pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Authors: Baris Gecer, Ozge Yalcinkaya, Onur Tasar, Selim Aksoy
Date: 2015-10-10 00:00:00
Tasks: Breast Cancer Detection, General Classification, Medical Diagnosis, Multi-Label Learning

Multi-instance multi-label (MIML) learning is a challenging problem in many
aspects. Such learning approaches might be useful for many medical diagnosis
applications including breast cancer detection and classification. In this
study subset of digiPATH dataset (whole slide digital breast cancer
histopathology images) are used for training and evaluation of six
state-of-the-art MIML methods.
  At the end, performance comparison of these approaches are given by means of
effective evaluation metrics. It is shown that MIML-kNN achieve the best
performance that is %65.3 average precision, where most of other methods attain
acceptable results as well.
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion
Authors: Wensen Feng, Yunjin Chen
Date: 2015-10-10 00:00:00
Tasks: Computational Efficiency, Denoising, GPU, Image Restoration

The degradation of the acquired signal by Poisson noise is a common problem
for various imaging applications, such as medical imaging, night vision and
microscopy. Up to now, many state-of-the-art Poisson denoising techniques
mainly concentrate on achieving utmost performance, with little consideration
for the computation efficiency. Therefore, in this study we aim to propose an
efficient Poisson denoising model with both high computational efficiency and
recovery quality. To this end, we exploit the newly-developed trainable
nonlinear reaction diffusion model which has proven an extremely fast image
restoration approach with performance surpassing recent state-of-the-arts. We
retrain the model parameters, including the linear filters and influence
functions by taking into account the Poisson noise statistics, and end up with
an optimized nonlinear diffusion model specialized for Poisson denoising. The
trained model provides strongly competitive results against state-of-the-art
approaches, meanwhile bearing the properties of simple structure and high
efficiency. Furthermore, our proposed model comes along with an additional
advantage, that the diffusion process is well-suited for parallel computation
on GPUs. For images of size $512 \times 512$, our GPU implementation takes less
than 0.1 seconds to produce state-of-the-art Poisson denoising performance.
DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
Authors: Srinivas S. S. Kruthiventi, Kumar Ayush, R. Venkatesh Babu
Date: 2015-10-10 00:00:00
Tasks: Saliency Prediction

Understanding and predicting the human visual attentional mechanism is an
active area of research in the fields of neuroscience and computer vision. In
this work, we propose DeepFix, a first-of-its-kind fully convolutional neural
network for accurate saliency prediction. Unlike classical works which
characterize the saliency map using various hand-crafted features, our model
automatically learns features in a hierarchical fashion and predicts saliency
map in an end-to-end manner. DeepFix is designed to capture semantics at
multiple scales while taking global context into account using network layers
with very large receptive fields. Generally, fully convolutional nets are
spatially invariant which prevents them from modeling location dependent
patterns (e.g. centre-bias). Our network overcomes this limitation by
incorporating a novel Location Biased Convolutional layer. We evaluate our
model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that
it outperforms other recent approaches by a significant margin.
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
Authors: Min Yang, Yunde Jia
Date: 2015-10-10 00:00:00
Tasks: feature selection

Robust online multi-person tracking requires the correct associations of
online detection responses with existing trajectories. We address this problem
by developing a novel appearance modeling approach to provide accurate
appearance affinities to guide data association. In contrast to most existing
algorithms that only consider the spatial structure of human appearances, we
exploit the temporal dynamic characteristics within temporal appearance
sequences to discriminate different persons. The temporal dynamic makes a
sufficient complement to the spatial structure of varying appearances in the
feature space, which significantly improves the affinity measurement between
trajectories and detections. We propose a feature selection algorithm to
describe the appearance variations with mid-level semantic features, and
demonstrate its usefulness in terms of temporal dynamic appearance modeling.
Moreover, the appearance model is learned incrementally by alternatively
evaluating newly-observed appearances and adjusting the model parameters to be
suitable for online tracking. Reliable tracking of multiple persons in complex
scenes is achieved by incorporating the learned model into an online
tracking-by-detection framework. Our experiments on the challenging benchmark
MOTChallenge 2015 demonstrate that our method outperforms the state-of-the-art
multi-person tracking algorithms.
Survey on Feature Selection
Authors: Tarek Amr Abdallah, Beatriz de La Iglesia
Date: 2015-10-10 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Survey

Feature selection plays an important role in the data mining process. It is
needed to deal with the excessive number of features, which can become a
computational burden on the learning algorithms. It is also necessary, even
when computational resources are not scarce, since it improves the accuracy of
the machine learning tasks, as we will see in the upcoming sections. In this
review, we discuss the different feature selection approaches, and the relation
between them and the various machine learning algorithms.
Remarks on kernel Bayes' rule
Authors: Hisashi Johno, Kazunori Nakamoto, Tatsuhiko Saigo
Date: 2015-07-04 00:00:00
Tasks: Bayesian Inference

