pwc-archive/papers-with-abstracts huggingface dataset:
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Authors: Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an
Date: 2017-04-15 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, Translation

We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation. We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data. Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e. hidden states of the encoder) that are sensitive to their syntactic neighborhoods. GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks). We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.
Sparse Deep Nonnegative Matrix Factorization
Authors: Zhenxing Guo, Shihua Zhang
Date: 2017-07-28 00:00:00
Tasks: Benchmarking, Dimensionality Reduction, General Classification, Representation Learning

Nonnegative matrix factorization is a powerful technique to realize dimension
reduction and pattern recognition through single-layer data representation
learning. Deep learning, however, with its carefully designed hierarchical
structure, is able to combine hidden features to form more representative
features for pattern recognition. In this paper, we proposed sparse deep
nonnegative matrix factorization models to analyze complex data for more
accurate classification and better feature interpretation. Such models are
designed to learn localized features or generate more discriminative
representations for samples in distinct classes by imposing $L_1$-norm penalty
on the columns of certain factors. By extending one-layer model into
multi-layer one with sparsity, we provided a hierarchical way to analyze big
data and extract hidden features intuitively due to nonnegativity. We adopted
the Nesterov's accelerated gradient algorithm to accelerate the computing
process with the convergence rate of $O(1/k^2)$ after $k$ steps iteration. We
also analyzed the computing complexity of our framework to demonstrate their
efficiency. To improve the performance of dealing with linearly inseparable
data, we also considered to incorporate popular nonlinear functions into this
framework and explored their performance. We applied our models onto two
benchmarking image datasets, demonstrating our models can achieve competitive
or better classification performance and produce intuitive interpretations
compared with the typical NMF and competing multi-layer models.
Text Summarization Techniques: A Brief Survey
Authors: Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
Date: 2017-07-07 00:00:00
Tasks: Survey, Text Summarization

In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
Authors: Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
Date: 2017-07-10 00:00:00
Tasks: Clustering, General Classification

The amount of text that is generated every day is increasing dramatically.
This tremendous volume of mostly unstructured text cannot be simply processed
and perceived by computers. Therefore, efficient and effective techniques and
algorithms are required to discover useful patterns. Text mining is the task of
extracting meaningful information from text, which has gained significant
attentions in recent years. In this paper, we describe several of the most
fundamental text mining tasks and techniques including text pre-processing,
classification and clustering. Additionally, we briefly explain text mining in
biomedical and health care domains.
The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation
Authors: Georgi Karadjov, Tsvetomila Mihaylova, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
Date: 2017-07-12 00:00:00

Users posting online expect to remain anonymous unless they have logged in,
which is often needed for them to be able to discuss freely on various topics.
Preserving the anonymity of a text's writer can be also important in some other
contexts, e.g., in the case of witness protection or anonymity programs.
However, each person has his/her own style of writing, which can be analyzed
using stylometry, and as a result, the true identity of the author of a piece
of text can be revealed even if s/he has tried to hide it. Thus, it could be
helpful to design automatic tools that can help a person obfuscate his/her
identity when writing text. In particular, here we propose an approach that
changes the text, so that it is pushed towards average values for some general
stylometric characteristics, thus making the use of these characteristics less
discriminative. The approach consists of three main steps: first, we calculate
the values for some popular stylometric metrics that can indicate authorship;
then we apply various transformations to the text, so that these metrics are
adjusted towards the average level, while preserving the semantics and the
soundness of the text; and finally, we add random noise. This approach turned
out to be very efficient, and yielded the best performance on the Author
Obfuscation task at the PAN-2016 competition.
The WILDTRACK Multi-Camera Person Dataset
Authors: Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet, Andrii Maksai, Cijo Jose, Louis Lettry, Pascal Fua, Luc van Gool, François Fleuret
Date: 2017-07-28 00:00:00
Tasks: Camera Calibration, Pedestrian Detection

