pwc-archive/papers-with-abstracts huggingface dataset:
Distance entropy cartography characterises centrality in complex networks
Authors: Massimo Stella, Manlio De Domenico
Date: 2018-02-28 00:00:00

We introduce distance entropy as a measure of homogeneity in the distribution
of path lengths between a given node and its neighbours in a complex network.
Distance entropy defines a new centrality measure whose properties are
investigated for a variety of synthetic network models. By coupling distance
entropy information with closeness centrality, we introduce a network
cartography which allows one to reduce the degeneracy of ranking based on
closeness alone. We apply this methodology to the empirical multiplex lexical
network encoding the linguistic relationships known to English speaking
toddlers. We show that the distance entropy cartography better predicts how
children learn words compared to closeness centrality. Our results highlight
the importance of distance entropy for gaining insights from distance patterns
in complex networks.
Sequence-to-Sequence ASR Optimization via Reinforcement Learning
Authors: Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Date: 2017-10-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition

Despite the success of sequence-to-sequence approaches in automatic speech
recognition (ASR) systems, the models still suffer from several problems,
mainly due to the mismatch between the training and inference conditions. In
the sequence-to-sequence architecture, the model is trained to predict the
grapheme of the current time-step given the input of speech signal and the
ground-truth grapheme history of the previous time-steps. However, it remains
unclear how well the model approximates real-world speech during inference.
Thus, generating the whole transcription from scratch based on previous
predictions is complicated and errors can propagate over time. Furthermore, the
model is optimized to maximize the likelihood of training data instead of error
rate evaluation metrics that actually quantify recognition quality. This paper
presents an alternative strategy for training sequence-to-sequence ASR models
by adopting the idea of reinforcement learning (RL). Unlike the standard
training scheme with maximum likelihood estimation, our proposed approach
utilizes the policy gradient algorithm. We can (1) sample the whole
transcription based on the model's prediction in the training process and (2)
directly optimize the model with negative Levenshtein distance as the reward.
Experimental results demonstrate that we significantly improved the performance
compared to a model trained only with maximum likelihood estimation.
A Bayesian Model for Activities Recommendation and Event Structure Optimization Using Visitors Tracking
Authors: Henrique X. Goulart, Guilherme A. Wachs-Lopes
Date: 2018-02-28 00:00:00
Tasks: Management

In events that are composed by many activities, there is a problem that
involves retrieve and management the information of visitors that are visiting
the activities. This management is crucial to find some activities that are
drawing attention of visitors; identify an ideal positioning for activities;
which path is more frequented by visitors. In this work, these features are
studied using Complex Network theory. For the beginning, an artificial database
was generated to study the mentioned features. Secondly, this work shows a
method to optimize the event structure that is better than a random method and
a recommendation system that achieves ~95% of accuracy.
Learning from Between-class Examples for Deep Sound Recognition
Authors: Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-11-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation

Deep learning methods have achieved high performance in sound recognition
tasks. Deciding how to feed the training data is important for further
performance improvement. We propose a novel learning method for deep sound
recognition: Between-Class learning (BC learning). Our strategy is to learn a
discriminative feature space by recognizing the between-class sounds as
between-class sounds. We generate between-class sounds by mixing two sounds
belonging to different classes with a random ratio. We then input the mixed
sound to the model and train the model to output the mixing ratio. The
advantages of BC learning are not limited only to the increase in variation of
the training data; BC learning leads to an enlargement of Fisher's criterion in
the feature space and a regularization of the positional relationship among the
feature distributions of the classes. The experimental results show that BC
learning improves the performance on various sound recognition networks,
datasets, and data augmentation schemes, in which BC learning proves to be
always beneficial. Furthermore, we construct a new deep sound recognition
network (EnvNet-v2) and train it with BC learning. As a result, we achieved a
performance surpasses the human level.
Graph Kernels based on High Order Graphlet Parsing and Hashing
Authors: Anjan Dutta, Hichem Sahbi
Date: 2018-02-28 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

