pwc-archive/papers-with-abstracts huggingface dataset:
Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary
Authors: Dong Nguyen, Barbara McGillivray, Taha Yasseri
Date: 2017-12-22 00:00:00

The Internet facilitates large-scale collaborative projects and the emergence
of Web 2.0 platforms, where producers and consumers of content unify, has
drastically changed the information market. On the one hand, the promise of the
"wisdom of the crowd" has inspired successful projects such as Wikipedia, which
has become the primary source of crowd-based information in many languages. On
the other hand, the decentralized and often un-monitored environment of such
projects may make them susceptible to low quality content. In this work, we
focus on Urban Dictionary, a crowd-sourced online dictionary. We combine
computational methods with qualitative annotation and shed light on the overall
features of Urban Dictionary in terms of growth, coverage and types of content.
We measure a high presence of opinion-focused entries, as opposed to the
meaning-focused entries that we expect from traditional dictionaries.
Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as
proper nouns. Urban Dictionary also contains offensive content, but highly
offensive content tends to receive lower scores through the dictionary's voting
system. The low threshold to include new material in Urban Dictionary enables
quick recording of new words and new meanings, but the resulting heterogeneous
content can pose challenges in using Urban Dictionary as a source to study
language innovation.
A Large-Scale Study of Language Models for Chord Prediction
Authors: Filip Korzeniowski, David R. W. Sears, Gerhard Widmer
Date: 2018-04-05 00:00:00
Tasks: Chord Recognition

We conduct a large-scale study of language models for chord prediction.
Specifically, we compare N-gram models to various flavours of recurrent neural
networks on a comprehensive dataset comprising all publicly available datasets
of annotated chords known to us. This large amount of data allows us to
systematically explore hyper-parameter settings for the recurrent neural
networks---a crucial step in achieving good results with this model class. Our
results show not only a quantitative difference between the models, but also a
qualitative one: in contrast to static N-gram models, certain RNN
configurations adapt to the songs at test time. This finding constitutes a
further step towards the development of chord recognition systems that are more
aware of local musical context than what was previously possible.
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Authors: Victor Escorcia, Cuong D. Dao, Mihir Jain, Bernard Ghanem, Cees Snoek
Date: 2018-04-05 00:00:00
Tasks: Action Localization, Weakly Supervised Action Localization

This paper addresses the problem of spatiotemporal localization of actions in
videos. Compared to leading approaches, which all learn to localize based on
carefully annotated boxes on training video frames, we adhere to a
weakly-supervised solution that only requires a video class label. We introduce
an actor-supervised architecture that exploits the inherent compositionality of
actions in terms of actor transformations, to localize actions. We make two
contributions. First, we propose actor proposals derived from a detector for
human and non-human actors intended for images, which is linked over time by
Siamese similarity matching to account for actor deformations. Second, we
propose an actor-based attention mechanism that enables the localization of the
actions from action class labels and actor proposals and is end-to-end
trainable. Experiments on three human and non-human action datasets show actor
supervision is state-of-the-art for weakly-supervised action localization and
is even competitive to some fully-supervised alternatives.
Adversarial Variational Optimization of Non-Differentiable Simulators
Authors: Gilles Louppe, Joeri Hermans, Kyle Cranmer
Date: 2017-07-22 00:00:00

Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes. We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator. We solve the resulting non-differentiable minimax problem by minimizing variational upper bounds of the two adversarial objectives. Effectively, the procedure results in learning a proposal distribution over simulator parameters, such that the JS divergence between the marginal distribution of the synthetic data and the empirical distribution of observed data is minimized. We evaluate and compare the method with simulators producing both discrete and continuous data.
End-to-End Saliency Mapping via Probability Distribution Prediction
Authors: Saumya Jetley, Naila Murray, Eleonora Vig
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2016 6
Tasks: Saliency Prediction, Text Detection

Most saliency estimation methods aim to explicitly model low-level
conspicuity cues such as edges or blobs and may additionally incorporate
top-down cues using face or text detection. Data-driven methods for training
saliency models using eye-fixation data are increasingly popular, particularly
with the introduction of large-scale datasets and deep architectures. However,
current methods in this latter paradigm use loss functions designed for
classification or regression tasks whereas saliency estimation is evaluated on
topographical maps. In this work, we introduce a new saliency map model which
formulates a map as a generalized Bernoulli distribution. We then train a deep
architecture to predict such maps using novel loss functions which pair the
softmax activation function with measures designed to compute distances between
probability distributions. We show in extensive experiments the effectiveness
of such loss functions over standard ones on four public benchmark datasets,
and demonstrate improved performance over state-of-the-art saliency methods.
Word Segmentation as Graph Partition
Authors: Yuan-Hao Liu, Sheng Yu
Date: 2018-04-05 00:00:00
Tasks: Chinese Word Segmentation, Segmentation, Sentence

