pwc-archive/papers-with-abstracts huggingface dataset:
Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers
Authors: Xiansheng Guo, Sihua Shao, Nirwan Ansari, Abdallah Khreishah
Date: 2017-03-07 00:00:00
Tasks: Indoor Localization

A multiple classifiers fusion localization technique using received signal
strengths (RSSs) of visible light is proposed, in which the proposed system
transmits different intensity modulated sinusoidal signals by LEDs and the
signals received by a Photo Diode (PD) placed at various grid points. First, we
obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints
by capturing the peaks of power spectral density (PSD) of the received signals
at each given grid point. Unlike the existing RSSs based algorithms, several
representative machine learning approaches are adopted to train multiple
classifiers based on these RSSs fingerprints. The multiple classifiers
localization estimators outperform the classical RSS-based LED localization
approaches in accuracy and robustness. To further improve the localization
performance, two robust fusion localization algorithms, namely, grid
independent least square (GI-LS) and grid dependent least square (GD-LS), are
proposed to combine the outputs of these classifiers. We also use a singular
value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical
stability problem when the prediction matrix is singular. Experiments conducted
on intensity modulated direct detection (IM/DD) systems have demonstrated the
effectiveness of the proposed algorithms. The experimental results show that
the probability of having mean square positioning error (MSPE) of less than 5cm
achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared
to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length
of 2000.
Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment
Authors: Xiansheng Guo, Nirwan Ansari
Date: 2016-09-01 00:00:00
Tasks: Indoor Localization

Most existing fingerprints-based indoor localization approaches are based on
some single fingerprints, such as received signal strength (RSS), channel
impulse response (CIR), and signal subspace. However, the localization accuracy
obtained by the single fingerprint approach is rather susceptible to the
changing environment, multi-path, and non-line-of-sight (NLOS) propagation.
Furthermore, building the fingerprints is a very time consuming process. In
this paper, we propose a novel localization framework by Fusing A Group Of
fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first
build a GrOup Of Fingerprints (GOOF), which includes five different
fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low
order moment, and fourth-order cumulant, which are obtained by different
transformations of the received signals from multiple antennas in the offline
stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost
(GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong
multiple classifiers. In the online stage, we input the corresponding
transformations of the real measurements into these strong classifiers to
obtain independent decisions. Finally, we propose an efficient combination
fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion
algorithm to improve the accuracy of localization by combining the predictions
of multiple classifiers with different samples. As compared with the single
fingerprint approaches, the prediction probability of our proposed approach is
improved significantly. The process for building fingerprints can also be
reduced drastically. We demonstrate the feasibility and performance of the
proposed algorithm through extensive simulations as well as via real
experimental data using a Universal Software Radio Peripheral (USRP) platform
with four antennas.
Order-Free RNN with Visual Attention for Multi-Label Classification
Authors: Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang
Date: 2017-07-18 00:00:00
Tasks: Classification, General Classification, Image Captioning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, we propose the joint learning attention and recurrent neural
network (RNN) models for multi-label classification. While approaches based on
the use of either model exist (e.g., for the task of image captioning),
training such existing network architectures typically require pre-defined
label sequences. For multi-label classification, it would be desirable to have
a robust inference process, so that the prediction error would not propagate
and thus affect the performance. Our proposed model uniquely integrates
attention and Long Short Term Memory (LSTM) models, which not only addresses
the above problem but also allows one to identify visual objects of interests
with varying sizes without the prior knowledge of particular label ordering.
More importantly, label co-occurrence information can be jointly exploited by
our LSTM model. Finally, by advancing the technique of beam search, prediction
of multiple labels can be efficiently achieved by our proposed network model.
On the Diversity of Realistic Image Synthesis
Authors: Zichen Yang, Haifeng Liu, Deng Cai
Date: 2017-12-20 00:00:00
Tasks: Colorization, Diversity, Image Generation, Super-Resolution

Many image processing tasks can be formulated as translating images between
two image domains, such as colorization, super resolution and conditional image
synthesis. In most of these tasks, an input image may correspond to multiple
outputs. However, current existing approaches only show very minor diversity of
the outputs. In this paper, we present a novel approach to synthesize diverse
realistic images corresponding to a semantic layout. We introduce a diversity
loss objective, which maximizes the distance between synthesized image pairs
and links the input noise to the semantic segments in the synthesized images.
Thus, our approach can not only produce diverse images, but also allow users to
manipulate the output images by adjusting the noise manually. Experimental
results show that images synthesized by our approach are significantly more
diverse than that of the current existing works and equipping our diversity
loss does not degrade the reality of the base networks.
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models
Authors: Kevin H. Lee, Lingzhou Xue, David R. Hunter
Date: 2017-12-20 00:00:00
Tasks: Clustering, Model Selection

