pwc-archive/papers-with-abstracts huggingface dataset:
Jansen-MIDAS: a multi-level photomicrograph segmentation software based on isotropic undecimated wavelets
Authors: Alexandre Fioravante de Siqueira, Flávio Camargo Cabrera, Wagner Massayuki Nakasuga, Aylton Pagamisse, Aldo Eloizo Job
Date: 2016-04-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation, the process of separating the elements within an image,
is frequently used for obtaining information from photomicrographs. However,
segmentation methods should be used with reservations: incorrect segmentation
can mislead when interpreting regions of interest (ROI), thus decreasing the
success rate of additional procedures. Multi-Level Starlet Segmentation (MLSS)
and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to address
the photomicrograph segmentation deficiency on general tools. These methods
gave rise to Jansen-MIDAS, an open-source software which a scientist can use to
obtain a multi-level threshold segmentation of his/hers photomicrographs. This
software is presented in two versions: a text-based version, for GNU Octave,
and a graphical user interface (GUI) version, for MathWorks MATLAB. It can be
used to process several types of images, becoming a reliable alternative to the
scientist.
Geometry-aware stationary subspace analysis
Authors: Inbal Horev, Florian Yger, Masashi Sugiyama
Date: 2016-05-25 00:00:00
Tasks: Brain Computer Interface

In many real-world applications data exhibits non-stationarity, i.e., its
distribution changes over time. One approach to handling non-stationarity is to
remove or minimize it before attempting to analyze the data. In the context of
brain computer interface (BCI) data analysis this may be done by means of
stationary subspace analysis (SSA). The classic SSA method finds a matrix that
projects the data onto a stationary subspace by optimizing a cost function
based on a matrix divergence. In this work we present an alternative method for
SSA based on a symmetrized version of this matrix divergence. We show that this
frames the problem in terms of distances between symmetric positive definite
(SPD) matrices, suggesting a geometric interpretation of the problem. Stemming
from this geometric viewpoint, we introduce and analyze a method which utilizes
the geometry of the SPD matrix manifold and the invariance properties of its
metrics. Most notably we show that these invariances alleviate the need to
whiten the input matrices, a common step in many SSA methods which often
introduces errors. We demonstrate the usefulness of our technique in
experiments on both synthesized and real-world data.
Preconditioning Kernel Matrices
Authors: Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone
Date: 2016-02-22 00:00:00

The computational and storage complexity of kernel machines presents the
primary barrier to their scaling to large, modern, datasets. A common way to
tackle the scalability issue is to use the conjugate gradient algorithm, which
relieves the constraints on both storage (the kernel matrix need not be stored)
and computation (both stochastic gradients and parallelization can be used).
Even so, conjugate gradient is not without its own issues: the conditioning of
kernel matrices is often such that conjugate gradients will have poor
convergence in practice. Preconditioning is a common approach to alleviating
this issue. Here we propose preconditioned conjugate gradients for kernel
machines, and develop a broad range of preconditioners particularly useful for
kernel matrices. We describe a scalable approach to both solving kernel
machines and learning their hyperparameters. We show this approach is exact in
the limit of iterations and outperforms state-of-the-art approximations for a
given computational budget.
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
Authors: Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Date: 2016-05-25 00:00:00
Proceeding: ACL 2016 8
Tasks: Word Embeddings, Word Similarity

We propose a novel vector representation that integrates lexical contrast
into distributional vectors and strengthens the most salient features for
determining degrees of word similarity. The improved vectors significantly
outperform standard models and distinguish antonyms from synonyms with an
average precision of 0.66-0.76 across word classes (adjectives, nouns, verbs).
Moreover, we integrate the lexical contrast vectors into the objective function
of a skip-gram model. The novel embedding outperforms state-of-the-art models
on predicting word similarities in SimLex-999, and on distinguishing antonyms
from synonyms.
Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites
Authors: Dimo Brockhoff, Tea Tusar, Anne Auger, Nikolaus Hansen
Date: 2016-04-01 00:00:00
Tasks: Benchmarking, Multiobjective Optimization

Several test function suites are being used for numerical benchmarking of
multiobjective optimization algorithms. While they have some desirable
properties, like well-understood Pareto sets and Pareto fronts of various
shapes, most of the currently used functions possess characteristics that are
arguably under-represented in real-world problems. They mainly stem from the
easier construction of such functions and result in improbable properties such
as separability, optima located exactly at the boundary constraints, and the
existence of variables that solely control the distance between a solution and
the Pareto front. Here, we propose an alternative way to constructing
multiobjective problems-by combining existing single-objective problems from
the literature. We describe in particular the bbob-biobj test suite with 55
bi-objective functions in continuous domain, and its extended version with 92
bi-objective functions (bbob-biobj-ext). Both test suites have been implemented
in the COCO platform for black-box optimization benchmarking. Finally, we
recommend a general procedure for creating test suites for an arbitrary number
of objectives. Besides providing the formal function definitions and presenting
their (known) properties, this paper also aims at giving the rationale behind
our approach in terms of groups of functions with similar properties, objective
space normalization, and problem instances. The latter allows us to easily
compare the performance of deterministic and stochastic solvers, which is an
often overlooked issue in benchmarking.
Improving energy efficiency and classification accuracy of neuromorphic chips by learning binary synaptic crossbars
Authors: Antonio Jimeno Yepes, Jianbin Tang
Date: 2016-05-25 00:00:00
Tasks: General Classification, GPU

