pwc-archive/papers-with-abstracts huggingface dataset:
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Authors: Daniyal Amir Awan, Renato L. G. Cavalcante, Masahiro Yukawa, Slawomir Stanczak
Date: 2017-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.
A guide to convolution arithmetic for deep learning
Authors: Vincent Dumoulin, Francesco Visin
Date: 2016-03-23 00:00:00
Tasks: Deep Learning

We introduce a guide to help deep learning practitioners understand and
manipulate convolutional neural network architectures. The guide clarifies the
relationship between various properties (input shape, kernel shape, zero
padding, strides and output shape) of convolutional, pooling and transposed
convolutional layers, as well as the relationship between convolutional and
transposed convolutional layers. Relationships are derived for various cases,
and are illustrated in order to make them intuitive.
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Authors: Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang
Date: 2017-11-06 00:00:00
Tasks: Deep Learning, Recommendation Systems, Response Generation, Survey

Dialogue systems have attracted more and more attention. Recent advances on
dialogue systems are overwhelmingly contributed by deep learning techniques,
which have been employed to enhance a wide range of big data applications such
as computer vision, natural language processing, and recommender systems. For
dialogue systems, deep learning can leverage a massive amount of data to learn
meaningful feature representations and response generation strategies, while
requiring a minimum amount of hand-crafting. In this article, we give an
overview to these recent advances on dialogue systems from various perspectives
and discuss some possible research directions. In particular, we generally
divide existing dialogue systems into task-oriented and non-task-oriented
models, then detail how deep learning techniques help them with representative
algorithms and finally discuss some appealing research directions that can
bring the dialogue system research into a new frontier.
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
Authors: Wenshuo Wang, Junqiang Xi, Ding Zhao
Date: 2018-01-11 00:00:00
Tasks: Sensitivity, Specificity

Accurately predicting and inferring a driver's decision to brake is critical
for designing warning systems and avoiding collisions. In this paper we focus
on predicting a driver's intent to brake in car-following scenarios from a
perception-decision-action perspective according to his/her driving history. A
learning-based inference method, using onboard data from CAN-Bus, radar and
cameras as explanatory variables, is introduced to infer drivers' braking
decisions by combining a Gaussian mixture model (GMM) with a hidden Markov
model (HMM). The GMM is used to model stochastic relationships among variables,
while the HMM is applied to infer drivers' braking actions based on the GMM.
Real-case driving data from 49 drivers (more than three years' driving data per
driver on average) have been collected from the University of Michigan Safety
Pilot Model Deployment database. We compare the GMM-HMM method to a support
vector machine (SVM) method and an SVM-Bayesian filtering method. The
experimental results are evaluated by employing three performance metrics:
accuracy, sensitivity, specificity. The comparison results show that the
GMM-HMM obtains the best performance, with an accuracy of 90%, sensitivity of
84%, and specificity of 97%. Thus, we believe that this method has great
potential for real-world active safety systems.
MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning
Authors: Amith R Mamidala, Georgios Kollias, Chris Ward, Fausto Artico
Date: 2018-01-11 00:00:00

Existing Deep Learning frameworks exclusively use either Parameter Server(PS)
approach or MPI parallelism. In this paper, we discuss the drawbacks of such
approaches and propose a generic framework supporting both PS and MPI
programming paradigms, co-existing at the same time. The key advantage of the
new model is to embed the scaling benefits of MPI parallelism into the loosely
coupled PS task model. Apart from providing a practical usage model of MPI in
cloud, such framework allows for novel communication avoiding algorithms that
do parameter averaging in Stochastic Gradient Descent(SGD) approaches. We show
how MPI and PS models can synergestically apply algorithms such as Elastic SGD
to improve the rate of convergence against existing approaches. These new
algorithms directly help scaling SGD clusterwide. Further, we also optimize the
critical component of the framework, namely global aggregation or allreduce
using a novel concept of tensor collectives. These treat a group of vectors on
a node as a single object allowing for the existing single vector algorithms to
be directly applicable. We back our claims with sufficient emperical evidence
using large scale ImageNet 1K data. Our framework is built upon MXNET but the
design is generic and can be adapted to other popular DL infrastructures.
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case
Authors: Christopher K. I. Williams, Charlie Nash, Alfredo Nazábal
Date: 2018-01-11 00:00:00
Tasks: Decoder