Kernel Bayes' rule has been proposed as a nonparametric kernel-based method
to realize Bayesian inference in reproducing kernel Hilbert spaces. However, we
demonstrate both theoretically and experimentally that the prediction result by
kernel Bayes' rule is in some cases unnatural. We consider that this phenomenon
is in part due to the fact that the assumptions in kernel Bayes' rule do not
hold in general.
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
Authors: Wufeng Xue, Xuanqin Mou, Lei Zhang
Date: 2015-10-10 00:00:00
Tasks: Image Quality Assessment

Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is
particularly challenging due to the absence of knowledge about the reference
image and distortion type. Features based on natural scene statistics (NSS)
have been successfully used in BIQA, while the quality relevance of the feature
plays an essential role to the quality prediction performance. Motivated by the
fact that the early processing stage in human visual system aims to remove the
signal redundancies for efficient visual coding, we propose a simple but very
effective BIQA method by computing the statistics of self-similarity (SOS) in
an image. Specifically, we calculate the inter-scale similarity and intra-scale
similarity of the distorted image, extract the SOS features from these
similarities, and learn a regression model to map the SOS features to the
subjective quality score. Extensive experiments demonstrate very competitive
quality prediction performance and generalization ability of the proposed SOS
based BIQA method.
TSEB: More Efficient Thompson Sampling for Policy Learning
Authors: P. Prasanna, Sarath Chandar, Balaraman Ravindran
Date: 2015-10-10 00:00:00
Tasks: Thompson Sampling

In model-based solution approaches to the problem of learning in an unknown
environment, exploring to learn the model parameters takes a toll on the
regret. The optimal performance with respect to regret or PAC bounds is
achievable, if the algorithm exploits with respect to reward or explores with
respect to the model parameters, respectively. In this paper, we propose TSEB,
a Thompson Sampling based algorithm with adaptive exploration bonus that aims
to solve the problem with tighter PAC guarantees, while being cautious on the
regret as well. The proposed approach maintains distributions over the model
parameters which are successively refined with more experience. At any given
time, the agent solves a model sampled from this distribution, and the sampled
reward distribution is skewed by an exploration bonus in order to generate more
informative exploration. The policy by solving is then used for generating more
experience that helps in updating the posterior over the model parameters. We
provide a detailed analysis of the PAC guarantees, and convergence of the
proposed approach. We show that our adaptive exploration bonus encourages the
additional exploration required for better PAC bounds on the algorithm. We
provide empirical analysis on two different simulated domains.
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Authors: Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu
Date: 2015-09-30 00:00:00
Tasks: Image Classification

We seek to improve deep neural networks by generalizing the pooling
operations that play a central role in current architectures. We pursue a
careful exploration of approaches to allow pooling to learn and to adapt to
complex and variable patterns. The two primary directions lie in (1) learning a
pooling function via (two strategies of) combining of max and average pooling,
and (2) learning a pooling function in the form of a tree-structured fusion of
pooling filters that are themselves learned. In our experiments every
generalized pooling operation we explore improves performance when used in
place of average or max pooling. We experimentally demonstrate that the
proposed pooling operations provide a boost in invariance properties relative
to conventional pooling and set the state of the art on several widely adopted
benchmark datasets; they are also easy to implement, and can be applied within
various deep neural network architectures. These benefits come with only a
light increase in computational overhead during training and a very modest
increase in the number of model parameters.
Wavelet Frame Based Image Restoration Using Sparsity, Nonlocal and Support Prior of Frame Coefficients
Authors: Liangtian He, Yi-Lun Wang
Date: 2015-10-10 00:00:00
Tasks: Image Restoration

The wavelet frame systems have been widely investigated and applied for image
restoration and many other image processing problems over the past decades,
attributing to their good capability of sparsely approximating piece-wise
smooth functions such as images. Most wavelet frame based models exploit the
$l_1$ norm of frame coefficients for a sparsity constraint in the past. The
authors in \cite{ZhangY2013, Dong2013} proposed an $l_0$ minimization model,
where the $l_0$ norm of wavelet frame coefficients is penalized instead, and
have demonstrated that significant improvements can be achieved compared to the
commonly used $l_1$ minimization model. Very recently, the authors in
\cite{Chen2015} proposed $l_0$-$l_2$ minimization model, where the nonlocal
prior of frame coefficients is incorporated. This model proved to outperform
the single $l_0$ minimization based model in terms of better recovered image
quality. In this paper, we propose a truncated $l_0$-$l_2$ minimization model
which combines sparsity, nonlocal and support prior of the frame coefficients.
The extensive experiments have shown that the recovery results from the
proposed regularization method performs better than existing state-of-the-art
wavelet frame based methods, in terms of edge enhancement and texture
preserving performance.
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models
Authors: Jie Ding, Mohammad Noshad, Vahid Tarokh
Date: 2015-06-06 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

