information contained in EHRs, structure it and perform
statistical analysis on this structured information. The main difficulties with
the existing approaches is the requirement of synonyms or ontology
dictionaries, that are mostly available in English only and do not include
local or custom notations. In this work, a team composed of oncologists as
domain experts and data scientists develop a custom NLP-based system to process
and structure textual clinical reports of patients suffering from breast
cancer. The tool relies on the combination of standard text mining techniques
and an advanced synonym detection method. It allows for a global analysis by
retrieval of indicators such as medical history, tumor characteristics,
therapeutic responses, recurrences and prognosis. The versatility of the method
allows to obtain easily new indicators, thus opening up the way for
retrospective studies with a substantial reduction of the amount of manual
work. With no need for biomedical annotators or pre-defined ontologies, this
language-agnostic method reached an good extraction accuracy for several
concepts of interest, according to a comparison with a manually structured
file, without requiring any existing corpus with local or new notations.
ObamaNet: Photo-realistic lip-sync from text
Authors: Rithesh Kumar, Jose Sotelo, Kundan Kumar, Alexandre de Brebisson, Yoshua Bengio
Date: 2017-12-06 00:00:00
Tasks: Constrained Lip-synchronization, text-to-speech, Text to Speech

We present ObamaNet, the first architecture that generates both audio and
synchronized photo-realistic lip-sync videos from any new text. Contrary to
other published lip-sync approaches, ours is only composed of fully trainable
neural modules and does not rely on any traditional computer graphics methods.
More precisely, we use three main modules: a text-to-speech network based on
Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio,
and a network based on Pix2Pix to generate the video frames conditioned on the
keypoints.
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
Authors: Bolin Wei, Shuai Lu, Lili Mou, Hao Zhou, Pascal Poupart, Ge Li, Zhi Jin
Date: 2017-12-06 00:00:00
Tasks: Machine Translation, Translation

This paper addresses the question: Why do neural dialog systems generate
short and meaningless replies? We conjecture that, in a dialog system, an
utterance may have multiple equally plausible replies, causing the deficiency
of neural networks in the dialog application. We propose a systematic way to
mimic the dialog scenario in a machine translation system, and manage to
reproduce the phenomenon of generating short and less meaningful sentences in
the translation setting, showing evidence of our conjecture.
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
Authors: Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Isabelle Augenstein
Date: 2017-12-06 00:00:00
Tasks: General Classification, Stance Classification

Rumour stance classification, defined as classifying the stance of specific
social media posts into one of supporting, denying, querying or commenting on
an earlier post, is becoming of increasing interest to researchers. While most
previous work has focused on using individual tweets as classifier inputs, here
we report on the performance of sequential classifiers that exploit the
discourse features inherent in social media interactions or 'conversational
threads'. Testing the effectiveness of four sequential classifiers -- Hawkes
Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured
Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM)
-- on eight datasets associated with breaking news stories, and looking at
different types of local and contextual features, our work sheds new light on
the development of accurate stance classifiers. We show that sequential
classifiers that exploit the use of discourse properties in social media
conversations while using only local features, outperform non-sequential
classifiers. Furthermore, we show that LSTM using a reduced set of features can
outperform the other sequential classifiers; this performance is consistent
across datasets and across types of stances. To conclude, our work also
analyses the different features under study, identifying those that best help
characterise and distinguish between stances, such as supporting tweets being
more likely to be accompanied by evidence than denying tweets. We also set
forth a number of directions for future research.
Beyond the Pixel-Wise Loss for Topology-Aware Delineation
Authors: Agata Mosinska, Pablo Marquez-Neila, Mateusz Kozinski, Pascal Fua
Date: 2017-12-06 00:00:00
Proceeding: CVPR 2018 6

Delineation of curvilinear structures is an important problem in Computer
Vision with multiple practical applications. With the advent of Deep Learning,
many current approaches on automatic delineation have focused on finding more
powerful deep architectures, but have continued using the habitual pixel-wise
losses such as binary cross-entropy. In this paper we claim that pixel-wise
losses alone are unsuitable for this problem because of their inability to
reflect the topological impact of mistakes in the final prediction. We propose
a new loss term that is aware of the higher-order topological features of
linear structures. We also introduce a refinement pipeline that iteratively
applies the same model over the previous delineation to refine the predictions
at each step while keeping the number of parameters and the complexity of the
model constant.
  When combined with the standard pixel-wise loss, both our new loss term and
our iterative refinement boost the quality of the predicted delineations, in
some cases almost doubling the accuracy as compared to the same classifier
trained with the binary cross-entropy alone. We show that our approach
outperforms state-of-the-art methods on a wide range of data, from microscopy
to aerial images.
Measuring Relations Between Concepts In Conceptual Spaces
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2017-07-07 00:00:00

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations between different domains in a geometric way. In this
paper, we extend our formalization by providing quantitative mathematical
definitions for the notions of concept size, subsethood, implication,
similarity, and betweenness. This considerably increases the representational
power of our formalization by introducing measurable ways of describing
relations between concepts.