pwc-archive/papers-with-abstracts huggingface dataset:
Path-following based Point Matching using Similarity Transformation
Authors: Wei Lian
Date: 2017-01-04 00:00:00

To address the problem of 3D point matching where the poses of two point sets
are unknown, we adapt a recently proposed path following based method to use
similarity transformation instead of the original affine transformation. The
reduced number of transformation parameters leads to more constrained and
desirable matching results. Experimental results demonstrate better robustness
of the proposed method over state-of-the-art methods.
Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices
Authors: Wenjia Meng, Zonghua Gu, Ming Zhang, Zhaohui Wu
Date: 2017-01-02 00:00:00
Tasks: Computational Efficiency, General Classification, Model Compression, Vocal Bursts Valence Prediction

With the rapid proliferation of Internet of Things and intelligent edge
devices, there is an increasing need for implementing machine learning
algorithms, including deep learning, on resource-constrained mobile embedded
devices with limited memory and computation power. Typical large Convolutional
Neural Networks (CNNs) need large amounts of memory and computational power,
and cannot be deployed on embedded devices efficiently. We present Two-Bit
Networks (TBNs) for model compression of CNNs with edge weights constrained to
(-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the
memory usage and improve computational efficiency significantly while achieving
good performance in terms of classification accuracy, thus representing a
reasonable tradeoff between model size and performance.
World Literature According to Wikipedia: Introduction to a DBpedia-Based Framework
Authors: Christoph Hube, Frank Fischer, Robert Jäschke, Gerhard Lauer, Mads Rosendahl Thomsen
Date: 2017-01-04 00:00:00
Tasks: Articles

Among the manifold takes on world literature, it is our goal to contribute to
the discussion from a digital point of view by analyzing the representation of
world literature in Wikipedia with its millions of articles in hundreds of
languages. As a preliminary, we introduce and compare three different
approaches to identify writers on Wikipedia using data from DBpedia, a
community project with the goal of extracting and providing structured
information from Wikipedia. Equipped with our basic set of writers, we analyze
how they are represented throughout the 15 biggest Wikipedia language versions.
We combine intrinsic measures (mostly examining the connectedness of articles)
with extrinsic ones (analyzing how often articles are frequented by readers)
and develop methods to evaluate our results. The better part of our findings
seems to convey a rather conservative, old-fashioned version of world
literature, but a version derived from reproducible facts revealing an implicit
literary canon based on the editing and reading behavior of millions of people.
While still having to solve some known issues, the introduced methods will help
us build an observatory of world literature to further investigate its
representativeness and biases.
A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets
Authors: Wei Lian, Lei Zhang
Date: 2017-01-04 00:00:00

Point matching refers to the process of finding spatial transformation and
correspondences between two sets of points. In this paper, we focus on the case
that there is only partial overlap between two point sets. Following the
approach of the robust point matching method, we model point matching as a
mixed linear assignment-least square problem and show that after eliminating
the transformation variable, the resulting problem of minimization with respect
to point correspondence is a concave optimization problem. Furthermore, this
problem has the property that the objective function can be converted into a
form with few nonlinear terms via a linear transformation. Based on these
properties, we employ the branch-and-bound (BnB) algorithm to optimize the
resulting problem where the dimension of the search space is small. To further
improve efficiency of the BnB algorithm where computation of the lower bound is
the bottleneck, we propose a new lower bounding scheme which has a
k-cardinality linear assignment formulation and can be efficiently solved.
Experimental results show that the proposed algorithm outperforms
state-of-the-art methods in terms of robustness to disturbances and point
matching accuracy.
Playtime Measurement with Survival Analysis
Authors: Markus Viljanen, Antti Airola, Jukka Heikkonen, Tapio Pahikkala
Date: 2017-01-04 00:00:00
Tasks: Survival Analysis

