pwc-archive/papers-with-abstracts huggingface dataset:
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
Authors: Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun, Haiqing Jiang, Zhangxin Chen
Date: 2017-10-19 00:00:00
Tasks: Machine Translation, Prediction, Protein Function Prediction, Translation

With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction.
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Authors: Chengzhou Tang, Oliver Wang, Ping Tan
Date: 2017-08-16 00:00:00
Tasks: Visual Odometry

Many monocular visual SLAM algorithms are derived from incremental
structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM
method which integrates recent advances made in global SfM. In particular, we
present two main contributions to visual SLAM. First, we solve the visual
odometry problem by a novel rank-1 matrix factorization technique which is more
robust to the errors in map initialization. Second, we adopt a recent global
SfM method for the pose-graph optimization, which leads to a multi-stage linear
formulation and enables L1 optimization for better robustness to false loops.
The combination of these two approaches generates more robust reconstruction
and is significantly faster (4X) than recent state-of-the-art SLAM systems. We
also present a new dataset recorded with ground truth camera motion in a Vicon
motion capture room, and compare our method to prior systems on it and
established benchmark datasets.
Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition
Authors: Cheng Yaw Low, Andrew Beng Jin Teoh, Cong Jie Ng
Date: 2016-04-24 00:00:00
Tasks: Face Recognition

This paper devises a new means of filter diversification, dubbed multi-fold
filter convolution (M-FFC), for face recognition. On the assumption that M-FFC
receives single-scale Gabor filters of varying orientations as input, these
filters are self-cross convolved by M-fold to instantiate a filter offspring
set. The M-FFC flexibility also permits cross convolution amongst Gabor filters
and other filter banks of profoundly dissimilar traits, e.g., principal
component analysis (PCA) filters, and independent component analysis (ICA)
filters. The 2-FFC of Gabor, PCA and ICA filters thus yields three offspring
sets: (1) Gabor filters solely, (2) Gabor-PCA filters, and (3) Gabor-ICA
filters, to render the learning-free and the learning-based 2-FFC descriptors.
To facilitate a sensible Gabor filter selection for M-FFC, the 40 multi-scale,
multi-orientation Gabor filters are condensed into 8 elementary filters. Aside
from that, an average histogram pooling operator is employed to leverage the
2-FFC histogram features, prior to the final whitening PCA compression. The
empirical results substantiate that the 2-FFC descriptors prevail over, or on
par with, other face descriptors on both identification and verification tasks.
Machine Learning as Statistical Data Assimilation
Authors: H. D. I. Abarbanel, P. J. Rozdeba, S. Shirman
Date: 2017-10-19 00:00:00
Tasks: BIG-bench Machine Learning

We identify a strong equivalence between neural network based machine
learning (ML) methods and the formulation of statistical data assimilation
(DA), known to be a problem in statistical physics. DA, as used widely in
physical and biological sciences, systematically transfers information in
observations to a model of the processes producing the observations. The
correspondence is that layer label in the ML setting is the analog of time in
the data assimilation setting. Utilizing aspects of this equivalence we discuss
how to establish the global minimum of the cost functions in the ML context,
using a variational annealing method from DA. This provides a design method for
optimal networks for ML applications and may serve as the basis for
understanding the success of "deep learning". Results from an ML example are
presented.
  When the layer label is taken to be continuous, the Euler-Lagrange equation
for the ML optimization problem is an ordinary differential equation, and we
see that the problem being solved is a two point boundary value problem. The
use of continuous layers is denoted "deepest learning". The Hamiltonian version
provides a direct rationale for back propagation as a solution method for the
canonical momentum; however, it suggests other solution methods are to be
preferred.
Minimax Estimation of Bandable Precision Matrices
Authors: Addison Hu, Sahand Negahban
Date: 2017-10-19 00:00:00
Proceeding: NeurIPS 2017 12

The inverse covariance matrix provides considerable insight for understanding
statistical models in the multivariate setting. In particular, when the
distribution over variables is assumed to be multivariate normal, the sparsity
pattern in the inverse covariance matrix, commonly referred to as the precision
matrix, corresponds to the adjacency matrix representation of the Gauss-Markov
graph, which encodes conditional independence statements between variables.
Minimax results under the spectral norm have previously been established for
covariance matrices, both sparse and banded, and for sparse precision matrices.
We establish minimax estimation bounds for estimating banded precision matrices
under the spectral norm. Our results greatly improve upon the existing bounds;
in particular, we find that the minimax rate for estimating banded precision
matrices matches that of estimating banded covariance matrices. The key insight
in our analysis is that we are able to obtain barely-noisy estimates of $k
\times k$ subblocks of the precision matrix by inverting slightly wider blocks
of the empirical covariance matrix along the diagonal. Our theoretical results
are complemented by experiments demonstrating the sharpness of our bounds.
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Authors: Gintare Karolina Dziugaite, Daniel M. Roy
Date: 2017-03-31 00:00:00
Tasks: Generalization Bounds