Dynamic networks are a general language for describing time-evolving complex
systems, and discrete time network models provide an emerging statistical
technique for various applications. It is a fundamental research question to
detect the community structure in time-evolving networks. However, due to
significant computational challenges and difficulties in modeling communities
of time-evolving networks, there is little progress in the current literature
to effectively find communities in time-evolving networks. In this work, we
propose a novel model-based clustering framework for time-evolving networks
based on discrete time exponential-family random graph models. To choose the
number of communities, we use conditional likelihood to construct an effective
model selection criterion. Furthermore, we propose an efficient variational
expectation-maximization (EM) algorithm to find approximate maximum likelihood
estimates of network parameters and mixing proportions. By using variational
methods and minorization-maximization (MM) techniques, our method has appealing
scalability for large-scale time-evolving networks. The power of our method is
demonstrated in simulation studies and empirical applications to international
trade networks and the collaboration networks of a large American research
university.
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Authors: Huai-Qian Khor, John See
Date: 2017-12-20 00:00:00
Tasks: Descriptive, object-detection, Object Detection, Time Series, Time Series Analysis

Video surveillance is a well researched area of study with substantial work
done in the aspects of object detection, tracking and behavior analysis. With
the abundance of video data captured over a long period of time, we can
understand patterns in human behavior and scene dynamics through data-driven
temporal analytics. In this work, we propose two schemes to perform descriptive
and predictive analytics on long-term video surveillance data. We generate
heatmap and footmap visualizations to describe spatially pooled trajectory
patterns with respect to time and location. We also present two approaches for
anomaly prediction at the day-level granularity: a trajectory-based statistical
approach, and a time-series based approach. Experimentation with one year data
from a single camera demonstrates the ability to uncover interesting insights
about the scene and to predict anomalies reasonably well.
A Flexible Approach to Automated RNN Architecture Generation
Authors: Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher
Date: 2017-12-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling, Machine Translation, Neural Architecture Search, Reinforcement Learning, Translation

The process of designing neural architectures requires expert knowledge and
extensive trial and error. While automated architecture search may simplify
these requirements, the recurrent neural network (RNN) architectures generated
by existing methods are limited in both flexibility and components. We propose
a domain-specific language (DSL) for use in automated architecture search which
can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough
to define standard architectures such as the Gated Recurrent Unit and Long
Short Term Memory and allows the introduction of non-standard RNN components
such as trigonometric curves and layer normalization. Using two different
candidate generation techniques, random search with a ranking function and
reinforcement learning, we explore the novel architectures produced by the RNN
DSL for language modeling and machine translation domains. The resulting
architectures do not follow human intuition yet perform well on their targeted
tasks, suggesting the space of usable RNN architectures is far larger than
previously assumed.
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Authors: Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer
Date: 2017-12-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Two-dimensional embeddings remain the dominant approach to visualize high
dimensional data. The choice of embeddings ranges from highly non-linear ones,
which can capture complex relationships but are difficult to interpret
quantitatively, to axis-aligned projections, which are easy to interpret but
are limited to bivariate relationships. Linear project can be considered as a
compromise between complexity and interpretability, as they allow explicit axes
labels, yet provide significantly more degrees of freedom compared to
axis-aligned projections. Nevertheless, interpreting the axes directions, which
are linear combinations often with many non-trivial components, remains
difficult. To address this problem we introduce a structure aware decomposition
of (multiple) linear projections into sparse sets of axis aligned projections,
which jointly capture all information of the original linear ones. In
particular, we use tools from Dempster-Shafer theory to formally define how
relevant a given axis aligned project is to explain the neighborhood relations
displayed in some linear projection. Furthermore, we introduce a new approach
to discover a diverse set of high quality linear projections and show that in
practice the information of $k$ linear projections is often jointly encoded in
$\sim k$ axis aligned plots. We have integrated these ideas into an interactive
visualization system that allows users to jointly browse both linear
projections and their axis aligned representatives. Using a number of case
studies we show how the resulting plots lead to more intuitive visualizations
and new insight.
Detection and classification of masses in mammographic images in a multi-kernel approach
Authors: Sidney Marlon Lopes de Lima, Abel Guilhermino da Silva Filho, Wellington Pinheiro dos Santos
Date: 2017-12-20 00:00:00
Tasks: Diagnostic, General Classification