Maximizing product use is a central goal of many businesses, which makes
retention and monetization two central analytics metrics in games. Player
retention may refer to various duration variables quantifying product use:
total playtime or session playtime are popular research targets, and active
playtime is well-suited for subscription games. Such research often has the
goal of increasing player retention or conversely decreasing player churn.
Survival analysis is a framework of powerful tools well suited for retention
type data. This paper contributes new methods to game analytics on how playtime
can be analyzed using survival analysis without covariates. Survival and hazard
estimates provide both a visual and an analytic interpretation of the playtime
phenomena as a funnel type nonparametric estimate. Metrics based on the
survival curve can be used to aggregate this playtime information into a single
statistic. Comparison of survival curves between cohorts provides a scientific
AB-test. All these methods work on censored data and enable computation of
confidence intervals. This is especially important in time and sample limited
data which occurs during game development. Throughout this paper, we illustrate
the application of these methods to real world game development problems on the
Hipster Sheep mobile game.
Dense Associative Memory is Robust to Adversarial Inputs
Authors: Dmitry Krotov, John J. Hopfield
Date: 2017-01-04 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Deep neural networks (DNN) trained in a supervised way suffer from two known
problems. First, the minima of the objective function used in learning
correspond to data points (also known as rubbish examples or fooling images)
that lack semantic similarity with the training data. Second, a clean input can
be changed by a small, and often imperceptible for human vision, perturbation,
so that the resulting deformed input is misclassified by the network. These
findings emphasize the differences between the ways DNN and humans classify
patterns, and raise a question of designing learning algorithms that more
accurately mimic human perception compared to the existing methods.
  Our paper examines these questions within the framework of Dense Associative
Memory (DAM) models. These models are defined by the energy function, with
higher order (higher than quadratic) interactions between the neurons. We show
that in the limit when the power of the interaction vertex in the energy
function is sufficiently large, these models have the following three
properties. First, the minima of the objective function are free from rubbish
images, so that each minimum is a semantically meaningful pattern. Second,
artificial patterns poised precisely at the decision boundary look ambiguous to
human subjects and share aspects of both classes that are separated by that
decision boundary. Third, adversarial images constructed by models with small
power of the interaction vertex, which are equivalent to DNN with rectified
linear units (ReLU), fail to transfer to and fool the models with higher order
interactions. This opens up a possibility to use higher order models for
detecting and stopping malicious adversarial attacks. The presented results
suggest that DAM with higher order energy functions are closer to human visual
perception than DNN with ReLUs.
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Authors: Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng
Date: 2016-12-03 00:00:00
Tasks: Natural Language Understanding

Natural language understanding and dialogue policy learning are both
essential in conversational systems that predict the next system actions in
response to a current user utterance. Conventional approaches aggregate
separate models of natural language understanding (NLU) and system action
prediction (SAP) as a pipeline that is sensitive to noisy outputs of
error-prone NLU. To address the issues, we propose an end-to-end deep recurrent
neural network with limited contextual dialogue memory by jointly training NLU
and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our
proposed model significantly outperforms the state-of-the-art pipeline models
for both NLU and SAP, which indicates that our joint model is capable of
mitigating the affects of noisy NLU outputs, and NLU model can be refined by
error flows backpropagating from the extra supervised signals of system
actions.
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition
Authors: Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan, Shu Wang, Bin Hu, Li Cheng, David S. Rosenblum
Date: 2017-01-04 00:00:00
Tasks: Activity Recognition, Diversity

Complex activity recognition is challenging due to the inherent uncertainty
and diversity of performing a complex activity. Normally, each instance of a
complex activity has its own configuration of atomic actions and their temporal
dependencies. We propose in this paper an atomic action-based Bayesian model
that constructs Allen's interval relation networks to characterize complex
activities with structural varieties in a probabilistic generative way: By
introducing latent variables from the Chinese restaurant process, our approach
is able to capture all possible styles of a particular complex activity as a
unique set of distributions over atomic actions and relations. We also show
that local temporal dependencies can be retained and are globally consistent in
the resulting interval network. Moreover, network structure can be learned from
empirical data. A new dataset of complex hand activities has been constructed
and made publicly available, which is much larger in size than any existing
datasets. Empirical evaluations on benchmark datasets as well as our in-house
dataset demonstrate the competitiveness of our approach.
AENet: Learning Deep Audio Features for Video Analysis
Authors: Naoya Takahashi, Michael Gygli, Luc van Gool
Date: 2017-01-03 00:00:00
Tasks: Action Recognition, Data Augmentation, Event Detection, GPU, Highlight Detection, Temporal Action Localization, Transfer Learning

We propose a new deep network for audio event recognition, called AENet. In
contrast to speech, sounds coming from audio events may be produced by a wide
variety of sources. Furthermore, distinguishing them often requires analyzing
an extended time period due to the lack of clear sub-word units that are
present in speech. In order to incorporate this long-time frequency structure
of audio events, we introduce a convolutional neural network (CNN) operating on
a large temporal input. In contrast to previous works this allows us to train
an audio event detection system end-to-end. The combination of our network
architecture and a novel data augmentation outperforms previous methods for
audio event detection by 16%. Furthermore, we perform transfer learning and
show that our model learnt generic audio features, similar to the way CNNs
learn generic features on vision tasks. In video analysis, combining visual
features and traditional audio features such as MFCC typically only leads to
marginal improvements. Instead, combining visual features with our AENet
features, which can be computed efficiently on a GPU, leads to significant
performance improvements on action recognition and video highlight detection.
In video highlight detection, our audio features improve the performance by
more than 8% over visual features alone.
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
Authors: Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin
Date: 2017-01-04 00:00:00
Tasks: Evolutionary Algorithms, Multiobjective Optimization

