pwc-archive/papers-with-abstracts huggingface dataset:
Single Classifier-based Passive System for Source Printer Classification using Local Texture Features
Authors: Sharad Joshi, Nitin Khanna
Date: 2017-06-22 00:00:00
Tasks: General Classification, Optical Character Recognition (OCR)

An important aspect of examining printed documents for potential forgeries
and copyright infringement is the identification of source printer as it can be
helpful for ascertaining the leak and detecting forged documents. This paper
proposes a system for classification of source printer from scanned images of
printed documents using all the printed letters simultaneously. This system
uses local texture patterns based features and a single classifier for
classifying all the printed letters. Letters are extracted from scanned images
using connected component analysis followed by morphological filtering without
the need of using an OCR. Each letter is sub-divided into a flat region and an
edge region, and local tetra patterns are estimated separately for these two
regions. A strategically constructed pooling technique is used to extract the
final feature vectors. The proposed method has been tested on both a publicly
available dataset of 10 printers and a new dataset of 18 printers scanned at a
resolution of 600 dpi as well as 300 dpi printed in four different fonts. The
results indicate shape independence property in the proposed method as using a
single classifier it outperforms existing handcrafted feature-based methods and
needs much smaller number of training pages by using all the printed letters.
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Authors: Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
Date: 2017-05-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning, Dynamic neural networks

New types of machine learning hardware in development and entering the market
hold the promise of revolutionizing deep learning in a manner as profound as
GPUs. However, existing software frameworks and training algorithms for deep
learning have yet to evolve to fully leverage the capability of the new wave of
silicon. We already see the limitations of existing algorithms for models that
exploit structured input via complex and instance-dependent control flow, which
prohibits minibatching. We present an asynchronous model-parallel (AMP)
training algorithm that is specifically motivated by training on networks of
interconnected devices. Through an implementation on multi-core CPUs, we show
that AMP training converges to the same accuracy as conventional synchronous
training algorithms in a similar number of epochs, but utilizes the available
hardware more efficiently even for small minibatch sizes, resulting in
significantly shorter overall training times. Our framework opens the door for
scaling up a new class of deep learning models that cannot be efficiently
trained today.
Universal Sampling Rate Distortion
Authors: Vinay Praneeth Boda, Prakash Narayan
Date: 2017-06-22 00:00:00

We examine the coordinated and universal rate-efficient sampling of a subset
of correlated discrete memoryless sources followed by lossy compression of the
sampled sources. The goal is to reconstruct a predesignated subset of sources
within a specified level of distortion. The combined sampling mechanism and
rate distortion code are universal in that they are devised to perform robustly
without exact knowledge of the underlying joint probability distribution of the
sources. In Bayesian as well as nonBayesian settings, single-letter
characterizations are provided for the universal sampling rate distortion
function for fixed-set sampling, independent random sampling and memoryless
random sampling. It is illustrated how these sampling mechanisms are
successively better. Our achievability proofs bring forth new schemes for joint
source distribution-learning and lossy compression.
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Authors: Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin
Date: 2016-07-13 00:00:00
Proceeding: ICML 2017 8

Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources. In this work, we propose a data-driven approach that leverages the approximation power of deep-learning with the precision of standard solvers to obtain fast and highly realistic simulations. Our method solves the incompressible Euler equations using the standard operator splitting method, in which a large sparse linear system with many free parameters must be solved. We use a Convolutional Network with a highly tailored architecture, trained using a novel unsupervised learning framework to solve the linear system. We present real-time 2D and 3D simulations that outperform recently proposed data-driven methods; the obtained results are realistic and show good generalization properties.
Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features
Authors: Ting Sun, Lin Sun, Dit-yan Yeung
Date: 2017-06-22 00:00:00
Tasks: Object