We propose a new approach to the Chinese word segmentation problem that
considers the sentence as an undirected graph, whose nodes are the characters.
One can use various techniques to compute the edge weights that measure the
connection strength between characters. Spectral graph partition algorithms are
used to group the characters and achieve word segmentation. We follow the graph
partition approach and design several unsupervised algorithms, and we show
their inspiring segmentation results on two corpora: (1) electronic health
records in Chinese, and (2) benchmark data from the Second International
Chinese Word Segmentation Bakeoff.
Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Authors: Andres Garcia, Jose Manuel Gomez-Perez
Date: 2018-04-05 00:00:00
Tasks: Knowledge Graphs, Specificity, Word Embeddings

The emergence of knowledge graphs in the scholarly communication domain and
recent advances in artificial intelligence and natural language processing
bring us closer to a scenario where intelligent systems can assist scientists
over a range of knowledge-intensive tasks. In this paper we present
experimental results about the generation of word embeddings from scholarly
publications for the intelligent processing of scientific texts extracted from
SciGraph. We compare the performance of domain-specific embeddings with
existing pre-trained vectors generated from very large and general purpose
corpora. Our results suggest that there is a trade-off between corpus
specificity and volume. Embeddings from domain-specific scientific corpora
effectively capture the semantics of the domain. On the other hand, obtaining
comparable results through general corpora can also be achieved, but only in
the presence of very large corpora of well formed text. Furthermore, We also
show that the degree of overlapping between knowledge areas is directly related
to the performance of embeddings in domain evaluation tasks.
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints
Authors: Elena Burceanu, Marius Leordeanu
Date: 2018-04-05 00:00:00
Tasks: Object Tracking

Object tracking is an essential problem in computer vision that has been
researched for several decades. One of the main challenges in tracking is to
adapt to object appearance changes over time and avoiding drifting to
background clutter. We address this challenge by proposing a deep neural
network composed of different parts, which functions as a society of tracking
parts. They work in conjunction according to a certain policy and learn from
each other in a robust manner, using co-occurrence constraints that ensure
robust inference and learning. From a structural point of view, our network is
composed of two main pathways. One pathway is more conservative. It carefully
monitors a large set of simple tracker parts learned as linear filters over
deep feature activation maps. It assigns the parts different roles. It promotes
the reliable ones and removes the inconsistent ones. We learn these filters
simultaneously in an efficient way, with a single closed-form formulation, for
which we propose novel theoretical properties. The second pathway is more
progressive. It is learned completely online and thus it is able to better
model object appearance changes. In order to adapt in a robust manner, it is
learned only on highly confident frames, which are decided using co-occurrences
with the first pathway. Thus, our system has the full benefit of two main
approaches in tracking. The larger set of simpler filter parts offers
robustness, while the full deep network learned online provides adaptability to
change. As shown in the experimental section, our approach achieves state of
the art performance on the challenging VOT17 benchmark, outperforming the
published methods both on the general EAO metric and in the number of fails, by
a significant margin.
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
Authors: Siyou Liu, Long-Yue Wang, Chao-Hong Liu
Date: 2018-04-05 00:00:00
Proceeding: LREC 2018 5
Tasks: Machine Translation, NMT, Translation

Although there are increasing and significant ties between China and
Portuguese-speaking countries, there is not much parallel corpora in the
Chinese-Portuguese language pair. Both languages are very populous, with 1.2
billion native Chinese speakers and 279 million native Portuguese speakers, the
language pair, however, could be considered as low-resource in terms of
available parallel corpora. In this paper, we describe our methods to curate
Chinese-Portuguese parallel corpora and evaluate their quality. We extracted
bilingual data from Macao government websites and proposed a hierarchical
strategy to build a large parallel corpus. Experiments are conducted on
existing and our corpora using both Phrased-Based Machine Translation (PBMT)
and the state-of-the-art Neural Machine Translation (NMT) models. The results
of this work can be used as a benchmark for future Chinese-Portuguese MT
systems. The approach we used in this paper also shows a good example on how to
boost performance of MT systems for low-resource language pairs.
Domain Adaptation for Statistical Machine Translation
Authors: Long-Yue Wang
Date: 2018-04-05 00:00:00
Tasks: Domain Adaptation, Machine Translation, Translation