Over the last three decades, a large number of evolutionary algorithms have
been developed for solving multiobjective optimization problems. However, there
lacks an up-to-date and comprehensive software platform for researchers to
properly benchmark existing algorithms and for practitioners to apply selected
algorithms to solve their real-world problems. The demand of such a common tool
becomes even more urgent, when the source code of many proposed algorithms has
not been made publicly available. To address these issues, we have developed a
MATLAB platform for evolutionary multi-objective optimization in this paper,
called PlatEMO, which includes more than 50 multi-objective evolutionary
algorithms and more than 100 multi-objective test problems, along with several
widely used performance indicators. With a user-friendly graphical user
interface, PlatEMO enables users to easily compare several evolutionary
algorithms at one time and collect statistical results in Excel or LaTeX files.
More importantly, PlatEMO is completely open source, such that users are able
to develop new algorithms on the basis of it. This paper introduces the main
features of PlatEMO and illustrates how to use it for performing comparative
experiments, embedding new algorithms, creating new test problems, and
developing performance indicators. Source code of PlatEMO is now available at:
http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
Authors: Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi, Chen, Pieter Abbeel
Date: 2017-01-03 00:00:00
Tasks: MuJoCo, Policy Gradient Methods

The high variance issue in unbiased policy-gradient methods such as VPG and
REINFORCE is typically mitigated by adding a baseline. However, the baseline
fitting itself suffers from the underfitting or the overfitting problem. In
this paper, we develop a K-fold method for baseline estimation in policy
gradient algorithms. The parameter K is the baseline estimation hyperparameter
that can adjust the bias-variance trade-off in the baseline estimates. We
demonstrate the usefulness of our approach via two state-of-the-art policy
gradient algorithms on three MuJoCo locomotive control tasks.
Unsupervised neural and Bayesian models for zero-resource speech processing
Authors: Herman Kamper
Date: 2017-01-03 00:00:00
Tasks: Clustering, Language Modelling, Representation Learning

In settings where only unlabelled speech data is available, zero-resource
speech technology needs to be developed without transcriptions, pronunciation
dictionaries, or language modelling text. There are two central problems in
zero-resource speech processing: (i) finding frame-level feature
representations which make it easier to discriminate between linguistic units
(phones or words), and (ii) segmenting and clustering unlabelled speech into
meaningful units. In this thesis, we argue that a combination of top-down and
bottom-up modelling is advantageous in tackling these two problems.
  To address the problem of frame-level representation learning, we present the
correspondence autoencoder (cAE), a neural network trained with weak top-down
supervision from an unsupervised term discovery system. By combining this
top-down supervision with unsupervised bottom-up initialization, the cAE yields
much more discriminative features than previous approaches. We then present our
unsupervised segmental Bayesian model that segments and clusters unlabelled
speech into hypothesized words. By imposing a consistent top-down segmentation
while also using bottom-up knowledge from detected syllable boundaries, our
system outperforms several others on multi-speaker conversational English and
Xitsonga speech data. Finally, we show that the clusters discovered by the
segmental Bayesian model can be made less speaker- and gender-specific by using
features from the cAE instead of traditional acoustic features.
  In summary, the different models and systems presented in this thesis show
that both top-down and bottom-up modelling can improve representation learning,
segmentation and clustering of unlabelled speech data.
Fuzzy finite element model updating using metaheuristic optimization algorithms
Authors: I. Boulkaibet, T. Marwala, M. I. Friswell, H. Haddad Khodaparast, S. Adhikari
Date: 2017-01-03 00:00:00
Tasks: Metaheuristic Optimization, Uncertainty Quantification

In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due to randomness in the parameters. This kind of aleatory
uncertainty is irreducible, and may decrease the accuracy of the finite element
model updating process. However, uncertainty quantification methods can be used
to identify the uncertainty in the updating parameters. In this paper, the
uncertainties associated with the modal parameters are defined as fuzzy
membership functions, while the model updating procedure is defined as an
optimization problem at each {\alpha}-cut level. To determine the membership
functions of the updated parameters, an objective function is defined and
minimized using two metaheuristic optimization algorithms: ant colony
optimization (ACO) and particle swarm optimization (PSO). A structural example
is used to investigate the accuracy of the fuzzy model updating strategy using
the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy
finite element model updating are compared with the Bayesian model updating
results.
Collapsing of dimensionality
Authors: Marco Gori, Marco Maggini, Alessandro Rossi
Date: 2017-01-03 00:00:00