Graph-based methods are known to be successful in many machine learning and
pattern classification tasks. These methods consider semi-structured data as
graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives.
However, these non-vectorial graph data cannot be straightforwardly plugged
into off-the-shelf machine learning algorithms without a preliminary step of --
explicit/implicit -- graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly
discriminant. In this paper, we propose a novel high-order stochastic graphlet
embedding (SGE) that maps graphs into vector spaces. Our main contribution
includes a new stochastic search procedure that efficiently parses a given
graph and extracts/samples unlimitedly high-order graphlets. We consider these
graphlets, with increasing orders, to model local primitives as well as their
increasingly complex interactions. In order to build our graph representation,
we measure the distribution of these graphlets into a given graph, using
particular hash functions that efficiently assign sampled graphlets into
isomorphic sets with a very low probability of collision. When combined with
maximum margin classifiers, these graphlet-based representations have positive
impact on the performance of pattern comparison and recognition as corroborated
through extensive experiments using standard benchmark databases.
Prioritized Norms in Formal Argumentation
Authors: Beishui Liao, Nir Oren, Leendert van der Torre, Serena Villata
Date: 2017-09-23 00:00:00

To resolve conflicts among norms, various nonmonotonic formalisms can be used
to perform prioritized normative reasoning. Meanwhile, formal argumentation
provides a way to represent nonmonotonic logics. In this paper, we propose a
representation of prioritized normative reasoning by argumentation. Using
hierarchical abstract normative systems, we define three kinds of prioritized
normative reasoning approaches, called Greedy, Reduction, and Optimization.
Then, after formulating an argumentation theory for a hierarchical abstract
normative system, we show that for a totally ordered hierarchical abstract
normative system, Greedy and Reduction can be represented in argumentation by
applying the weakest link and the last link principles respectively, and
Optimization can be represented by introducing additional defeats capturing the
idea that for each argument that contains a norm not belonging to the maximal
obeyable set then this argument should be rejected.
Unit Commitment using Nearest Neighbor as a Short-Term Proxy
Authors: Gal Dalal, Elad Gilboa, Shie Mannor, Louis Wehenkel
Date: 2016-11-30 00:00:00

We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as
a proxy for quickly approximating outcomes of short-term decisions, to make
tractable hierarchical long-term assessment and planning for large power
systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96
show high accuracy measured on operational cost, achieved in runtimes that are
lower in several orders of magnitude than the traditional approach.
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Authors: Thanh-Toan Do, Ming Cai, Trung Pham, Ian Reid
Date: 2018-02-28 00:00:00
Tasks: Benchmarking, Instance Segmentation, Object, object-detection, Object Detection, Pose Estimation, Semantic Segmentation

Detecting objects and their 6D poses from only RGB images is an important
task for many robotic applications. While deep learning methods have made
significant progress in visual object detection and segmentation, the object
pose estimation task is still challenging. In this paper, we introduce an
end-toend deep learning framework, named Deep-6DPose, that jointly detects,
segments, and most importantly recovers 6D poses of object instances from a
single RGB image. In particular, we extend the recent state-of-the-art instance
segmentation network Mask R-CNN with a novel pose estimation branch to directly
regress 6D object poses without any post-refinements. Our key technical
contribution is the decoupling of pose parameters into translation and rotation
so that the rotation can be regressed via a Lie algebra representation. The
resulting pose regression loss is differential and unconstrained, making the
training tractable. The experiments on two standard pose benchmarking datasets
show that our proposed approach compares favorably with the state-of-the-art
RGB-based multi-stage pose estimation methods. Importantly, due to the
end-to-end architecture, Deep-6DPose is considerably faster than competing
multi-stage methods, offers an inference speed of 10 fps that is well suited
for robotic applications.
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
Authors: Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa, Raluca D. Gaina, Julian Togelius, Simon M. Lucas
Date: 2018-02-28 00:00:00

General Video Game Playing (GVGP) aims at designing an agent that is capable
of playing multiple video games with no human intervention. In 2014, The
General Video Game AI (GVGAI) competition framework was created and released
with the purpose of providing researchers a common open-source and easy to use
platform for testing their AI methods with potentially infinity of games
created using Video Game Description Language (VGDL). The framework has been
expanded into several tracks during the last few years to meet the demand of
different research directions. The agents are required either to play multiple
unknown games with or without access to game simulations, or to design new game
levels or rules. This survey paper presents the VGDL, the GVGAI framework,
existing tracks, and reviews the wide use of GVGAI framework in research,
education and competitions five years after its birth. A future plan of
framework improvements is also described.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Authors: Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber
Date: 2018-02-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Common Sense Reasoning