Statistical machine translation (SMT) systems perform poorly when it is
applied to new target domains. Our goal is to explore domain adaptation
approaches and techniques for improving the translation quality of
domain-specific SMT systems. However, translating texts from a specific domain
(e.g., medicine) is full of challenges. The first challenge is ambiguity. Words
or phrases contain different meanings in different contexts. The second one is
language style due to the fact that texts from different genres are always
presented in different syntax, length and structural organization. The third
one is the out-of-vocabulary words (OOVs) problem. In-domain training data are
often scarce with low terminology coverage. In this thesis, we explore the
state-of-the-art domain adaptation approaches and propose effective solutions
to address those problems.
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
Authors: Saurav Jha, Nikhil Agarwal, Suneeta Agarwal
Date: 2018-04-05 00:00:00
Tasks: Face Detection, Face Recognition, Transfer Learning

Given the recent deep learning advancements in face detection and recognition
techniques for human faces, this paper answers the question "how well would
they work for cartoons'?" - a domain that remains largely unexplored until
recently, mainly due to the unavailability of large scale datasets and the
failure of traditional methods on these. Our work studies and extends multiple
frameworks for the aforementioned tasks. For face detection, we incorporate the
Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrast it
with conventional methods. For face recognition, our two-fold contributions
include: (i) an inductive transfer learning approach combining the feature
learning capability of the Inception v3 network and the feature recognizing
capability of Support Vector Machines (SVMs), (ii) a proposed Hybrid
Convolutional Neural Network (HCNN) framework trained over a fusion of pixel
values and 15 manually located facial keypoints. All the methods are evaluated
on the Cartoon Faces in the Wild (IIIT-CFW) database. We demonstrate that the
HCNN model offers stability superior to that of Inception+SVM over larger input
variations, and explore the plausible architectural principles. We show that
the Inception+SVM model establishes a state-of-the-art F1 score on the task of
gender recognition of cartoon faces. Further, we introduce a small database
hosting location coordinates of 15 points on the cartoon faces belonging to 50
public figures of the IIIT-CFW database.
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Authors: Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
Date: 2014-09-18 00:00:00
Tasks: Bayesian Inference

Kernel Bayesian inference is a principled approach to nonparametric inference
in probabilistic graphical models, where probabilistic relationships between
variables are learned from data in a nonparametric manner. Various algorithms
of kernel Bayesian inference have been developed by combining kernelized basic
probabilistic operations such as the kernel sum rule and kernel Bayes' rule.
However, the current framework is fully nonparametric, and it does not allow a
user to flexibly combine nonparametric and model-based inferences. This is
inefficient when there are good probabilistic models (or simulation models)
available for some parts of a graphical model; this is in particular true in
scientific fields where "models" are the central topic of study. Our
contribution in this paper is to introduce a novel approach, termed the {\em
model-based kernel sum rule} (Mb-KSR), to combine a probabilistic model and
kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized
probabilistic rules, one can develop various algorithms for hybrid (i.e.,
nonparametric and model-based) inferences. As an illustrative example, we
consider Bayesian filtering in a state space model, where typically there
exists an accurate probabilistic model for the state transition process. We
propose a novel filtering method that combines model-based inference for the
state transition process and data-driven, nonparametric inference for the
observation generating process. We empirically validate our approach with
synthetic and real-data experiments, the latter being the problem of
vision-based mobile robot localization in robotics, which illustrates the
effectiveness of the proposed hybrid approach.
Auto-Encoding Sequential Monte Carlo
Authors: Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
Date: 2017-05-29 00:00:00
Proceeding: ICLR 2018 1

We build on auto-encoding sequential Monte Carlo (AESMC): a method for model
and proposal learning based on maximizing the lower bound to the log marginal
likelihood in a broad family of structured probabilistic models. Our approach
relies on the efficiency of sequential Monte Carlo (SMC) for performing
inference in structured probabilistic models and the flexibility of deep neural
networks to model complex conditional probability distributions. We develop
additional theoretical insights and introduce a new training procedure which
improves both model and proposal learning. We demonstrate that our approach
provides a fast, easy-to-implement and scalable means for simultaneous model
learning and proposal adaptation in deep generative models.
Bib2vec: An Embedding-based Search System for Bibliographic Information
Authors: Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki
Date: 2017-06-16 00:00:00

We propose a novel embedding model that represents relationships among
several elements in bibliographic information with high representation ability
and flexibility. Based on this model, we present a novel search system that
shows the relationships among the elements in the ACL Anthology Reference
Corpus. The evaluation results show that our model can achieve a high
prediction ability and produce reasonable search results.
Improving Massive MIMO Belief Propagation Detector with Deep Neural Network
Authors: Xiaosi Tan, Weihong Xu, Yair Be'ery, Zaichen Zhang, Xiaohu You, Chuan Zhang
Date: 2018-04-02 00:00:00