One of the defining properties of deep learning is that models are chosen to
have many more parameters than available training data. In light of this
capacity for overfitting, it is remarkable that simple algorithms like SGD
reliably return solutions with low test error. One roadblock to explaining
these phenomena in terms of implicit regularization, structural properties of
the solution, and/or easiness of the data is that many learning bounds are
quantitatively vacuous when applied to networks learned by SGD in this "deep
learning" regime. Logically, in order to explain generalization, we need
nonvacuous bounds. We return to an idea by Langford and Caruana (2001), who
used PAC-Bayes bounds to compute nonvacuous numerical bounds on generalization
error for stochastic two-layer two-hidden-unit neural networks via a
sensitivity analysis. By optimizing the PAC-Bayes bound directly, we are able
to extend their approach and obtain nonvacuous generalization bounds for deep
stochastic neural network classifiers with millions of parameters trained on
only tens of thousands of examples. We connect our findings to recent and old
work on flat minima and MDL-based explanations of generalization.
Improved Search in Hamming Space using Deep Multi-Index Hashing
Authors: Hanjiang Lai, Yan Pan
Date: 2017-10-19 00:00:00
Tasks: Image Retrieval, Retrieval

Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. There has been considerable
research on generating efficient image representation via the
deep-network-based hashing methods. However, the issue of efficient searching
in the deep representation space remains largely unsolved. To this end, we
propose a simple yet efficient deep-network-based multi-index hashing method
for simultaneously learning the powerful image representation and the efficient
searching. To achieve these two goals, we introduce the multi-index hashing
(MIH) mechanism into the proposed deep architecture, which divides the binary
codes into multiple substrings. Due to the non-uniformly distributed codes will
result in inefficiency searching, we add the two balanced constraints at
feature-level and instance-level, respectively. Extensive evaluations on
several benchmark image retrieval datasets show that the learned balanced
binary codes bring dramatic speedups and achieve comparable performance over
the existing baselines.
Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
Authors: Li Shen, Laurie R. Margolies, Joseph H. Rothstein, Eugene Fluder, Russell B. McBride, Weiva Sieh
Date: 2017-08-30 00:00:00
Tasks: Breast Cancer Detection, Cancer-no cancer per image classification, Sensitivity, Specificity

The rapid development of deep learning, a family of machine learning
techniques, has spurred much interest in its application to medical imaging
problems. Here, we develop a deep learning algorithm that can accurately detect
breast cancer on screening mammograms using an "end-to-end" training approach
that efficiently leverages training datasets with either complete clinical
annotation or only the cancer status (label) of the whole image. In this
approach, lesion annotations are required only in the initial training stage,
and subsequent stages require only image-level labels, eliminating the reliance
on rarely available lesion annotations. Our all convolutional network method
for classifying screening mammograms attained excellent performance in
comparison with previous methods. On an independent test set of digitized film
mammograms from Digital Database for Screening Mammography (DDSM), the best
single model achieved a per-image AUC of 0.88, and four-model averaging
improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On a
validation set of full-field digital mammography (FFDM) images from the
INbreast database, the best single model achieved a per-image AUC of 0.95, and
four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity:
96.1%). We also demonstrate that a whole image classifier trained using our
end-to-end approach on the DDSM digitized film mammograms can be transferred to
INbreast FFDM images using only a subset of the INbreast data for fine-tuning
and without further reliance on the availability of lesion annotations. These
findings show that automatic deep learning methods can be readily trained to
attain high accuracy on heterogeneous mammography platforms, and hold
tremendous promise for improving clinical tools to reduce false positive and
false negative screening mammography results.
Computational ghost imaging using deep learning
Authors: Tomoyoshi Shimobaba, Yutaka Endo, Takashi Nishitsuji, Takayuki Takahashi, Yuki Nagahama, Satoki Hasegawa, Marie Sano, Ryuji Hirayama, Takashi Kakue, Atsushi Shiraki, Tomoyoshi Ito
Date: 2017-10-19 00:00:00
Tasks: Deep Learning