People detection methods are highly sensitive to the perpetual occlusions
among the targets. As multi-camera set-ups become more frequently encountered,
joint exploitation of the across views information would allow for improved
detection performances. We provide a large-scale HD dataset named WILDTRACK
which finally makes advanced deep learning methods applicable to this problem.
The seven-static-camera set-up captures realistic and challenging scenarios of
walking people.
  Notably, its camera calibration with jointly high-precision projection widens
the range of algorithms which may make use of this dataset. In aim to help
accelerate the research on automatic camera calibration, such annotations also
accompany this dataset.
  Furthermore, the rich-in-appearance visual context of the pedestrian class
makes this dataset attractive for monocular pedestrian detection as well,
since: the HD cameras are placed relatively close to the people, and the size
of the dataset further increases seven-fold.
  In summary, we overview existing multi-camera datasets and detection methods,
enumerate details of our dataset, and we benchmark multi-camera state of the
art detectors on this new dataset.
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
Authors: Ryo Yonetani, Vishnu Naresh Boddeti, Kris M. Kitani, Yoichi Sato
Date: 2017-04-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Privacy Preserving

We propose a privacy-preserving framework for learning visual classifiers by
leveraging distributed private image data. This framework is designed to
aggregate multiple classifiers updated locally using private data and to ensure
that no private information about the data is exposed during and after its
learning procedure. We utilize a homomorphic cryptosystem that can aggregate
the local classifiers while they are encrypted and thus kept secret. To
overcome the high computational cost of homomorphic encryption of
high-dimensional classifiers, we (1) impose sparsity constraints on local
classifier updates and (2) propose a novel efficient encryption scheme named
doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse
high-dimensional data. DPHE (i) decomposes sparse data into its constituent
non-zero values and their corresponding support indices, (ii) applies
homomorphic encryption only to the non-zero values, and (iii) employs double
permutations on the support indices to make them secret. Our experimental
evaluation on several public datasets shows that the proposed approach achieves
comparable performance against state-of-the-art visual recognition methods
while preserving privacy and significantly outperforms other privacy-preserving
methods.
No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)
Authors: Ferhan Ture, Oliver Jojic
Date: 2016-06-16 00:00:00
Tasks: Question Answering

First-order factoid question answering assumes that the question can be
answered by a single fact in a knowledge base (KB). While this does not seem
like a challenging task, many recent attempts that apply either complex
linguistic reasoning or deep neural networks achieve 65%-76% accuracy on
benchmark sets. Our approach formulates the task as two machine learning
problems: detecting the entities in the question, and classifying the question
as one of the relation types in the KB. We train a recurrent neural network to
solve each problem. On the SimpleQuestions dataset, our approach yields
substantial improvements over previously published results --- even neural
networks based on much more complex architectures. The simplicity of our
approach also has practical advantages, such as efficiency and modularity, that
are valuable especially in an industry setting. In fact, we present a
preliminary analysis of the performance of our model on real queries from
Comcast's X1 entertainment platform with millions of users every day.
Review of Machine Learning Algorithms in Differential Expression Analysis
Authors: Irina Kuznetsova, Yuliya V Karpievitch, Aleksandra Filipovska, Artur Lugmayr, Andreas Holzinger
Date: 2017-07-28 00:00:00
Tasks: BIG-bench Machine Learning

In biological research machine learning algorithms are part of nearly every
analytical process. They are used to identify new insights into biological
phenomena, interpret data, provide molecular diagnosis for diseases and develop
personalized medicine that will enable future treatments of diseases. In this
paper we (1) illustrate the importance of machine learning in the analysis of
large scale sequencing data, (2) present an illustrative standardized workflow
of the analysis process, (3) perform a Differential Expression (DE) analysis of
a publicly available RNA sequencing (RNASeq) data set to demonstrate the
capabilities of various algorithms at each step of the workflow, and (4) show a
machine learning solution in improving the computing time, storage
requirements, and minimize utilization of computer memory in analyses of
RNA-Seq datasets. The source code of the analysis pipeline and associated
scripts are presented in the paper appendix to allow replication of
experiments.
Generator Reversal
Authors: Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann
Date: 2017-07-28 00:00:00

We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we propose instead to use more flexible code distributions. These
distributions are estimated non-parametrically by reversing the generator map
during training. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
A weighting strategy for Active Shape Models
Authors: Alma Eguizabal, Peter J. Schreier
Date: 2017-07-28 00:00:00

