pwc-archive/papers-with-abstracts huggingface dataset:
Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations
Authors: Syed A. W. Shah, Karim Abed-Meraim, Tareq Y. Al-Naffouri
Date: 2015-06-22 00:00:00
Tasks: blind source separation

This paper addresses the problem of blind demixing of instantaneous mixtures
in a multiple-input multiple-output communication system. The main objective is
to present efficient blind source separation (BSS) algorithms dedicated to
moderate or high-order QAM constellations. Four new iterative batch BSS
algorithms are presented dealing with the multimodulus (MM) and alphabet
matched (AM) criteria. For the optimization of these cost functions, iterative
methods of Givens and hyperbolic rotations are used. A pre-whitening operation
is also utilized to reduce the complexity of design problem. It is noticed that
the designed algorithms using Givens rotations gives satisfactory performance
only for large number of samples. However, for small number of samples, the
algorithms designed by combining both Givens and hyperbolic rotations
compensate for the ill-whitening that occurs in this case and thus improves the
performance. Two algorithms dealing with the MM criterion are presented for
moderate order QAM signals such as 16-QAM. The other two dealing with the AM
criterion are presented for high-order QAM signals. These methods are finally
compared with the state of art batch BSS algorithms in terms of
signal-to-interference and noise ratio, symbol error rate and convergence rate.
Simulation results show that the proposed methods outperform the contemporary
batch BSS algorithms.
Small coherence implies the weak Null Space Property
Authors: Stéphane Chrétien, Zhen Wai Olivier Ho
Date: 2016-06-29 00:00:00
Tasks: compressed sensing

In the Compressed Sensing community, it is well known that given a matrix $X
\in \mathbb R^{n\times p}$ with $\ell_2$ normalized columns, the Restricted
Isometry Property (RIP) implies the Null Space Property (NSP). It is also well
known that a small Coherence $\mu$ implies a weak RIP, i.e. the singular values
of $X_T$ lie between $1-\delta$ and $1+\delta$ for "most" index subsets $T
\subset \{1,\ldots,p\}$ with size governed by $\mu$ and $\delta$. In this short
note, we show that a small Coherence implies a weak Null Space Property, i.e.
$\Vert h_T\Vert_2 \le C \ \Vert h_{T^c}\Vert_1/\sqrt{s}$ for most $T \subset
\{1,\ldots,p\}$ with cardinality $|T|\le s$. We moreover prove some singular
value perturbation bounds that may also prove useful for other applications.
A Semi-Definite Programming approach to low dimensional embedding for unsupervised clustering
Authors: Stéphane Chrétien, Clément Dombry, Adrien Faivre
Date: 2016-06-29 00:00:00
Tasks: Clustering

This paper proposes a variant of the method of Gu\'edon and Verhynin for
estimating the cluster matrix in the Mixture of Gaussians framework via
Semi-Definite Programming. A clustering oriented embedding is deduced from this
estimate. The procedure is suitable for very high dimensional data because it
is based on pairwise distances only. Theoretical garantees are provided and an
eigenvalue optimisation approach is proposed for computing the embedding. The
performance of the method is illustrated via Monte Carlo experiements and
comparisons with other embeddings from the literature.
Disease Trajectory Maps
Authors: Peter Schulam, Raman Arora
Date: 2016-06-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Time Series, Time Series Analysis, Variational Inference

Medical researchers are coming to appreciate that many diseases are in fact
complex, heterogeneous syndromes composed of subpopulations that express
different variants of a related complication. Time series data extracted from
individual electronic health records (EHR) offer an exciting new way to study
subtle differences in the way these diseases progress over time. In this paper,
we focus on answering two questions that can be asked using these databases of
time series. First, we want to understand whether there are individuals with
similar disease trajectories and whether there are a small number of degrees of
freedom that account for differences in trajectories across the population.
Second, we want to understand how important clinical outcomes are associated
with disease trajectories. To answer these questions, we propose the Disease
Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional
representations of sparse and irregularly sampled time series. We propose a
stochastic variational inference algorithm for learning the DTM that allows the
model to scale to large modern medical datasets. To demonstrate the DTM, we
analyze data collected on patients with the complex autoimmune disease,
scleroderma. We find that DTM learns meaningful representations of disease
trajectories and that the representations are significantly associated with
important clinical outcomes.
Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
Authors: Akash Kumar Dhaka, Giampiero Salvi
Date: 2016-06-29 00:00:00
Tasks: Low-latency processing, Phoneme Recognition