In this work, we consider the class of multi-state autoregressive processes
that can be used to model non-stationary time-series of interest. In order to
capture different autoregressive (AR) states underlying an observed time
series, it is crucial to select the appropriate number of states. We propose a
new model selection technique based on the Gap statistics, which uses a null
reference distribution on the stable AR filters to check whether adding a new
AR state significantly improves the performance of the model. To that end, we
define a new distance measure between AR filters based on mean squared
prediction error (MSPE), and propose an efficient method to generate random
stable filters that are uniformly distributed in the coefficient space.
Numerical results are provided to evaluate the performance of the proposed
approach.
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Authors: Izhar Wallach, Michael Dzamba, Abraham Heifets
Date: 2015-10-10 00:00:00
Tasks: Drug Discovery

Deep convolutional neural networks comprise a subclass of deep neural
networks (DNN) with a constrained architecture that leverages the spatial and
temporal structure of the domain they model. Convolutional networks achieve the
best predictive performance in areas such as speech and image recognition by
hierarchically composing simple local features into complex models. Although
DNNs have been used in drug discovery for QSAR and ligand-based bioactivity
predictions, none of these models have benefited from this powerful
convolutional architecture. This paper introduces AtomNet, the first
structure-based, deep convolutional neural network designed to predict the
bioactivity of small molecules for drug discovery applications. We demonstrate
how to apply the convolutional concepts of feature locality and hierarchical
composition to the modeling of bioactivity and chemical interactions. In
further contrast to existing DNN techniques, we show that AtomNet's application
of local convolutional filters to structural target information successfully
predicts new active molecules for targets with no previously known modulators.
Finally, we show that AtomNet outperforms previous docking approaches on a
diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9
on 57.8% of the targets in the DUDE benchmark.
Efficient Per-Example Gradient Computations
Authors: Ian Goodfellow
Date: 2015-10-07 00:00:00

This technical report describes an efficient technique for computing the norm
of the gradient of the loss function for a neural network with respect to its
parameters. This gradient norm can be computed efficiently for every example.
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting
Authors: Yves-Laurent Kom Samo, Stephen J. Roberts
Date: 2015-10-09 00:00:00
Tasks: Gaussian Processes, regression, Time Series, Time Series Analysis, Time Series Forecasting, Translation

In this paper we introduce a novel online time series forecasting model we
refer to as the pM-GP filter. We show that our model is equivalent to Gaussian
process regression, with the advantage that both online forecasting and online
learning of the hyper-parameters have a constant (rather than cubic) time
complexity and a constant (rather than squared) memory requirement in the
number of observations, without resorting to approximations. Moreover, the
proposed model is expressive in that the family of covariance functions of the
implied latent process, namely the spectral Matern kernels, have recently been
proven to be capable of approximating arbitrarily well any
translation-invariant covariance function. The benefit of our approach compared
to competing models is demonstrated using experiments on several real-life
datasets.
Gelisp: A Library to Represent Musical CSPs and Search Strategies
Authors: Mauricio Toro, Camilo Rueda, Carlos Agón, Gérard Assayag
Date: 2015-10-09 00:00:00
Tasks: Music Generation

In this paper we present Gelisp, a new library to represent musical
Constraint Satisfaction Problems and search strategies intuitively. Gelisp has
two interfaces, a command-line one for Common Lisp and a graphical one for
OpenMusic. Using Gelisp, we solved a problem of automatic music generation
proposed by composer Michael Jarrell and we found solutions for the
All-interval series.
Generalized Spectral Kernels
Authors: Yves-Laurent Kom Samo, Stephen Roberts
Date: 2015-06-07 00:00:00

In this paper we propose a family of tractable kernels that is dense in the
family of bounded positive semi-definite functions (i.e. can approximate any
bounded kernel with arbitrary precision). We start by discussing the case of
stationary kernels, and propose a family of spectral kernels that extends
existing approaches such as spectral mixture kernels and sparse spectrum
kernels. Our extension has two primary advantages. Firstly, unlike existing
spectral approaches that yield infinite differentiability, the kernels we
introduce allow learning the degree of differentiability of the latent function
in Gaussian process (GP) models and functions in the reproducing kernel Hilbert
space (RKHS) in other kernel methods. Secondly, we show that some of the
kernels we propose require fewer parameters than existing spectral kernels for
the same accuracy, thereby leading to faster and more robust inference.
Finally, we generalize our approach and propose a flexible and tractable family
of spectral kernels that we prove can approximate any continuous bounded
nonstationary kernel.
Mapping Generative Models onto a Network of Digital Spiking Neurons
Authors: Bruno U. Pedroni, Srinjoy Das, John V. Arthur, Paul A. Merolla, Bryan L. Jackson, Dharmendra S. Modha, Kenneth Kreutz-Delgado, Gert Cauwenberghs
Date: 2015-09-24 00:00:00
Tasks: image-classification, Image Classification, Quantization, speech-recognition, Speech Recognition

Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have
been successfully used in applications ranging from speech recognition to image
classification. Inference and learning in these algorithms use a Markov Chain
Monte Carlo procedure called Gibbs sampling, where a logistic function forms
the kernel of this sampler. On the other side of the spectrum, neuromorphic
systems have shown great promise for low-power and parallelized cognitive
computing, but lack well-suited applications and automation procedures. In this
work, we propose a systematic method for bridging the RBM algorithm and digital
neuromorphic systems, with a generative pattern completion task as proof of
concept. For this, we first propose a method of producing the Gibbs sampler
using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe
the process of mapping generative RBMs trained offline onto the IBM TrueNorth
neurosynaptic processor -- a low-power digital neuromorphic VLSI substrate.
Mapping these algorithms onto neuromorphic hardware presents unique challenges
in network connectivity and weight and bias quantization, which, in turn,
require architectural and design strategies for the physical realization.
Generative performance metrics are analyzed to validate the neuromorphic
requirements and to best select the neuron parameters for the model. Lastly, we
describe a design automation procedure which achieves optimal resource usage,
accounting for the novel hardware adaptations. This work represents the first
implementation of generative RBM inference on a neuromorphic VLSI substrate.
Human languages order information efficiently
Authors: Daniel Gildea, T. Florian Jaeger
Date: 2015-10-09 00:00:00

Most languages use the relative order between words to encode meaning
relations. Languages differ, however, in what orders they use and how these
orders are mapped onto different meanings. We test the hypothesis that, despite
these differences, human languages might constitute different `solutions' to
common pressures of language use. Using Monte Carlo simulations over data from
five languages, we find that their word orders are efficient for processing in
terms of both dependency length and local lexical probability. This suggests
that biases originating in how the brain understands language strongly
constrain how human languages change over generations.
Human Head Pose Estimation by Facial Features Location
Authors: Eugene Borovikov
Date: 2015-10-09 00:00:00
Tasks: Head Pose Estimation, Pose Estimation

We describe a method for estimating human head pose in a color image that
contains enough of information to locate the head silhouette and detect
non-trivial color edges of individual facial features. The method works by
spotting the human head on an arbitrary background, extracting the head
outline, and locating facial features necessary to describe the head
orientation in the 3D space. It is robust enough to work with both color and
gray-level images featuring quasi-frontal views of a human head under variable
lighting conditions.
Procams-Based Cybernetics
Authors: Kosuke Sato, Daisuke Iwai, Sei Ikeda, Noriko Takemura
Date: 2015-10-09 00:00:00

Procams-based cybernetics is a unique, emerging research field, which aims at
enhancing and supporting our activities by naturally connecting human and
computers/machines as a cooperative integrated system via projector-camera
systems (procams). It rests on various research domains such as
virtual/augmented reality, computer vision, computer graphics, projection
display, human computer interface, human robot interaction and so on. This
laboratory presentation provides a brief history including recent achievements
of our procams-based cybernetics project.
Large-scale Artificial Neural Network: MapReduce-based Deep Learning
Authors: Kairan Sun, Xu Wei, Gengtao Jia, Risheng Wang, Ruizhi Li
Date: 2015-10-09 00:00:00
Tasks: Cloud Computing, Deep Learning, Handwritten Digit Recognition

Faced with continuously increasing scale of data, original back-propagation
neural network based machine learning algorithm presents two non-trivial
challenges: huge amount of data makes it difficult to maintain both efficiency
and accuracy; redundant data aggravates the system workload. This project is
mainly focused on the solution to the issues above, combining deep learning
algorithm with cloud computing platform to deal with large-scale data. A
MapReduce-based handwriting character recognizer will be designed in this
project to verify the efficiency improvement this mechanism will achieve on
training and practical large-scale data. Careful discussion and experiment will
be developed to illustrate how deep learning algorithm works to train
handwritten digits data, how MapReduce is implemented on deep learning neural
network, and why this combination accelerates computation. Besides performance,
the scalability and robustness will be mentioned in this report as well. Our
system comes with two demonstration software that visually illustrates our
handwritten digit recognition/encoding application.