Active Shape Models (ASM) are an iterative segmentation technique to find a
landmark-based contour of an object. In each iteration, a least-squares fit of
a plausible shape to some detected target landmarks is determined. Finding
these targets is a critical step: some landmarks are more reliably detected
than others, and some landmarks may not be within the field of view of their
detectors. To add robustness while preserving simplicity at the same time, a
generalized least-squares approach can be used, where a weighting matrix
incorporates reliability information about the landmarks. We propose a strategy
to choose this matrix, based on the covariance of empirically determined
residuals of the fit. We perform a further step to determine whether the target
landmarks are within the range of their detectors. We evaluate our strategy on
fluoroscopic X-ray images to segment the femur. We show that our technique
outperforms the standard ASM as well as other more heuristic weighted
least-squares strategies.
Improving coreference resolution with automatically predicted prosodic information
Authors: Ina Rösiger, Sabrina Stehwien, Arndt Riester, Ngoc Thang Vu
Date: 2017-07-28 00:00:00
Proceeding: WS 2017 9
Tasks: coreference-resolution, Coreference Resolution

Adding manually annotated prosodic information, specifically pitch accents
and phrasing, to the typical text-based feature set for coreference resolution
has previously been shown to have a positive effect on German data. Practical
applications on spoken language, however, would rely on automatically predicted
prosodic information. In this paper we predict pitch accents (and phrase
boundaries) using a convolutional neural network (CNN) model from acoustic
features extracted from the speech signal. After an assessment of the quality
of these automatic prosodic annotations, we show that they also significantly
improve coreference resolution.
Enabling Smart Data: Noise filtering in Big Data classification
Authors: Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera
Date: 2017-04-06 00:00:00
Tasks: Classification, General Classification

In any knowledge discovery process the value of extracted knowledge is
directly related to the quality of the data used. Big Data problems, generated
by massive growth in the scale of data observed in recent years, also follow
the same dictate. A common problem affecting data quality is the presence of
noise, particularly in classification problems, where label noise refers to the
incorrect labeling of training instances, and is known to be a very disruptive
feature of data. However, in this Big Data era, the massive growth in the scale
of the data poses a challenge to traditional proposals created to tackle noise,
as they have difficulties coping with such a large amount of data. New
algorithms need to be proposed to treat the noise in Big Data problems,
providing high quality and clean data, also known as Smart Data. In this paper,
two Big Data preprocessing approaches to remove noisy examples are proposed: an
homogeneous ensemble and an heterogeneous ensemble filter, with special
emphasis in their scalability and performance traits. The obtained results show
that these proposals enable the practitioner to efficiently obtain a Smart
Dataset from any Big Data classification problem.
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
Authors: Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, Enrique Munoz de Cote
Date: 2017-07-28 00:00:00
Tasks: Multi-Armed Bandits, Reinforcement Learning

The key challenge in multiagent learning is learning a best response to the
behaviour of other agents, which may be non-stationary: if the other agents
adapt their strategy as well, the learning target moves. Disparate streams of
research have approached non-stationarity from several angles, which make a
variety of implicit assumptions that make it hard to keep an overview of the
state of the art and to validate the innovation and significance of new works.
This survey presents a coherent overview of work that addresses
opponent-induced non-stationarity with tools from game theory, reinforcement
learning and multi-armed bandits. Further, we reflect on the principle
approaches how algorithms model and cope with this non-stationarity, arriving
at a new framework and five categories (in increasing order of sophistication):
ignore, forget, respond to target models, learn models, and theory of mind. A
wide range of state-of-the-art algorithms is classified into a taxonomy, using
these categories and key characteristics of the environment (e.g.,
observability) and adaptation behaviour of the opponents (e.g., smooth,
abrupt). To clarify even further we present illustrative variations of one
domain, contrasting the strengths and limitations of each category. Finally, we
discuss in which environments the different approaches yield most merit, and
point to promising avenues of future research.
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding
Authors: Giuseppe Lisanti, Niki Martinel, Alberto del Bimbo, Gian Luca Foresti
Date: 2017-07-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dictionary Learning, Person Re-Identification