Fine-grained categorization can benefit from part-based features which reveal
subtle visual differences between object categories. Handcrafted features have
been widely used for part detection and classification. Although a recent trend
seeks to learn such features automatically using powerful deep learning models
such as convolutional neural networks (CNN), their training and possibly also
testing require manually provided annotations which are costly to obtain. To
relax these requirements, we assume in this study a general problem setting in
which the raw images are only provided with object-level class labels for model
training with no other side information needed. Specifically, by extracting and
interpreting the hierarchical hidden layer features learned by a CNN, we
propose an elaborate CNN-based system for fine-grained categorization. When
evaluated on the Caltech-UCSD Birds-200-2011, FGVC-Aircraft, Cars and Stanford
dogs datasets under the setting that only object-level class labels are used
for training and no other annotations are available for both training and
testing, our method achieves impressive performance that is superior or
comparable to the state of the art. Moreover, it sheds some light on ingenious
use of the hierarchical features learned by CNN which has wide applicability
well beyond the current fine-grained categorization task.
Learning across scales - A multiscale method for Convolution Neural Networks
Authors: Eldad Haber, Lars Ruthotto, Elliot Holtham, Seong-Hwan Jun
Date: 2017-03-06 00:00:00
Tasks: Relation

In this work we establish the relation between optimal control and training
deep Convolution Neural Networks (CNNs). We show that the forward propagation
in CNNs can be interpreted as a time-dependent nonlinear differential equation
and learning as controlling the parameters of the differential equation such
that the network approximates the data-label relation for given training data.
Using this continuous interpretation we derive two new methods to scale CNNs
with respect to two different dimensions. The first class of multiscale methods
connects low-resolution and high-resolution data through prolongation and
restriction of CNN parameters. We demonstrate that this enables classifying
high-resolution images using CNNs trained with low-resolution images and vice
versa and warm-starting the learning process. The second class of multiscale
methods connects shallow and deep networks and leads to new training strategies
that gradually increase the depths of the CNN while re-using parameters for
initializations.
On Detection of Faint Edges in Noisy Images
Authors: Nati Ofir, Meirav Galun, Sharon Alpert, Achi Brandt, Boaz Nadler, Ronen Basri
Date: 2017-06-22 00:00:00
Tasks: Edge Detection

A fundamental question for edge detection in noisy images is how faint can an
edge be and still be detected. In this paper we offer a formalism to study this
question and subsequently introduce computationally efficient multiscale edge
detection algorithms designed to detect faint edges in noisy images. In our
formalism we view edge detection as a search in a discrete, though potentially
large, set of feasible curves. First, we derive approximate expressions for the
detection threshold as a function of curve length and the complexity of the
search space. We then present two edge detection algorithms, one for straight
edges, and the second for curved ones. Both algorithms efficiently search for
edges in a large set of candidates by hierarchically constructing difference
filters that match the curves traced by the sought edges. We demonstrate the
utility of our algorithms in both simulations and applications involving
challenging real images. Finally, based on these principles, we develop an
algorithm for fiber detection and enhancement. We exemplify its utility to
reveal and enhance nerve axons in light microscopy images.
Tracking Single-Cells in Overcrowded Bacterial Colonies
Authors: Athanasios D. Balomenos, Panagiotis Tsakanikas, Elias S. Manolakos
Date: 2017-06-22 00:00:00
Tasks: Cell Tracking, Motion Estimation, Video Compression

Cell tracking enables data extraction from time-lapse "cell movies" and
promotes modeling biological processes at the single-cell level. We introduce a
new fully automated computational strategy to track accurately cells across
frames in time-lapse movies. Our method is based on a dynamic neighborhoods
formation and matching approach, inspired by motion estimation algorithms for
video compression. Moreover, it exploits "divide and conquer" opportunities to
solve effectively the challenging cells tracking problem in overcrowded
bacterial colonies. Using cell movies generated by different labs we
demonstrate that the accuracy of the proposed method remains very high (exceeds
97%) even when analyzing large overcrowded microbial colonies.
Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis
Authors: Athanasios D. Balomenos, Elias S. Manolakos
Date: 2017-06-22 00:00:00
Tasks: Cell Segmentation, Cell Tracking, Segmentation