Computational ghost imaging (CGI) is a single-pixel imaging technique that
exploits the correlation between known random patterns and the measured
intensity of light transmitted (or reflected) by an object. Although CGI can
obtain two- or three- dimensional images with a single or a few bucket
detectors, the quality of the reconstructed images is reduced by noise due to
the reconstruction of images from random patterns. In this study, we improve
the quality of CGI images using deep learning. A deep neural network is used to
automatically learn the features of noise-contaminated CGI images. After
training, the network is able to predict low-noise images from new
noise-contaminated CGI images.
RCAMP: A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity
Authors: Sergio Caccamo, Ramviyas Parasuraman, Luigi Freda, Mario Gianni, Petter Ögren
Date: 2017-10-18 00:00:00
Tasks: Motion Planning, Position

Mobile robots, be it autonomous or teleoperated, require stable communication
with the base station to exchange valuable information. Given the stochastic
elements in radio signal propagation, such as shadowing and fading, and the
possibilities of unpredictable events or hardware failures, communication loss
often presents a significant mission risk, both in terms of probability and
impact, especially in Urban Search and Rescue (USAR) operations. Depending on
the circumstances, disconnected robots are either abandoned or attempt to
autonomously back-trace their way to the base station. Although recent results
in Communication-Aware Motion Planning can be used to effectively manage
connectivity with robots, there are no results focusing on autonomously
re-establishing the wireless connectivity of a mobile robot without
back-tracking or using detailed a priori information of the network.
  In this paper, we present a robust and online radio signal mapping method
using Gaussian Random Fields and propose a Resilient Communication-Aware Motion
Planner (RCAMP) that integrates the above signal mapping framework with a
motion planner. RCAMP considers both the environment and the physical
constraints of the robot, based on the available sensory information. We also
propose a self-repair strategy using RCMAP, that takes both connectivity and
the goal position into account when driving to a connection-safe position in
the event of a communication loss. We demonstrate the proposed planner in a set
of realistic simulations of an exploration task in single or multi-channel
communication scenarios.
First-Person Perceptual Guidance Behavior Decomposition using Active Constraint Classification
Authors: Andrew Feit, Berenice Mettler
Date: 2017-10-18 00:00:00
Tasks: Classification, General Classification

Humans exhibit a wide range of adaptive and robust dynamic motion behavior
that is yet unmatched by autonomous control systems. These capabilities are
essential for real-time behavior generation in cluttered environments. Recent
work suggests that human capabilities rely on task structure learning and
embedded or ecological cognition in the form of perceptual guidance. This paper
describes the experimental investigation of the functional elements of human
motion guidance, focusing on the control and perceptual mechanisms. The motion,
control, and perceptual data from first-person guidance experiments is
decomposed into elemental segments based on invariants. These elements are then
analyzed to determine their functional characteristics. The resulting model
explains the structure of the agent-environment interaction and provides lawful
descriptions of specific perceptual guidance and control mechanisms.
Concept Drift Learning with Alternating Learners
Authors: Yunwen Xu, Rui Xu, Weizhong Yan, Paul Ardis
Date: 2017-10-18 00:00:00

Data-driven predictive analytics are in use today across a number of
industrial applications, but further integration is hindered by the requirement
of similarity among model training and test data distributions. This paper
addresses the need of learning from possibly nonstationary data streams, or
under concept drift, a commonly seen phenomenon in practical applications. A
simple dual-learner ensemble strategy, alternating learners framework, is
proposed. A long-memory model learns stable concepts from a long relevant time
window, while a short-memory model learns transient concepts from a small
recent window. The difference in prediction performance of these two models is
monitored and induces an alternating policy to select, update and reset the two
models. The method features an online updating mechanism to maintain the
ensemble accuracy, and a concept-dependent trigger to focus on relevant data.
Through empirical studies the method demonstrates effective tracking and
prediction when the steaming data carry abrupt and/or gradual changes.
Unsupervised Object Discovery and Segmentation of RGBD-images
Authors: Johan Ekekrantz, Nils Bore, Rares Ambrus, John Folkesson, Patric Jensfelt
Date: 2017-10-18 00:00:00
Tasks: Object, Object Discovery, Segmentation

In this paper we introduce a system for unsupervised object discovery and
segmentation of RGBD-images. The system models the sensor noise directly from
data, allowing accurate segmentation without sensor specific hand tuning of
measurement noise models making use of the recently introduced Statistical
Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the
system is able to apply probabilistic inference, enabling reliable segmentation
in previously challenging scenarios. In addition, we introduce new methods for
filtering out false positives, significantly improving the signal to noise
ratio. We show that the system significantly outperform state-of-the-art in on
a challenging real-world dataset.
The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications
Authors: James P. Crutchfield
Date: 2017-10-18 00:00:00
Tasks: Time Series, Time Series Analysis