Latent variable models can be used to probabilistically "fill-in" missing
data entries. The variational autoencoder architecture (Kingma and Welling,
2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that
infers the latent variables given the data variables. However, it is not clear
how to handle missing data variables in this network. The factor analysis (FA)
model is a basic autoencoder, using linear encoder and decoder networks. We
show how to calculate exactly the latent posterior distribution for the factor
analysis (FA) model in the presence of missing data, and note that this
solution implies that a different encoder network is required for each pattern
of missingness. We also discuss various approximations to the exact solution.
Experiments compare the effectiveness of various approaches to filling in the
missing data.
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
Authors: William Falcon, Henning Schulzrinne
Date: 2017-10-29 00:00:00
Proceeding: ICLR 2018 1

In cities with tall buildings, emergency responders need an accurate floor
level location to find 911 callers quickly. We introduce a system to estimate a
victim's floor level via their mobile device's sensor data in a two-step
process. First, we train a neural network to determine when a smartphone enters
or exits a building via GPS signal changes. Second, we use a barometer equipped
smartphone to measure the change in barometric pressure from the entrance of
the building to the victim's indoor location. Unlike impractical previous
approaches, our system is the first that does not require the use of beacons,
prior knowledge of the building infrastructure, or knowledge of user behavior.
We demonstrate real-world feasibility through 63 experiments across five
different tall buildings throughout New York City where our system predicted
the correct floor level with 100% accuracy.
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Authors: Jun Liu, Gang Wang, Ling-Yu Duan, Kamila Abdiyeva, Alex C. Kot
Date: 2017-07-18 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for action recognition, and the irrelevant joints often bring noise
which can degrade the performance, we need to pay more attention to the
informative ones. However, the original LSTM network does not have explicit
attention ability. In this paper, we propose a new class of LSTM network,
Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action
recognition. This network is capable of selectively focusing on the informative
joints in each frame of each skeleton sequence by using a global context memory
cell. To further improve the attention capability of our network, we also
introduce a recurrent attention mechanism, with which the attention performance
of the network can be enhanced progressively. Moreover, we propose a stepwise
training scheme in order to train our network effectively. Our approach
achieves state-of-the-art performance on five challenging benchmark datasets
for skeleton based action recognition.
Controlling for Unobserved Confounds in Classification Using Correlational Constraints
Authors: Virgile Landeiro, Aron Culotta
Date: 2017-03-05 00:00:00
Tasks: Classification, General Classification

As statistical classifiers become integrated into real-world applications, it
is important to consider not only their accuracy but also their robustness to
changes in the data distribution. In this paper, we consider the case where
there is an unobserved confounding variable $z$ that influences both the
features $\mathbf{x}$ and the class variable $y$. When the influence of $z$
changes from training to testing data, we find that the classifier accuracy can
degrade rapidly. In our approach, we assume that we can predict the value of
$z$ at training time with some error. The prediction for $z$ is then fed to
Pearl's back-door adjustment to build our model. Because of the attenuation
bias caused by measurement error in $z$, standard approaches to controlling for
$z$ are ineffective. In response, we propose a method to properly control for
the influence of $z$ by first estimating its relationship with the class
variable $y$, then updating predictions for $z$ to match that estimated
relationship. By adjusting the influence of $z$, we show that we can build a
model that exceeds competing baselines on accuracy as well as on robustness
over a range of confounding relationships.
Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion
Authors: Ugo Boscain, Roman Chertovskih, Jean-Paul Gauthier, Dario Prandi, Alexey Remizov
Date: 2018-01-11 00:00:00
Tasks: Image Inpainting, Image Reconstruction