Cell segmentation and tracking allow us to extract a plethora of cell
attributes from bacterial time-lapse cell movies, thus promoting computational
modeling and simulation of biological processes down to the single-cell level.
However, to analyze successfully complex cell movies, imaging multiple
interacting bacterial clones as they grow and merge to generate overcrowded
bacterial communities with thousands of cells in the field of view,
segmentation results should be near perfect to warrant good tracking results.
We introduce here a fully automated closed-loop bio-inspired computational
strategy that exploits prior knowledge about the expected structure of a
colony's lineage tree to locate and correct segmentation errors in analyzed
movie frames. We show that this correction strategy is effective, resulting in
improved cell tracking and consequently trustworthy deep colony lineage trees.
Our image analysis approach has the unique capability to keep tracking cells
even after clonal subpopulations merge in the movie. This enables the
reconstruction of the complete Forest of Lineage Trees (FLT) representation of
evolving multi-clonal bacterial communities. Moreover, the percentage of valid
cell trajectories extracted from the image analysis almost doubles after
segmentation correction. This plethora of trustworthy data extracted from a
complex cell movie analysis enables single-cell analytics as a tool for
addressing compelling questions for human health, such as understanding the
role of single-cell stochasticity in antibiotics resistance without losing site
of the inter-cellular interactions and microenvironment effects that may shape
it.
An approach to reachability analysis for feed-forward ReLU neural networks
Authors: Alessio Lomuscio, Lalit Maganti
Date: 2017-06-22 00:00:00

We study the reachability problem for systems implemented as feed-forward
neural networks whose activation function is implemented via ReLU functions. We
draw a correspondence between establishing whether some arbitrary output can
ever be outputed by a neural system and linear problems characterising a neural
system of interest. We present a methodology to solve cases of practical
interest by means of a state-of-the-art linear programs solver. We evaluate the
technique presented by discussing the experimental results obtained by
analysing reachability properties for a number of benchmarks in the literature.
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
Authors: Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
Date: 2017-06-22 00:00:00
Tasks: Pancreas Segmentation, Segmentation

Automatic segmentation of an organ and its cystic region is a prerequisite of
computer-aided diagnosis. In this paper, we focus on pancreatic cyst
segmentation in abdominal CT scan. This task is important and very useful in
clinical practice yet challenging due to the low contrast in boundary, the
variability in location, shape and the different stages of the pancreatic
cancer. Inspired by the high relevance between the location of a pancreas and
its cystic region, we introduce extra deep supervision into the segmentation
network, so that cyst segmentation can be improved with the help of relatively
easier pancreas segmentation. Under a reasonable transformation function, our
approach can be factorized into two stages, and each stage can be efficiently
optimized via gradient back-propagation throughout the deep networks. We
collect a new dataset with 131 pathological samples, which, to the best of our
knowledge, is the largest set for pancreatic cyst segmentation. Without human
assistance, our approach reports a 63.44% average accuracy, measured by the
Dice-S{\o}rensen coefficient (DSC), which is higher than the number (60.46%)
without deep supervision.
K+ Means : An Enhancement Over K-Means Clustering Algorithm
Authors: Srikanta Kolay, Kumar Sankar Ray, Abhoy Chand Mondal
Date: 2017-06-08 00:00:00
Tasks: Clustering

K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning
algorithms that solve the well-known clustering problem. The procedure follows
a simple and easy way to classify a given data set to a predefined, say K
number of clusters. Determination of K is a difficult job and it is not known
that which value of K can partition the objects as per our intuition. To
overcome this problem we proposed K+ Means algorithm. This algorithm is an
enhancement over K-Means algorithm.
Unimodal probability distributions for deep ordinal classification
Authors: Christopher Beckham, Christopher Pal
Date: 2017-05-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Classification, General Classification, Ordinal Classification

Probability distributions produced by the cross-entropy loss for ordinal
classification problems can possess undesired properties. We propose a
straightforward technique to constrain discrete ordinal probability
distributions to be unimodal via the use of the Poisson and binomial
probability distributions. We evaluate this approach in the context of deep
learning on two large ordinal image datasets, obtaining promising results.
A Minimal Developmental Model Can Increase Evolvability in Soft Robots
Authors: Sam Kriegman, Nick Cheney, Francesco Corucci, Josh C. Bongard
Date: 2017-06-22 00:00:00