Common-sense physical reasoning is an essential ingredient for any
intelligent agent operating in the real-world. For example, it can be used to
simulate the environment, or to infer the state of parts of the world that are
currently unobserved. In order to match real-world conditions this causal
knowledge must be learned without access to supervised data. To address this
problem we present a novel method that learns to discover objects and model
their physical interactions from raw visual images in a purely
\emph{unsupervised} fashion. It incorporates prior knowledge about the
compositional nature of human perception to factor interactions between
object-pairs and learn efficiently. On videos of bouncing balls we show the
superior modelling capabilities of our method compared to other unsupervised
neural approaches that do not incorporate such prior knowledge. We demonstrate
its ability to handle occlusion and show that it can extrapolate learned
knowledge to scenes with different numbers of objects.
Learning to Adapt Structured Output Space for Semantic Segmentation
Authors: Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker
Date: 2018-02-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Synthetic-to-Real Translation

Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.
Efficient Optimization for Rank-based Loss Functions
Authors: Pritish Mohapatra, Michal Rolinek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
Date: 2016-04-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Information Retrieval, Retrieval

The accuracy of information retrieval systems is often measured using complex
loss functions such as the average precision (AP) or the normalized discounted
cumulative gain (NDCG). Given a set of positive and negative samples, the
parameters of a retrieval system can be estimated by minimizing these loss
functions. However, the non-differentiability and non-decomposability of these
loss functions does not allow for simple gradient based optimization
algorithms. This issue is generally circumvented by either optimizing a
structured hinge-loss upper bound to the loss function or by using asymptotic
methods like the direct-loss minimization framework. Yet, the high
computational complexity of loss-augmented inference, which is necessary for
both the frameworks, prohibits its use in large training data sets. To
alleviate this deficiency, we present a novel quicksort flavored algorithm for
a large class of non-decomposable loss functions. We provide a complete
characterization of the loss functions that are amenable to our algorithm, and
show that it includes both AP and NDCG based loss functions. Furthermore, we
prove that no comparison based algorithm can improve upon the computational
complexity of our approach asymptotically. We demonstrate the effectiveness of
our approach in the context of optimizing the structured hinge loss upper bound
of AP and NDCG loss for learning models for a variety of vision tasks. We show
that our approach provides significantly better results than simpler
decomposable loss functions, while requiring a comparable training time.
A Model for Medical Diagnosis Based on Plantar Pressure
Authors: Guoxiong Xu, Zhengfei Wang, Hongshi Huang, Wenxin Li, Can Liu, Shilei Liu
Date: 2018-02-28 00:00:00
Tasks: Diagnostic, Medical Diagnosis

The process of determining which disease or condition explains a person's
symptoms and signs can be very complicated and may be inaccurate in some cases.
The general belief is that diagnosing diseases relies on doctors' keen
intuition, rich experience and professional equipment. In this work, we employ
ideas from recent advances in plantar pressure research and from the powerful
capacity of the convolutional neural network for learning representations.
Here, we propose a model using convolutional neural network based on plantar
pressure for medical diagnosis. Our model learns a network that maps plantar
pressure data to its corresponding medical diagnostic label. We then apply our
model to make the medical diagnosis on datasets we collected from cooperative
hospital and achieve an accuracy of 98.36%. We demonstrate that the model base
on the convolutional neural network is competitive in medical diagnosis.
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
Authors: Maksym Byshkin, Alex Stivala, Antonietta Mira, Garry Robins, Alessandro Lomi
Date: 2018-02-28 00:00:00