In this paper we review several algorithms for image inpainting based on the
hypoelliptic diffusion naturally associated with a mathematical model of the
primary visual cortex. In particular, we present one algorithm that does not
exploit the information of where the image is corrupted, and others that do it.
While the first algorithm is able to reconstruct only images that our visual
system is still capable of recognize, we show that those of the second type
completely transcend such limitation providing reconstructions at the
state-of-the-art in image inpainting. This can be interpreted as a validation
of the fact that our visual cortex actually encodes the first type of
algorithm.
A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition
Authors: Dario Prandi, Jean-Paul Gauthier
Date: 2017-04-10 00:00:00
Tasks: Image Reconstruction

In his beautiful book [66], Jean Petitot proposes a sub-Riemannian model for
the primary visual cortex of mammals. This model is neurophysiologically
justified. Further developments of this theory lead to efficient algorithms for
image reconstruction, based upon the consideration of an associated
hypoelliptic diffusion. The sub-Riemannian model of Petitot and Citti-Sarti (or
certain of its improvements) is a left-invariant structure over the group
$SE(2)$ of rototranslations of the plane. Here, we propose a semi-discrete
version of this theory, leading to a left-invariant structure over the group
$SE(2,N)$, restricting to a finite number of rotations. This apparently very
simple group is in fact quite atypical: it is maximally almost periodic, which
leads to much simpler harmonic analysis compared to $SE(2).$ Based upon this
semi-discrete model, we improve on previous image-reconstruction algorithms and
we develop a pattern-recognition theory that leads also to very efficient
algorithms in practice.
Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence
Authors: Dirk A. Lorenz, Quoc Tran-Dinh
Date: 2018-01-11 00:00:00

We revisit the classical Douglas-Rachford (DR) method for finding a zero of
the sum of two maximal monotone operators. Since the practical performance of
the DR method crucially depends on the stepsizes, we aim at developing an
adaptive stepsize rule. To that end, we take a closer look at a linear case of
the problem and use our findings to develop a stepsize strategy that eliminates
the need for stepsize tuning. We analyze a general non-stationary DR scheme and
prove its convergence for a convergent sequence of stepsizes with summable
increments. This, in turn, proves the convergence of the method with the new
adaptive stepsize rule. We also derive the related non-stationary alternating
direction method of multipliers (ADMM) from such a non-stationary DR method. We
illustrate the efficiency of the proposed methods on several numerical
examples.
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition
Authors: Cigdem Turan, Kin-Man Lam, Xiangjian He
Date: 2018-01-11 00:00:00
Tasks: Dimensionality Reduction, Facial Expression Recognition, Facial Expression Recognition (FER)

For image recognition, an extensive number of methods have been proposed to
overcome the high-dimensionality problem of feature vectors being used. These
methods vary from unsupervised to supervised, and from statistics to
graph-theory based. In this paper, the most popular and the state-of-the-art
methods for dimensionality reduction are firstly reviewed, and then a new and
more efficient manifold-learning method, named Soft Locality Preserving Map
(SLPM), is presented. Furthermore, feature generation and sample selection are
proposed to achieve better manifold learning. SLPM is a graph-based
subspace-learning method, with the use of k-neighbourhood information and the
class information. The key feature of SLPM is that it aims to control the level
of spread of the different classes, because the spread of the classes in the
underlying manifold is closely connected to the generalizability of the learned
subspace. Our proposed manifold-learning method can be applied to various
pattern recognition applications, and we evaluate its performances on facial
expression recognition. Experiments on databases, such as the Bahcesehir
University Multilingual Affective Face Database (BAUM-2), the Extended
Cohn-Kanade (CK+) Database, the Japanese Female Facial Expression (JAFFE)
Database, and the Taiwanese Facial Expression Image Database (TFEID), show that
SLPM can effectively reduce the dimensionality of the feature vectors and
enhance the discriminative power of the extracted features for expression
recognition. Furthermore, the proposed feature-generation method can improve
the generalizability of the underlying manifolds for facial expression
recognition.
A two-dimensional decomposition approach for matrix completion through gossip
Authors: Mukul Bhutani, Bamdev Mishra
Date: 2017-11-21 00:00:00
Tasks: Matrix Completion, Vocal Bursts Valence Prediction