Deep Neural Networks (DNN) have achieved human level performance in many
image analytics tasks but DNNs are mostly deployed to GPU platforms that
consume a considerable amount of power. Brain-inspired spiking neuromorphic
chips consume low power and can be highly parallelized. However, for deploying
DNNs to energy efficient neuromorphic chips the incompatibility between
continuous neurons and synaptic weights of traditional DNNs, discrete spiking
neurons and synapses of neuromorphic chips has to be overcome. Previous work
has achieved this by training a network to learn continuous probabilities and
deployment to a neuromorphic architecture by random sampling these
probabilities. An ensemble of sampled networks is needed to approximate the
performance of the trained network.
  In the work presented in this paper, we have extended previous research by
directly learning binary synaptic crossbars. Results on MNIST show that better
performance can be achieved with a small network in one time step (92.7%
maximum observed accuracy vs 95.98% accuracy in our work). Top results on a
larger network are similar to previously published results (99.42% maximum
observed accuracy vs 99.45% accuracy in our work). More importantly, in our
work a smaller ensemble is needed to achieve similar or better accuracy than
previous work, which translates into significantly decreased energy consumption
for both networks. Results of our work are stable since they do not require
random sampling.
Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies
Authors: Christian Huemmer, Christian Hofmann, Roland Maas, Walter Kellermann
Date: 2016-04-14 00:00:00
Tasks: Acoustic echo cancellation, Evolutionary Algorithms

In this article, we present the elitist particle filter based on evolutionary
strategies (EPFES) as an efficient approach for nonlinear system
identification. The EPFES is derived from the frequently-employed state-space
model, where the relevant information of the nonlinear system is captured by an
unknown state vector. Similar to classical particle filtering, the EPFES
consists of a set of particles and respective weights which represent different
realizations of the latent state vector and their likelihood of being the
solution of the optimization problem. As main innovation, the EPFES includes an
evolutionary elitist-particle selection which combines long-term information
with instantaneous sampling from an approximated continuous posterior
distribution. In this article, we propose two advancements of the
previously-published elitist-particle selection process. Further, the EPFES is
shown to be a generalization of the widely-used Gaussian particle filter and
thus evaluated with respect to the latter for two completely different
scenarios: First, we consider the so-called univariate nonstationary growth
model with time-variant latent state variable, where the evolutionary selection
of elitist particles is evaluated for non-recursively calculated particle
weights. Second, the problem of nonlinear acoustic echo cancellation is
addressed in a simulated scenario with speech as input signal: By using
long-term fitness measures, we highlight the efficacy of the well-generalizing
EPFES in estimating the nonlinear system even for large search spaces. Finally,
we illustrate similarities between the EPFES and evolutionary algorithms to
outline future improvements by fusing the achievements of both fields of
research.
Design and development a children's speech database
Authors: Radoslava Kraleva
Date: 2016-05-25 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis

The report presents the process of planning, designing and the development of
a database of spoken children's speech whose native language is Bulgarian. The
proposed model is designed for children between the age of 4 and 6 without
speech disorders, and reflects their specific capabilities. At this age most
children cannot read, there is no sustained concentration, they are emotional,
etc. The aim is to unite all the media information accompanying the recording
and processing of spoken speech, thereby to facilitate the work of researchers
in the field of speech recognition. This database will be used for the
development of systems for children's speech recognition, children's speech
synthesis systems, games which allow voice control, etc. As a result of the
proposed model a prototype system for speech recognition is presented.
On model architecture for a children's speech recognition interactive dialog system
Authors: Radoslava Kraleva, Velin Kralev
Date: 2016-05-25 00:00:00
Tasks: speech-recognition, Speech Recognition

This report presents a general model of the architecture of information
systems for the speech recognition of children. It presents a model of the
speech data stream and how it works. The result of these studies and presented
veins architectural model shows that research needs to be focused on
acoustic-phonetic modeling in order to improve the quality of children's speech
recognition and the sustainability of the systems to noise and changes in
transmission environment. Another important aspect is the development of more
accurate algorithms for modeling of spontaneous child speech.
Deeply-Fused Nets
Authors: Jingdong Wang, Zhen Wei, Ting Zhang, Wen-Jun Zeng
Date: 2016-05-25 00:00:00