According to the World Health Organization, breast cancer is the main cause
of cancer death among adult women in the world. Although breast cancer occurs
indiscriminately in countries with several degrees of social and economic
development, among developing and underdevelopment countries mortality rates
are still high, due to low availability of early detection technologies. From
the clinical point of view, mammography is still the most effective diagnostic
technology, given the wide diffusion of the use and interpretation of these
images. Herein this work we propose a method to detect and classify
mammographic lesions using the regions of interest of images. Our proposal
consists in decomposing each image using multi-resolution wavelets. Zernike
moments are extracted from each wavelet component. Using this approach we can
combine both texture and shape features, which can be applied both to the
detection and classification of mammary lesions. We used 355 images of fatty
breast tissue of IRMA database, with 233 normal instances (no lesion), 72
benign, and 83 malignant cases. Classification was performed by using SVM and
ELM networks with modified kernels, in order to optimize accuracy rates,
reaching 94.11%. Considering both accuracy rates and training times, we defined
the ration between average percentage accuracy and average training time in a
reverse order. Our proposal was 50 times higher than the ratio obtained using
the best method of the state-of-the-art. As our proposed model can combine high
accuracy rate with low learning time, whenever a new data is received, our work
will be able to save a lot of time, hours, in learning process in relation to
the best method of the state-of-the-art.
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images
Authors: Filipe Rolim Cordeiro, Wellington Pinheiro dos Santos, Abel Guilhermino da Silva Filho
Date: 2017-12-20 00:00:00
Tasks: Diagnostic, Segmentation

Breast cancer is already one of the most common form of cancer worldwide.
Mammography image analysis is still the most effective diagnostic method to
promote the early detection of breast cancer. Accurately segmenting tumors in
digital mammography images is important to improve diagnosis capabilities of
health specialists and avoid misdiagnosis. In this work, we evaluate the
feasibility of applying GrowCut to segment regions of tumor and we propose two
GrowCut semi-supervised versions. All the analysis was performed by evaluating
the application of segmentation techniques to a set of images obtained from the
Mini-MIAS mammography image database. GrowCut segmentation was compared to
Region Growing, Active Contours, Random Walks and Graph Cut techniques.
Experiments showed that GrowCut, when compared to the other techniques, was
able to acquire better results for the metrics analyzed. Moreover, the proposed
semi-supervised versions of GrowCut was proved to have a clinically
satisfactory quality of segmentation.
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Authors: Xiangyu Kong, Bo Xin, Fangchen Liu, Yizhou Wang
Date: 2017-12-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

Many tasks in artificial intelligence require the collaboration of multiple
agents. We exam deep reinforcement learning for multi-agent domains. Recent
research efforts often take the form of two seemingly conflicting perspectives,
the decentralized perspective, where each agent is supposed to have its own
controller; and the centralized perspective, where one assumes there is a
larger model controlling all agents. In this regard, we revisit the idea of the
master-slave architecture by incorporating both perspectives within one
framework. Such a hierarchical structure naturally leverages advantages from
one another. The idea of combining both perspectives is intuitive and can be
well motivated from many real world systems, however, out of a variety of
possible realizations, we highlights three key ingredients, i.e. composed
action representation, learnable communication and independent reasoning. With
network designs to facilitate these explicitly, our proposal consistently
outperforms latest competing methods both in synthetic experiments and when
applied to challenging StarCraft micromanagement tasks.
Block-diagonal Hessian-free Optimization for Training Neural Networks
Authors: Huishuai Zhang, Caiming Xiong, James Bradbury, Richard Socher
Date: 2017-12-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Second-order methods