Factoring a matrix into two low rank matrices is at the heart of many
problems. The problem of matrix completion especially uses it to decompose a
sparse matrix into two non sparse, low rank matrices which can then be used to
predict unknown entries of the original matrix. We present a scalable and
decentralized approach in which instead of learning two factors for the
original input matrix, we decompose the original matrix into a grid blocks,
each of whose factors can be individually learned just by communicating
(gossiping) with neighboring blocks. This eliminates any need for a central
server. We show that our algorithm performs well on both synthetic and real
datasets.
Multi-Band Covariance Interpolation with Applications in Massive MIMO
Authors: Saeid Haghighatshoar, Mahdi Barzegar Khalilsarai, Giuseppe Caire
Date: 2018-01-11 00:00:00

In this paper, we study the problem of multi-band (frequency-variant)
covariance interpolation with a particular emphasis towards massive MIMO
applications. In a massive MIMO system, the communication between each BS with
$M \gg 1$ antennas and each single-antenna user occurs through a collection of
scatterers in the environment, where the channel vector of each user at BS
antennas consists in a weighted linear combination of the array responses of
the scatterers, where each scatterer has its own angle of arrival (AoA) and
complex channel gain. The array response at a given AoA depends on the
wavelength of the incoming planar wave and is naturally frequency dependent.
This results in a frequency-dependent distortion where the second order
statistics, i.e., the covariance matrix, of the channel vectors varies with
frequency. In this paper, we show that although this effect is generally
negligible for a small number of antennas $M$, it results in a considerable
distortion of the covariance matrix and especially its dominant signal subspace
in the massive MIMO regime where $M \to \infty$, and can generally incur a
serious degradation of the performance especially in frequency division
duplexing (FDD) massive MIMO systems where the uplink (UL) and the downlink
(DL) communication occur over different frequency bands. We propose a novel
UL-DL covariance interpolation technique that is able to recover the covariance
matrix in the DL from an estimate of the covariance matrix in the UL under a
mild reciprocity condition on the angular power spread function (PSF) of the
users. We analyze the performance of our proposed scheme mathematically and
prove its robustness under a sufficiently large spatial oversampling of the
array. We also propose several simple off-the-shelf algorithms for UL-DL
covariance interpolation and evaluate their performance via numerical
simulations.
Optimal sequential treatment allocation
Authors: Anders Bredahl Kock, Martin Thyrsgaard
Date: 2017-05-28 00:00:00

In treatment allocation problems the individuals to be treated often arrive
sequentially. We study a problem in which the policy maker is not only
interested in the expected cumulative welfare but is also concerned about the
uncertainty/risk of the treatment outcomes. At the outset, the total number of
treatment assignments to be made may even be unknown. A sequential treatment
policy which attains the minimax optimal regret is proposed. We also
demonstrate that the expected number of suboptimal treatments only grows slowly
in the number of treatments. Finally, we study a setting where outcomes are
only observed with delay.
Formalized Conceptual Spaces with a Geometric Representation of Correlations
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2018-01-11 00:00:00

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.
Using Deep Neural Network Approximate Bayesian Network
Authors: Jie Jia, Honggang Zhou, Yunchun Li
Date: 2017-12-31 00:00:00
Tasks: GPU

We present a new method to approximate posterior probabilities of Bayesian
Network using Deep Neural Network. Experiment results on several public
Bayesian Network datasets shows that Deep Neural Network is capable of learning
joint probability distri- bution of Bayesian Network by learning from a few
observation and posterior probability distribution pairs with high accuracy.
Compared with traditional approximate method likelihood weighting sampling
algorithm, our method is much faster and gains higher accuracy in medium sized
Bayesian Network. Another advantage of our method is that our method can be
parallelled much easier in GPU without extra effort. We also ex- plored the
connection between the accuracy of our model and the number of training
examples. The result shows that our model saturate as the number of training
examples grow and we don't need many training examples to get reasonably good
result. Another contribution of our work is that we have shown discriminative
model like Deep Neural Network can approximate generative model like Bayesian
Network.