The principle goal of computational mechanics is to define pattern and
structure so that the organization of complex systems can be detected and
quantified. Computational mechanics developed from efforts in the 1970s and
early 1980s to identify strange attractors as the mechanism driving weak fluid
turbulence via the method of reconstructing attractor geometry from measurement
time series and in the mid-1980s to estimate equations of motion directly from
complex time series. In providing a mathematical and operational definition of
structure it addressed weaknesses of these early approaches to discovering
patterns in natural systems.
  Since then, computational mechanics has led to a range of results from
theoretical physics and nonlinear mathematics to diverse applications---from
closed-form analysis of Markov and non-Markov stochastic processes that are
ergodic or nonergodic and their measures of information and intrinsic
computation to complex materials and deterministic chaos and intelligence in
Maxwellian demons to quantum compression of classical processes and the
evolution of computation and language.
  This brief review clarifies several misunderstandings and addresses concerns
recently raised regarding early works in the field (1980s). We show that
misguided evaluations of the contributions of computational mechanics are
groundless and stem from a lack of familiarity with its basic goals and from a
failure to consider its historical context. For all practical purposes, its
modern methods and results largely supersede the early works. This not only
renders recent criticism moot and shows the solid ground on which computational
mechanics stands but, most importantly, shows the significant progress achieved
over three decades and points to the many intriguing and outstanding challenges
in understanding the computational nature of complex dynamic systems.
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Authors: Omer Gottesman, Weiwei Pan, Finale Doshi-Velez
Date: 2017-10-18 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition methods are popular tools for learning latent variables
given only lower-order moments of the data. However, the standard assumption is
that we have sufficient data to estimate these moments to high accuracy. In
this work, we consider the case in which certain dimensions of the data are not
always observed---common in applied settings, where not all measurements may be
taken for all observations---resulting in moment estimates of varying quality.
We derive a weighted tensor decomposition approach that is computationally as
efficient as the non-weighted approach, and demonstrate that it outperforms
methods that do not appropriately leverage these less-observed dimensions.
Photo-Guided Exploration of Volume Data Features
Authors: Mohammad Raji, Alok Hota, Robert Sisneros, Peter Messmer, Jian Huang
Date: 2017-10-18 00:00:00

In this work, we pose the question of whether, by considering qualitative
information such as a sample target image as input, one can produce a rendered
image of scientific data that is similar to the target. The algorithm resulting
from our research allows one to ask the question of whether features like those
in the target image exists in a given dataset. In that way, our method is one
of imagery query or reverse engineering, as opposed to manual parameter
tweaking of the full visualization pipeline. For target images, we can use
real-world photographs of physical phenomena. Our method leverages deep neural
networks and evolutionary optimization. Using a trained similarity function
that measures the difference between renderings of a phenomenon and real-world
photographs, our method optimizes rendering parameters. We demonstrate the
efficacy of our method using a superstorm simulation dataset and images found
online. We also discuss a parallel implementation of our method, which was run
on NCSA's Blue Waters.
Solving $\ell^p\!$-norm regularization with tensor kernels
Authors: Saverio Salzo, Johan A. K. Suykens, Lorenzo Rosasco
Date: 2017-07-18 00:00:00

In this paper, we discuss how a suitable family of tensor kernels can be used
to efficiently solve nonparametric extensions of $\ell^p$ regularized learning
methods. Our main contribution is proposing a fast dual algorithm, and showing
that it allows to solve the problem efficiently. Our results contrast recent
findings suggesting kernel methods cannot be extended beyond Hilbert setting.
Numerical experiments confirm the effectiveness of the method.
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets
Authors: Jonghwa Yim, Kyung-Ah Sohn
Date: 2017-10-18 00:00:00
Tasks: image-classification, Image Classification

Despite the appeal of deep neural networks that largely replace the
traditional handmade filters, they still suffer from isolated cases that cannot
be properly handled only by the training of convolutional filters. Abnormal
factors, including real-world noise, blur, or other quality degradations, ruin
the output of a neural network. These unexpected problems can produce critical
complications, and it is surprising that there has only been minimal research
into the effects of noise in the deep neural network model. Therefore, we
present an exhaustive investigation into the effect of noise in image
classification and suggest a generalized architecture of a dual-channel model
to treat quality degraded input images. We compare the proposed dual-channel
model with a simple single model and show it improves the overall performance
of neural networks on various types of quality degraded input datasets.