We present a systematic analysis on the performance of a phonetic recogniser
when the window of input features is not symmetric with respect to the current
frame. The recogniser is based on Context Dependent Deep Neural Networks
(CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the
latency of the system by reducing the number of future feature frames required
to estimate the current output. Our tests performed on the TIMIT database show
that the performance does not degrade when the input window is shifted up to 5
frames in the past compared to common practice (no future frame). This
corresponds to improving the latency by 50 ms in our settings. Our tests also
show that the best results are not obtained with the symmetric window commonly
employed, but with an asymmetric window with eight past and two future context
frames, although this observation should be confirmed on other data sets. The
reduction in latency suggested by our results is critical for specific
applications such as real-time lip synchronisation for tele-presence, but may
also be beneficial in general applications to improve the lag in human-machine
spoken interaction.
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming
Authors: Yangyang Xu
Date: 2016-06-29 00:00:00
Tasks: Denoising, Image Denoising

Motivated by big data applications, first-order methods have been extremely
popular in recent years. However, naive gradient methods generally converge
slowly. Hence, much efforts have been made to accelerate various first-order
methods. This paper proposes two accelerated methods towards solving structured
linearly constrained convex programming, for which we assume composite convex
objective.
  The first method is the accelerated linearized augmented Lagrangian method
(LALM). At each update to the primal variable, it allows linearization to the
differentiable function and also the augmented term, and thus it enables easy
subproblems. Assuming merely weak convexity, we show that LALM owns $O(1/t)$
convergence if parameters are kept fixed during all the iterations and can be
accelerated to $O(1/t^2)$ if the parameters are adapted, where $t$ is the
number of total iterations.
  The second method is the accelerated linearized alternating direction method
of multipliers (LADMM). In addition to the composite convexity, it further
assumes two-block structure on the objective. Different from classic ADMM, our
method allows linearization to the objective and also augmented term to make
the update simple. Assuming strong convexity on one block variable, we show
that LADMM also enjoys $O(1/t^2)$ convergence with adaptive parameters. This
result is a significant improvement over that in [Goldstein et. al, SIIMS'14],
which requires strong convexity on both block variables and no linearization to
the objective or augmented term.
  Numerical experiments are performed on quadratic programming, image
denoising, and support vector machine. The proposed accelerated methods are
compared to nonaccelerated ones and also existing accelerated methods. The
results demonstrate the validness of acceleration and superior performance of
the proposed methods over existing ones.
A spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging
Authors: Robert Amelard, David A. Clausi, Alexander Wong
Date: 2016-06-29 00:00:00

Photoplethysmographic imaging is a camera-based solution for non-contact
cardiovascular monitoring from a distance. This technology enables monitoring
in situations where contact-based devices may be problematic or infeasible,
such as ambulatory, sleep, and multi-individual monitoring. However, extracting
the blood pulse waveform signal is challenging due to the unknown mixture of
relevant (pulsatile) and irrelevant pixels in the scene. Here, we design and
implement a signal fusion framework, FusionPPG, for extracting a blood pulse
waveform signal with strong temporal fidelity from a scene without requiring
anatomical priors (e.g., facial tracking). The extraction problem is posed as a
Bayesian least squares fusion problem, and solved using a novel probabilistic
pulsatility model that incorporates both physiologically derived spectral and
spatial waveform priors to identify pulsatility characteristics in the scene.
Experimental results show statistically significantly improvements compared to
the FaceMeanPPG method ($p<0.001$) and DistancePPG ($p<0.001$) methods. Heart
rates predicted using FusionPPG correlated strongly with ground truth
measurements ($r^2=0.9952$). FusionPPG was the only method able to assess
cardiac arrhythmia via temporal analysis.
Mapping Heritability of Large-Scale Brain Networks with a Billion Connections {\em via} Persistent Homology
Authors: Moo. K. Chung, Victoria Vilalta-Gil, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
Date: 2015-09-15 00:00:00

In many human brain network studies, we do not have sufficient number (n) of
images relative to the number (p) of voxels due to the prohibitively expensive
cost of scanning enough subjects. Thus, brain network models usually suffer the
small-n large-p problem. Such a problem is often remedied by sparse network
models, which are usually solved numerically by optimizing L1-penalties.
Unfortunately, due to the computational bottleneck associated with optimizing
L1-penalties, it is not practical to apply such methods to construct
large-scale brain networks at the voxel-level. In this paper, we propose a new
scalable sparse network model using cross-correlations that bypass the
computational bottleneck. Our model can build sparse brain networks at the
voxel level with p > 25000. Instead of using a single sparse parameter that may
not be optimal in other studies and datasets, the computational speed gain
enables us to analyze the collection of networks at every possible sparse
parameter in a coherent mathematical framework via persistent homology. The
method is subsequently applied in determining the extent of heritability on a
functional brain network at the voxel-level for the first time using twin fMRI.
A Distributional Semantics Approach to Implicit Language Learning
Authors: Dimitrios Alikaniotis, John N. Williams
Date: 2016-06-29 00:00:00