In this paper, we present a novel deep learning approach, deeply-fused nets.
The central idea of our approach is deep fusion, i.e., combine the intermediate
representations of base networks, where the fused output serves as the input of
the remaining part of each base network, and perform such combinations deeply
over several intermediate representations. The resulting deeply fused net
enjoys several benefits. First, it is able to learn multi-scale representations
as it enjoys the benefits of more base networks, which could form the same
fused network, other than the initial group of base networks. Second, in our
suggested fused net formed by one deep and one shallow base networks, the flows
of the information from the earlier intermediate layer of the deep base network
to the output and from the input to the later intermediate layer of the deep
base network are both improved. Last, the deep and shallow base networks are
jointly learnt and can benefit from each other. More interestingly, the
essential depth of a fused net composed from a deep base network and a shallow
base network is reduced because the fused net could be composed from a less
deep base network, and thus training the fused net is less difficult than
training the initial deep base network. Empirical results demonstrate that our
approach achieves superior performance over two closely-related methods, ResNet
and Highway, and competitive performance compared to the state-of-the-arts.
Learning Nonparametric Forest Graphical Models with Prior Information
Authors: Yuancheng Zhu, Zhe Liu, Siqi Sun
Date: 2015-11-12 00:00:00
Tasks: Density Estimation

We present a framework for incorporating prior information into nonparametric
estimation of graphical models. To avoid distributional assumptions, we
restrict the graph to be a forest and build on the work of forest density
estimation (FDE). We reformulate the FDE approach from a Bayesian perspective,
and introduce prior distributions on the graphs. As two concrete examples, we
apply this framework to estimating scale-free graphs and learning multiple
graphs with similar structures. The resulting algorithms are equivalent to
finding a maximum spanning tree of a weighted graph with a penalty term on the
connectivity pattern of the graph. We solve the optimization problem via a
minorize-maximization procedure with Kruskal's algorithm. Simulations show that
the proposed methods outperform competing parametric methods, and are robust to
the true data distribution. They also lead to improvement in predictive power
and interpretability in two real data sets.
Bidirectional Helmholtz Machines
Authors: Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio
Date: 2015-06-12 00:00:00

Efficient unsupervised training and inference in deep generative models
remains a challenging problem. One basic approach, called Helmholtz machine,
involves training a top-down directed generative model together with a
bottom-up auxiliary model used for approximate inference. Recent results
indicate that better generative models can be obtained with better approximate
inference procedures. Instead of improving the inference procedure, we here
propose a new model which guarantees that the top-down and bottom-up
distributions can efficiently invert each other. We achieve this by
interpreting both the top-down and the bottom-up directed models as approximate
inference distributions and by defining the model distribution to be the
geometric mean of these two. We present a lower-bound for the likelihood of
this model and we show that optimizing this bound regularizes the model so that
the Bhattacharyya distance between the bottom-up and top-down approximate
distributions is minimized. This approach results in state of the art
generative models which prefer significantly deeper architectures while it
allows for orders of magnitude more efficient approximate inference.
A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data
Authors: Yi-Lun Wang, Zhiqiang Li, Yifeng Wang, Xiaona Wang, Junjie Zheng, Xujuan Duan, Huafu Chen
Date: 2015-06-27 00:00:00
Tasks: feature selection

Feature selection is among the most important components because it not only
helps enhance the classification accuracy, but also or even more important
provides potential biomarker discovery. However, traditional multivariate
methods is likely to obtain unstable and unreliable results in case of an
extremely high dimensional feature space and very limited training samples,
where the features are often correlated or redundant. In order to improve the
stability, generalization and interpretations of the discovered potential
biomarker and enhance the robustness of the resultant classifier, the redundant
but informative features need to be also selected. Therefore we introduced a
novel feature selection method which combines a recent implementation of the
stability selection approach and the elastic net approach. The advantage in
terms of better control of false discoveries and missed discoveries of our
approach, and the resulted better interpretability of the obtained potential
biomarker is verified in both synthetic and real fMRI experiments. In addition,
we are among the first to demonstrate the robustness of feature selection
benefiting from the incorporation of stability selection and also among the
first to demonstrate the possible unrobustness of the classical univariate
two-sample t-test method. Specifically, we show the robustness of our feature
selection results in existence of noisy (wrong) training labels, as well as the
robustness of the resulted classifier based on our feature selection results in
the existence of data variation, demonstrated by a multi-center
attention-deficit/hyperactivity disorder (ADHD) fMRI data.
2D Visual Place Recognition for Domestic Service Robots at Night
Authors: James Mount, Michael Milford
Date: 2016-05-25 00:00:00
Tasks: Visual Place Recognition