A major line of contemporary research on complex networks is based on the
development of statistical models that specify the local motifs associated with
macro-structural properties observed in actual networks. This statistical
approach becomes increasingly problematic as network size increases. In the
context of current research on efficient estimation of models for large network
data sets, we propose a fast algorithm for maximum likelihood estimation (MLE)
that afords a signifcant increase in the size of networks amenable to direct
empirical analysis. The algorithm we propose in this paper relies on properties
of Markov chains at equilibrium, and for this reason it is called equilibrium
expectation (EE). We demonstrate the performance of the EE algorithm in the
context of exponential random graphmodels (ERGMs) a family of statistical
models commonly used in empirical research based on network data observed at a
single period in time. Thus far, the lack of efcient computational strategies
has limited the empirical scope of ERGMs to relatively small networks with a
few thousand nodes. The approach we propose allows a dramatic increase in the
size of networks that may be analyzed using ERGMs. This is illustrated in an
analysis of several biological networks and one social network with 104,103
nodes
Variational Inference based on Robust Divergences
Authors: Futoshi Futami, Issei Sato, Masashi Sugiyama
Date: 2017-10-18 00:00:00
Tasks: Bayesian Inference, Variational Inference

Robustness to outliers is a central issue in real-world machine learning
applications. While replacing a model to a heavy-tailed one (e.g., from
Gaussian to Student-t) is a standard approach for robustification, it can only
be applied to simple models. In this paper, based on Zellner's optimization and
variational formulation of Bayesian inference, we propose an outlier-robust
pseudo-Bayesian variational method by replacing the Kullback-Leibler divergence
used for data fitting to a robust divergence such as the beta- and
gamma-divergences. An advantage of our approach is that superior but complex
models such as deep networks can also be handled. We theoretically prove that,
for deep networks with ReLU activation functions, the \emph{influence function}
in our proposed method is bounded, while it is unbounded in the ordinary
variational inference. This implies that our proposed method is robust to both
of input and output outliers, while the ordinary variational method is not. We
experimentally demonstrate that our robust variational method outperforms
ordinary variational inference in regression and classification with deep
networks.
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
Authors: Christian Reul, Uwe Springmann, Christoph Wick, Frank Puppe
Date: 2018-02-27 00:00:00
Tasks: Active Learning, Optical Character Recognition (OCR)

We combine three methods which significantly improve the OCR accuracy of OCR
models trained on early printed books: (1) The pretraining method utilizes the
information stored in already existing models trained on a variety of typesets
(mixed models) instead of starting the training from scratch. (2) Performing
cross fold training on a single set of ground truth data (line images and their
transcriptions) with a single OCR engine (OCRopus) produces a committee whose
members then vote for the best outcome by also taking the top-N alternatives
and their intrinsic confidence values into account. (3) Following the principle
of maximal disagreement we select additional training lines which the voters
disagree most on, expecting them to offer the highest information gain for a
subsequent training (active learning). Evaluations on six early printed books
yielded the following results: On average the combination of pretraining and
voting improved the character accuracy by 46% when training five folds starting
from the same mixed model. This number rose to 53% when using different models
for pretraining, underlining the importance of diverse voters. Incorporating
active learning improved the obtained results by another 16% on average
(evaluated on three of the six books). Overall, the proposed methods lead to an
average error rate of 2.5% when training on only 60 lines. Using a substantial
ground truth pool of 1,000 lines brought the error rate down even further to
less than 1% on average.
Compressed Sensing, ASBSR-method of image sampling and reconstruction and the problem of digital image acquisition with the lowest possible sampling rate
Authors: Leonid P. Yaroslavsky
Date: 2017-10-10 00:00:00
Tasks: compressed sensing, Demosaicking, Image Reconstruction

The problem of minimization of the number of measurements needed for digital
image acquisition and reconstruction with a given accuracy is addressed. Basics
of the sampling theory are outlined to show that the lower bound of signal
sampling rate sufficient for signal reconstruction with a given accuracy is
equal to the spectrum sparsity of the signal sparse approximation that has this
accuracy. It is revealed that the compressed sensing approach, which was
advanced as a solution to the sampling rate minimization problem, is far from
reaching the sampling rate theoretical minimum. Potentials and limitations of
compressed sensing are demystified using a simple and intutive model, A method
of image Arbitrary Sampling and Bounded Spectrum Reconstruction (ASBSR-method)
is described that allows to draw near the image sampling rate theoretical
minimum. Presented and discussed are also results of experimental verification
of the ASBSR-method and its possible applicability extensions to solving
various underdetermined inverse problems such as color image demosaicing, image
in-painting, image reconstruction from their sparsely sampled or decimated
projections, image reconstruction from the modulus of its Fourier spectrum, and
image reconstruction from its sparse samples in Fourier domain