In the present paper we show that distributional information is particularly
important when considering concept availability under implicit language
learning conditions. Based on results from different behavioural experiments we
argue that the implicit learnability of semantic regularities depends on the
degree to which the relevant concept is reflected in language use. In our
simulations, we train a Vector-Space model on either an English or a Chinese
corpus and then feed the resulting representations to a feed-forward neural
network. The task of the neural network was to find a mapping between the word
representations and the novel words. Using datasets from four behavioural
experiments, which used different semantic manipulations, we were able to
obtain learning patterns very similar to those obtained by humans.
Theory reconstruction: a representation learning view on predicate invention
Authors: Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel
Date: 2016-06-28 00:00:00
Tasks: Representation Learning

With this positional paper we present a representation learning view on
predicate invention. The intention of this proposal is to bridge the relational
and deep learning communities on the problem of predicate invention. We propose
a theory reconstruction approach, a formalism that extends autoencoder approach
to representation learning to the relational settings. Our intention is to
start a discussion to define a unifying framework for predicate invention and
theory revision.
A Single-Pass Classifier for Categorical Data
Authors: Kieran Greer
Date: 2015-03-09 00:00:00

This paper describes a new method for classifying a dataset that partitions
elements into their categories. It has relations with neural networks but a
slightly different structure, requiring only a single pass through the
classifier to generate the weight sets. A grid-like structure is required as
part of a novel idea of converting a 1-D row of real values into a 2-D
structure of value bands. Each cell in any band then stores a distinct set of
weights, to represent its own importance and its relation to each output
category. During classification, all of the output weight lists can be
retrieved and summed to produce a probability for what the correct output
category is. The bands possibly work like hidden layers of neurons, but they
are variable specific, making the process orthogonal. The construction process
can be a single update process without iterations, making it potentially much
faster. It can also be compared with k-NN and may be practical for partial or
competitive updating.
Modeling meaning: computational interpreting and understanding of natural language fragments
Authors: Michael Kapustin, Pavlo Kapustin
Date: 2015-05-29 00:00:00
Tasks: Natural Language Understanding

In this introductory article we present the basics of an approach to implementing computational interpreting of natural language aiming to model the meanings of words and phrases. Unlike other approaches, we attempt to define the meanings of text fragments in a composable and computer interpretable way. We discuss models and ideas for detecting different types of semantic incomprehension and choosing the interpretation that makes most sense in a given context. Knowledge representation is designed for handling context-sensitive and uncertain / imprecise knowledge, and for easy accommodation of new information. It stores quantitative information capturing the essence of the concepts, because it is crucial for working with natural language understanding and reasoning. Still, the representation is general enough to allow for new knowledge to be learned, and even generated by the system. The article concludes by discussing some reasoning-related topics: possible approaches to generation of new abstract concepts, and describing situations and concepts in words (e.g. for specifying interpretation difficulties).
Theta-RBM: Unfactored Gated Restricted Boltzmann Machine for Rotation-Invariant Representations
Authors: Mario Valerio Giuffrida, Sotirios A. Tsaftaris
Date: 2016-06-28 00:00:00

Learning invariant representations is a critical task in computer vision. In
this paper, we propose the Theta-Restricted Boltzmann Machine ({\theta}-RBM in
short), which builds upon the original RBM formulation and injects the notion
of rotation-invariance during the learning procedure. In contrast to previous
approaches, we do not transform the training set with all possible rotations.
Instead, we rotate the gradient filters when they are computed during the
Contrastive Divergence algorithm. We formulate our model as an unfactored gated
Boltzmann machine, where another input layer is used to modulate the input
visible layer to drive the optimisation procedure. Among our contributions is a
mathematical proof that demonstrates that {\theta}-RBM is able to learn
rotation-invariant features according to a recently proposed invariance
measure. Our method reaches an invariance score of ~90% on mnist-rot dataset,
which is the highest result compared with the baseline methods and the current
state of the art in transformation-invariant feature learning in RBM. Using an
SVM classifier, we also showed that our network learns discriminative features
as well, obtaining ~10% of testing error.
Decision making via semi-supervised machine learning techniques
Authors: Eftychios Protopapadakis
Date: 2016-06-29 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Semi-supervised learning (SSL) is a class of supervised learning tasks and
techniques that also exploits the unlabeled data for training. SSL
significantly reduces labeling related costs and is able to handle large data
sets. The primary objective is the extraction of robust inference rules.
Decision support systems (DSSs) who utilize SSL have significant advantages.
Only a small amount of labelled data is required for the initialization. Then,
new (unlabeled) data can be utilized and improve system's performance. Thus,
the DSS is continuously adopted to new conditions, with minimum effort.
Techniques which are cost effective and easily adopted to dynamic systems, can
be beneficial for many practical applications. Such applications fields are:
(a) industrial assembly lines monitoring, (b) sea border surveillance, (c)
elders' falls detection, (d) transportation tunnels inspection, (e) concrete
foundation piles defect recognition, (f) commercial sector companies financial
assessment and (g) image advanced filtering for cultural heritage applications.
De-Hashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search
Authors: Yin-Hsi Kuo, Winston H. Hsu
Date: 2016-06-29 00:00:00
Tasks: Retrieval, Video Retrieval