Domestic service robots such as lawn mowing and vacuum cleaning robots are
the most numerous consumer robots in existence today. While early versions
employed random exploration, recent systems fielded by most of the major
manufacturers have utilized range-based and visual sensors and user-placed
beacons to enable robots to map and localize. However, active range and visual
sensing solutions have the disadvantages of being intrusive, expensive, or only
providing a 1D scan of the environment, while the requirement for beacon
placement imposes other practical limitations. In this paper we present a
passive and potentially cheap vision-based solution to 2D localization at night
that combines easily obtainable day-time maps with low resolution
contrast-normalized image matching algorithms, image sequence-based matching in
two-dimensions, place match interpolation and recent advances in conventional
low light camera technology. In a range of experiments over a domestic lawn and
in a lounge room, we demonstrate that the proposed approach enables 2D
localization at night, and analyse the effect on performance of varying
odometry noise levels, place match interpolation and sequence matching length.
Finally we benchmark the new low light camera technology and show how it can
enable robust place recognition even in an environment lit only by a moonless
sky, raising the tantalizing possibility of being able to apply all
conventional vision algorithms, even in the darkest of nights.
Learning Purposeful Behaviour in the Absence of Rewards
Authors: Marlos C. Machado, Michael Bowling
Date: 2016-05-25 00:00:00
Tasks: Reinforcement Learning

Artificial intelligence is commonly defined as the ability to achieve goals
in the world. In the reinforcement learning framework, goals are encoded as
reward functions that guide agent behaviour, and the sum of observed rewards
provide a notion of progress. However, some domains have no such reward signal,
or have a reward signal so sparse as to appear absent. Without reward feedback,
agent behaviour is typically random, often dithering aimlessly and lacking
intentionality. In this paper we present an algorithm capable of learning
purposeful behaviour in the absence of rewards. The algorithm proceeds by
constructing temporally extended actions (options), through the identification
of purposes that are "just out of reach" of the agent's current behaviour.
These purposes establish intrinsic goals for the agent to learn, ultimately
resulting in a suite of behaviours that encourage the agent to visit different
parts of the state space. Moreover, the approach is particularly suited for
settings where rewards are very sparse, and such behaviours can help in the
exploration of the environment until reward is observed.
Engineering Deep Representations for Modeling Aesthetic Perception
Authors: Yanxiang Chen, Yuxing Hu, Luming Zhang, Ping Li, Chao Zhang
Date: 2016-05-25 00:00:00
Tasks: Attribute, Image Retargeting, Retrieval, Weakly-supervised Learning

Many aesthetic models in computer vision suffer from two shortcomings: 1) the low descriptiveness and interpretability of those hand-crafted aesthetic criteria (i.e., nonindicative of region-level aesthetics), and 2) the difficulty of engineering aesthetic features adaptively and automatically toward different image sets. To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting. More specifically, using a bag-ofwords (BoW) representation of the frequent Flickr image tags, a sparsity-constrained subspace algorithm discovers a compact set of textual attributes (e.g., landscape and sunset) for each image. Then, a weakly-supervised learning algorithm projects the textual attributes at image-level to the highly-responsive image patches at pixel-level. These patches indicate where humans look at appealing regions with respect to each textual attribute, which are employed to learn the visual attributes. Psychological and anatomical studies have shown that humans perceive visual concepts hierarchically. Hence, we normalize these patches and feed them into a five-layer convolutional neural network (CNN) to mimick the hierarchy of human perceiving the visual attributes. We apply the learned deep features on image retargeting, aesthetics ranking, and retrieval. Both subjective and objective experimental results thoroughly demonstrate the competitiveness of our approach.
Adversarial Autoencoders
Authors: Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey
Date: 2015-11-18 00:00:00
Tasks: Clustering, Data Visualization, Decoder, Dimensionality Reduction, General Classification, Unsupervised Image Classification, Unsupervised MNIST, Variational Inference

In this paper, we propose the "adversarial autoencoder" (AAE), which is a
probabilistic autoencoder that uses the recently proposed generative
adversarial networks (GAN) to perform variational inference by matching the
aggregated posterior of the hidden code vector of the autoencoder with an
arbitrary prior distribution. Matching the aggregated posterior to the prior
ensures that generating from any part of prior space results in meaningful
samples. As a result, the decoder of the adversarial autoencoder learns a deep
generative model that maps the imposed prior to the data distribution. We show
how the adversarial autoencoder can be used in applications such as
semi-supervised classification, disentangling style and content of images,
unsupervised clustering, dimensionality reduction and data visualization. We
performed experiments on MNIST, Street View House Numbers and Toronto Face
datasets and show that adversarial autoencoders achieve competitive results in
generative modeling and semi-supervised classification tasks.