Person re-identification is best known as the problem of associating a single
person that is observed from one or more disjoint cameras. The existing
literature has mainly addressed such an issue, neglecting the fact that people
usually move in groups, like in crowded scenarios. We believe that the
additional information carried by neighboring individuals provides a relevant
visual context that can be exploited to obtain a more robust match of single
persons within the group. Despite this, re-identifying groups of people
compound the common single person re-identification problems by introducing
changes in the relative position of persons within the group and severe
self-occlusions. In this paper, we propose a solution for group
re-identification that grounds on transferring knowledge from single person
re-identification to group re-identification by exploiting sparse dictionary
learning. First, a dictionary of sparse atoms is learned using patches
extracted from single person images. Then, the learned dictionary is exploited
to obtain a sparsity-driven residual group representation, which is finally
matched to perform the re-identification. Extensive experiments on the i-LIDS
groups and two newly collected datasets show that the proposed solution
outperforms state-of-the-art approaches.
Learning to Predict Charges for Criminal Cases with Legal Basis
Authors: Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao
Date: 2017-07-28 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Articles, Prediction

The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model the charge prediction task and the relevant article extraction task in a
unified framework. The experimental results show that, besides providing legal
basis, the relevant articles can also clearly improve the charge prediction
results, and our full model can effectively predict appropriate charges for
cases with different expression styles.
A Survey of Quantum Learning Theory
Authors: Srinivasan Arunachalam, Ronald de Wolf
Date: 2017-01-24 00:00:00
Tasks: BIG-bench Machine Learning, Learning Theory, Survey

This paper surveys quantum learning theory: the theoretical aspects of
machine learning using quantum computers. We describe the main results known
for three models of learning: exact learning from membership queries, and
Probably Approximately Correct (PAC) and agnostic learning from classical or
quantum examples.
A Consistent Regularization Approach for Structured Prediction
Authors: Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco
Date: 2016-05-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Prediction, Structured Prediction

We propose and analyze a regularization approach for structured prediction
problems. We characterize a large class of loss functions that allows to
naturally embed structured outputs in a linear space. We exploit this fact to
design learning algorithms using a surrogate loss approach and regularization
techniques. We prove universal consistency and finite sample bounds
characterizing the generalization properties of the proposed methods.
Experimental results are provided to demonstrate the practical usefulness of
the proposed approach.
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization
Authors: Francis Bach
Date: 2017-07-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: regression

We consider the minimization of submodular functions subject to ordering
constraints. We show that this optimization problem can be cast as a convex
optimization problem on a space of uni-dimensional measures, with ordering
constraints corresponding to first-order stochastic dominance. We propose new
discretization schemes that lead to simple and efficient algorithms based on
zero-th, first, or higher order oracles; these algorithms also lead to
improvements without isotonic constraints. Finally, our experiments show that
non-convex loss functions can be much more robust to outliers for isotonic
regression, while still leading to an efficient optimization problem.
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions
Authors: Pascal Mettes, Cees G. M. Snoek
Date: 2017-07-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Localization, Attribute, Classification, General Classification, Object, Retrieval, Transfer Learning

We aim for zero-shot localization and classification of human actions in
video. Where traditional approaches rely on global attribute or object
classification scores for their zero-shot knowledge transfer, our main
contribution is a spatial-aware object embedding. To arrive at spatial
awareness, we build our embedding on top of freely available actor and object
detectors. Relevance of objects is determined in a word embedding space and
further enforced with estimated spatial preferences. Besides local object
awareness, we also embed global object awareness into our embedding to maximize
actor and object interaction. Finally, we exploit the object positions and
sizes in the spatial-aware embedding to demonstrate a new spatio-temporal
action retrieval scenario with composite queries. Action localization and
classification experiments on four contemporary action video datasets support
our proposal. Apart from state-of-the-art results in the zero-shot localization
and classification settings, our spatial-aware embedding is even competitive
with recent supervised action localization alternatives.
Localizing Actions from Video Labels and Pseudo-Annotations
Authors: Pascal Mettes, Cees G. M. Snoek, Shih-Fu Chang
Date: 2017-07-28 00:00:00
Tasks: Action Localization

The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action proposals selected with human point-supervision perform as
well as using expensive box annotations. Rather than asking users to provide
point supervision, we propose fully automatic visual cues that replace manual
point annotations. We call the cues pseudo-annotations, introduce five of them,
and propose a correlation metric for automatically selecting and combining
them. Thorough evaluation on challenging action localization datasets shows
that we reach results comparable to results with full box supervision. We also
show that pseudo-annotations can be leveraged during testing to improve weakly-
and strongly-supervised localizers.