In this paper, deep neural network (DNN) is utilized to improve the belief
propagation (BP) detection for massive multiple-input multiple-output (MIMO)
systems. A neural network architecture suitable for detection task is firstly
introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
based on two modified BP detectors, damped BP and max-sum BP. The correction
factors in these algorithms are optimized through deep learning techniques,
aiming at improved detection performance. Numerical results are presented to
demonstrate the performance of the DNN detectors in comparison with various BP
modifications. The neural network is trained once and can be used for multiple
online detections. The results show that, compared to other state-of-the-art
detectors, the DNN detectors can achieve lower bit error rate (BER) with
improved robustness against various antenna configurations and channel
conditions at the same level of complexity.
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Authors: Tatsuya Yokota, Burak Erem, Seyhmus Guler, Simon K. Warfield, Hidekata Hontani
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Missing Elements, Time Series, Time Series Analysis

Let us consider a case where all of the elements in some continuous slices
are missing in tensor data.
  In this case, the nuclear-norm and total variation regularization methods
usually fail to recover the missing elements.
  The key problem is capturing some delay/shift-invariant structure.
  In this study, we consider a low-rank model in an embedded space of a tensor.
  For this purpose, we extend a delay embedding for a time series to a
"multi-way delay-embedding transform" for a tensor, which takes a given
incomplete tensor as the input and outputs a higher-order incomplete Hankel
tensor.
  The higher-order tensor is then recovered by Tucker-based low-rank tensor
factorization.
  Finally, an estimated tensor can be obtained by using the inverse multi-way
delay embedding transform of the recovered higher-order tensor.
  Our experiments showed that the proposed method successfully recovered
missing slices for some color images and functional magnetic resonance images.
Identifying Cross-Depicted Historical Motifs
Authors: Vinaychandran Pondenkandath, Michele Alberti, Nicole Eichenberger, Rolf Ingold, Marcus Liwicki
Date: 2018-04-05 00:00:00
Tasks: General Classification

Cross-depiction is the problem of identifying the same object even when it is
depicted in a variety of manners. This is a common problem in handwritten
historical documents image analysis, for instance when the same letter or motif
is depicted in several different ways. It is a simple task for humans yet
conventional heuristic computer vision methods struggle to cope with it. In
this paper we address this problem using state-of-the-art deep learning
techniques on a dataset of historical watermarks containing images created with
different methods of reproduction, such as hand tracing, rubbing, and
radiography. To study the robustness of deep learning based approaches to the
cross-depiction problem, we measure their performance on two different tasks:
classification and similarity rankings. For the former we achieve a
classification accuracy of 96% using deep convolutional neural networks. For
the latter we have a false positive rate at 95% true positive rate of 0.11.
These results outperform state-of-the-art methods by a significant margin.
Variational Rejection Sampling
Authors: Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
Date: 2018-04-05 00:00:00
Tasks: Variational Inference

Learning latent variable models with stochastic variational inference is
challenging when the approximate posterior is far from the true posterior, due
to high variance in the gradient estimates. We propose a novel rejection
sampling step that discards samples from the variational posterior which are
assigned low likelihoods by the model. Our approach provides an arbitrarily
accurate approximation of the true posterior at the expense of extra
computation. Using a new gradient estimator for the resulting unnormalized
proposal distribution, we achieve average improvements of 3.71 nats and 0.21
nats over state-of-the-art single-sample and multi-sample alternatives
respectively for estimating marginal log-likelihoods using sigmoid belief
networks on the MNIST dataset.
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
Authors: Xiaodan Liang, Ke Gong, Xiaohui Shen, Liang Lin
Date: 2018-04-05 00:00:00
Tasks: Human Parsing, Pose Estimation, Semantic Segmentation

Human parsing and pose estimation have recently received considerable
interest due to their substantial application potentials. However, the existing
datasets have limited numbers of images and annotations and lack a variety of
human appearances and coverage of challenging cases in unconstrained
environments. In this paper, we introduce a new benchmark named "Look into
Person (LIP)" that provides a significant advancement in terms of scalability,
diversity, and difficulty, which are crucial for future developments in
human-centric analysis. This comprehensive dataset contains over 50,000
elaborately annotated images with 19 semantic part labels and 16 body joints,
which are captured from a broad range of viewpoints, occlusions, and background
complexities. Using these rich annotations, we perform detailed analyses of the
leading human parsing and pose estimation approaches, thereby obtaining
insights into the successes and failures of these methods. To further explore
and take advantage of the semantic correlation of these two tasks, we propose a
novel joint human parsing and pose estimation network to explore efficient
context modeling, which can simultaneously predict parsing and pose with
extremely high quality. Furthermore, we simplify the network to solve human
parsing by exploring a novel self-supervised structure-sensitive learning
approach, which imposes human pose structures into the parsing results without
resorting to extra supervision. The dataset, code and models are available at
http://www.sysu-hcp.net/lip/.