Due to the prevalence of mobile devices, mobile search becomes a more
convenient way than desktop search. Different from the traditional desktop
search, mobile visual search needs more consideration for the limited resources
on mobile devices (e.g., bandwidth, computing power, and memory consumption).
The state-of-the-art approaches show that bag-of-words (BoW) model is robust
for image and video retrieval; however, the large vocabulary tree might not be
able to be loaded on the mobile device. We observe that recent works mainly
focus on designing compact feature representations on mobile devices for
bandwidth-limited network (e.g., 3G) and directly adopt feature matching on
remote servers (cloud). However, the compact (binary) representation might fail
to retrieve target objects (images, videos). Based on the hashed binary codes,
we propose a de-hashing process that reconstructs BoW by leveraging the
computing power of remote servers. To mitigate the information loss from binary
codes, we further utilize contextual information (e.g., GPS) to reconstruct a
context-aware BoW for better retrieval results. Experiment results show that
the proposed method can achieve competitive retrieval accuracy as BoW while
only transmitting few bits from mobile devices.
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
Authors: Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Narayan Bhamidipati, Slobodan Vucetic
Date: 2016-06-29 00:00:00
Tasks: Retrieval

We consider the problem of personalization of online services from the
viewpoint of ad targeting, where we seek to find the best ad categories to be
shown to each user, resulting in improved user experience and increased
advertisers' revenue. We propose to address this problem as a task of ranking
the ad categories depending on a user's preference, and introduce a novel label
ranking approach capable of efficiently learning non-linear, highly accurate
models in large-scale settings. Experiments on a real-world advertising data
set with more than 3.2 million users show that the proposed algorithm
outperforms the existing solutions in terms of both rank loss and top-K
retrieval performance, strongly suggesting the benefit of using the proposed
model on large-scale ranking problems.
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Authors: Sheng Chen, Arindam Banerjee
Date: 2016-06-29 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Vocal Bursts Intensity Prediction

We consider learning high-dimensional multi-response linear models with
structured parameters. By exploiting the noise correlations among responses, we
propose an alternating estimation (AltEst) procedure to estimate the model
parameters based on the generalized Dantzig selector. Under suitable sample
size and resampling assumptions, we show that the error of the estimates
generated by AltEst, with high probability, converges linearly to certain
minimum achievable level, which can be tersely expressed by a few geometric
measures, such as Gaussian width of sets related to the parameter structure. To
the best of our knowledge, this is the first non-asymptotic statistical
guarantee for such AltEst-type algorithm applied to estimation problem with
general structures.
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
Authors: Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A. Smith
Date: 2016-06-29 00:00:00
Proceeding: CONLL 2016 8
Tasks: Semantic Parsing

We present a transition-based parser that jointly produces syntactic and
semantic dependencies. It learns a representation of the entire algorithm
state, using stack long short-term memories. Our greedy inference algorithm has
linear time, including feature extraction. On the CoNLL 2008--9 English shared
tasks, we obtain the best published parsing performance among models that
jointly learn syntax and semantics.
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
Authors: Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, Santhoshkumar Saminathan
Date: 2016-06-29 00:00:00
Tasks: Clustering, Community Detection, Graph Classification, Link Prediction, Malware Detection

In this paper, we present subgraph2vec, a novel approach for learning latent
representations of rooted subgraphs from large graphs inspired by recent
advancements in Deep Learning and Graph Kernels. These latent representations
encode semantic substructure dependencies in a continuous vector space, which
is easily exploited by statistical models for tasks such as graph
classification, clustering, link prediction and community detection.
subgraph2vec leverages on local information obtained from neighbourhoods of
nodes to learn their latent representations in an unsupervised fashion. We
demonstrate that subgraph vectors learnt by our approach could be used in
conjunction with classifiers such as CNNs, SVMs and relational data clustering
algorithms to achieve significantly superior accuracies. Also, we show that the
subgraph vectors could be used for building a deep learning variant of
Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and
large-scale real-world datasets reveal that subgraph2vec achieves significant
improvements in accuracies over existing graph kernels on both supervised and
unsupervised learning tasks. Specifically, on two realworld program analysis
tasks, namely, code clone and malware detection, subgraph2vec outperforms
state-of-the-art kernels by more than 17% and 4%, respectively.