We analyze a new approach to Machine Learning coming from a modification of
classical regularization networks by casting the process in the time dimension,
leading to a sort of collapse of dimensionality in the problem of learning the
model parameters. This approach allows the definition of a online learning
algorithm that progressively accumulates the knowledge provided in the input
trajectory. The regularization principle leads to a solution based on a
dynamical system that is paired with a procedure to develop a graph structure
that stores the input regularities acquired from the temporal evolution. We
report an extensive experimental exploration on the behavior of the parameter
of the proposed model and an evaluation on artificial dataset.
Learning a Mixture of Deep Networks for Single Image Super-Resolution
Authors: Ding Liu, Zhaowen Wang, Nasser Nasrabadi, Thomas Huang
Date: 2017-01-03 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to
recover high-resolution (HR) images from their low-resolution (LR)
observations. The crux of this problem lies in learning the complex mapping
between low-resolution patches and the corresponding high-resolution patches.
Prior arts have used either a mixture of simple regression models or a single
non-linear neural network for this propose. This paper proposes the method of
learning a mixture of SR inference modules in a unified framework to tackle
this problem. Specifically, a number of SR inference modules specialized in
different image local patterns are first independently applied on the LR image
to obtain various HR estimates, and the resultant HR estimates are adaptively
aggregated to form the final HR image. By selecting neural networks as the SR
inference module, the whole procedure can be incorporated into a unified
network and be optimized jointly. Extensive experiments are conducted to
investigate the relation between restoration performance and different network
architectures. Compared with other current image SR approaches, our proposed
method achieves state-of-the-arts restoration results on a wide range of images
consistently while allowing more flexible design choices. The source codes are
available in http://www.ifp.illinois.edu/~dingliu2/accv2016.
Condensed Memory Networks for Clinical Diagnostic Inferencing
Authors: Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek Datla, Kathy Lee, Ashequl Qadir, Joey Liu, Oladimeji Farri
Date: 2016-12-06 00:00:00
Tasks: Diagnostic

Diagnosis of a clinical condition is a challenging task, which often requires
significant medical investigation. Previous work related to diagnostic
inferencing problems mostly consider multivariate observational data (e.g.
physiological signals, lab tests etc.). In contrast, we explore the problem
using free-text medical notes recorded in an electronic health record (EHR).
Complex tasks like these can benefit from structured knowledge bases, but those
are not scalable. We instead exploit raw text from Wikipedia as a knowledge
source. Memory networks have been demonstrated to be effective in tasks which
require comprehension of free-form text. They use the final iteration of the
learned representation to predict probable classes. We introduce condensed
memory neural networks (C-MemNNs), a novel model with iterative condensation of
memory representations that preserves the hierarchy of features in the memory.
Experiments on the MIMIC-III dataset show that the proposed model outperforms
other variants of memory networks to predict the most probable diagnoses given
a complex clinical scenario.
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
Authors: Yuki Itoh, Siwei Feng, Marco F. Duarte, Mario Parente
Date: 2017-01-03 00:00:00
Tasks: Hyperspectral Unmixing

This paper proposes a new hyperspectral unmixing method for nonlinearly mixed
hyperspectral data using a semantic representation in a semi-supervised
fashion, assuming the availability of a spectral reference library. Existing
semi-supervised unmixing algorithms select members from an endmember library
that are present at each of the pixels; most such methods assume a linear
mixing model. However, those methods will fail in the presence of nonlinear
mixing among the observed spectra. To address this issue, we develop an
endmember selection method using a recently proposed semantic spectral
representation obtained via non-homogeneous hidden Markov chain (NHMC) model
for a wavelet transform of the spectra. The semantic representation can encode
spectrally discriminative features for any observed spectrum and, therefore,
our proposed method can perform endmember selection without any assumption on
the mixing model. Experimental results show that in the presence of
sufficiently nonlinear mixing our proposed method outperforms dictionary-based
sparse unmixing approaches based on linear models.
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
Authors: Dong Yu, Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen
Date: 2016-07-01 00:00:00
Tasks: Clustering, Deep Clustering, Deep Learning, regression, Speech Separation

We propose a novel deep learning model, which supports permutation invariant
training (PIT), for speaker independent multi-talker speech separation,
commonly known as the cocktail-party problem. Different from most of the prior
arts that treat speech separation as a multi-class regression problem and the
deep clustering technique that considers it a segmentation (or clustering)
problem, our model optimizes for the separation regression error, ignoring the
order of mixing sources. This strategy cleverly solves the long-lasting label
permutation problem that has prevented progress on deep learning based
techniques for speech separation. Experiments on the equal-energy mixing setup
of a Danish corpus confirms the effectiveness of PIT. We believe improvements
built upon PIT can eventually solve the cocktail-party problem and enable
real-world adoption of, e.g., automatic meeting transcription and multi-party
human-computer interaction, where overlapping speech is common.