Second-order methods for neural network optimization have several advantages
over methods based on first-order gradient descent, including better scaling to
large mini-batch sizes and fewer updates needed for convergence. But they are
rarely applied to deep learning in practice because of high computational cost
and the need for model-dependent algorithmic variations. We introduce a variant
of the Hessian-free method that leverages a block-diagonal approximation of the
generalized Gauss-Newton matrix. Our method computes the curvature
approximation matrix only for pairs of parameters from the same layer or block
of the neural network and performs conjugate gradient updates independently for
each block. Experiments on deep autoencoders, deep convolutional networks, and
multilayer LSTMs demonstrate better convergence and generalization compared to
the original Hessian-free approach and the Adam method.
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
Authors: Tianmin Shu, Caiming Xiong, Richard Socher
Date: 2017-12-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Minecraft, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning policies for complex tasks that require multiple different skills is
a major challenge in reinforcement learning (RL). It is also a requirement for
its deployment in real-world scenarios. This paper proposes a novel framework
for efficient multi-task reinforcement learning. Our framework trains agents to
employ hierarchical policies that decide when to use a previously learned
policy and when to learn a new skill. This enables agents to continually
acquire new skills during different stages of training. Each learned task
corresponds to a human language description. Because agents can only access
previously learned skills through these descriptions, the agent can always
provide a human-interpretable description of its choices. In order to help the
agent learn the complex temporal dependencies necessary for the hierarchical
policy, we provide it with a stochastic temporal grammar that modulates when to
rely on previously learned skills and when to execute new skills. We validate
our approach on Minecraft games designed to explicitly test the ability to
reuse previously learned skills while simultaneously learning new skills.
LVreID: Person Re-Identification with Long Sequence Videos
Authors: Jianing Li, Shiliang Zhang, Jingdong Wang, Wen Gao, Qi Tian
Date: 2017-12-20 00:00:00
Tasks: Person Re-Identification

This paper mainly establishes a large-scale Long sequence Video database for
person re-IDentification (LVreID).
Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning
Authors: Andrew Owens, Jiajun Wu, Josh H. McDermott, William T. Freeman, Antonio Torralba
Date: 2017-12-20 00:00:00

The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a statistical summary of the sound associated with a video frame. We
show that, through this process, the network learns a representation that
conveys information about objects and scenes. We evaluate this representation
on several recognition tasks, finding that its performance is comparable to
that of other state-of-the-art unsupervised learning methods. Finally, we show
through visualizations that the network learns units that are selective to
objects that are often associated with characteristic sounds. This paper
extends an earlier conference paper, Owens et al. 2016, with additional
experiments and discussion.
Audio to Body Dynamics
Authors: Eli Shlizerman, Lucio M. Dery, Hayden Schoen, Ira Kemelmacher-Shlizerman
Date: 2017-12-19 00:00:00
Proceeding: CVPR 2018 6

We present a method that gets as input an audio of violin or piano playing,
and outputs a video of skeleton predictions which are further used to animate
an avatar. The key idea is to create an animation of an avatar that moves their
hands similarly to how a pianist or violinist would do, just from audio. Aiming
for a fully detailed correct arms and fingers motion is a goal, however, it's
not clear if body movement can be predicted from music at all. In this paper,
we present the first result that shows that natural body dynamics can be
predicted at all. We built an LSTM network that is trained on violin and piano
recital videos uploaded to the Internet. The predicted points are applied onto
a rigged avatar to create the animation.
Tag Prediction at Flickr: a View from the Darkroom
Authors: Kofi Boakye, Sachin Farfade, Hamid Izadinia, Yannis Kalantidis, Pierre Garrigues
Date: 2016-12-06 00:00:00
Tasks: Prediction, TAG

Automated photo tagging has established itself as one of the most compelling
applications of deep learning. While deep convolutional neural networks have
repeatedly demonstrated top performance on standard datasets for
classification, there are a number of often overlooked but important
considerations when deploying this technology in a real-world scenario. In this
paper, we present our efforts in developing a large-scale photo tagging system
for Flickr photo search. We discuss topics including how to 1) select the tags
that matter most to our users; 2) develop lightweight, high-performance models
for tag prediction; and 3) leverage the power of large amounts of noisy data
for training. Our results demonstrate that, for real-world datasets, training
exclusively with this noisy data yields performance on par with the standard
paradigm of first pre-training on clean data and then fine-tuning. In addition,
we observe that the models trained with user-generated data can yield better
fine-tuning results when a small amount of clean data is available. As such, we
advocate for the approach of harnessing user-generated data in large-scale
systems.
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
Authors: João Silvério, Yanlong Huang, Leonel Rozo, Sylvain Calinon, Darwin G. Caldwell
Date: 2017-12-19 00:00:00

When learning skills from demonstrations, one is often required to think in
advance about the appropriate task representation (usually in either
operational or configuration space). We here propose a probabilistic approach
for simultaneously learning and synthesizing torque control commands which take
into account task space, joint space and force constraints. We treat the
problem by considering different torque controllers acting on the robot, whose
relevance is learned probabilistically from demonstrations. This information is
used to combine the controllers by exploiting the properties of Gaussian
distributions, generating new torque commands that satisfy the important
features of the task. We validate the approach in two experimental scenarios
using 7-DoF torquecontrolled manipulators, with tasks that require the
consideration of different controllers to be properly executed.