Different subsystems of organisms adapt over many time scales, such as rapid
changes in the nervous system (learning), slower morphological and neurological
change over the lifetime of the organism (postnatal development), and change
over many generations (evolution). Much work has focused on instantiating
learning or evolution in robots, but relatively little on development. Although
many theories have been forwarded as to how development can aid evolution, it
is difficult to isolate each such proposed mechanism. Thus, here we introduce a
minimal yet embodied model of development: the body of the robot changes over
its lifetime, yet growth is not influenced by the environment. We show that
even this simple developmental model confers evolvability because it allows
evolution to sweep over a larger range of body plans than an equivalent
non-developmental system, and subsequent heterochronic mutations 'lock in' this
body plan in more morphologically-static descendants. Future work will involve
gradually complexifying the developmental model to determine when and how such
added complexity increases evolvability.
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Authors: Geoffrey Jones, Neil T. Clancy, Xiaofei Du, Maria Robu, Simon Arridge, Daniel S. Elson, Danail Stoyanov
Date: 2017-06-22 00:00:00
Tasks: GPU

Tissue oxygenation and perfusion can be an indicator for organ viability
during minimally invasive surgery, for example allowing real-time assessment of
tissue perfusion and oxygen saturation. Multispectral imaging is an optical
modality that can inspect tissue perfusion in wide field images without
contact. In this paper, we present a novel, fast method for using RGB images
for MSI, which while limiting the spectral resolution of the modality allows
normal laparoscopic systems to be used. We exploit the discrete Haar
decomposition to separate individual video frames into low pass and directional
coefficients and we utilise a different multispectral estimation technique on
each. The increase in speed is achieved by using fast Tikhonov regularisation
on the directional coefficients and more accurate Bayesian estimation on the
low pass component. The pipeline is implemented using a graphics processing
unit (GPU) architecture and achieves a frame rate of approximately 15Hz. We
validate the method on animal models and on human data captured using a da
Vinci stereo laparoscope.
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Authors: Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Date: 2017-06-22 00:00:00
Proceeding: ICML 2017 8
Tasks: Classification, Gaussian Processes, General Classification, Multi-class Classification, Variational Inference

This paper describes an expectation propagation (EP) method for multi-class
classification with Gaussian processes that scales well to very large datasets.
In such a method the estimate of the log-marginal-likelihood involves a sum
across the data instances. This enables efficient training using stochastic
gradients and mini-batches. When this type of training is used, the
computational cost does not depend on the number of data instances $N$.
Furthermore, extra assumptions in the approximate inference process make the
memory cost independent of $N$. The consequence is that the proposed EP method
can be used on datasets with millions of instances. We compare empirically this
method with alternative approaches that approximate the required computations
using variational inference. The results show that it performs similar or even
better than these techniques, which sometimes give significantly worse
predictive distributions in terms of the test log-likelihood. Besides this, the
training process of the proposed approach also seems to converge in a smaller
number of iterations.
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning
Authors: Jingjia Huang, Nannan Li, Tao Zhang, Ge Li
Date: 2017-06-22 00:00:00
Tasks: Action Detection, Position, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Existing action detection algorithms usually generate action proposals
through an extensive search over the video at multiple temporal scales, which
brings about huge computational overhead and deviates from the human perception
procedure. We argue that the process of detecting actions should be naturally
one of observation and refinement: observe the current window and refine the
span of attended window to cover true action regions. In this paper, we propose
an active action proposal model that learns to find actions through
continuously adjusting the temporal bounds in a self-adaptive way. The whole
process can be deemed as an agent, which is firstly placed at a position in the
video at random, adopts a sequence of transformations on the current attended
region to discover actions according to a learned policy. We utilize
reinforcement learning, especially the Deep Q-learning algorithm to learn the
agent's decision policy. In addition, we use temporal pooling operation to
extract more effective feature representation for the long temporal window, and
design a regression network to adjust the position offsets between predicted
results and the ground truth. Experiment results on THUMOS 2014 validate the
effectiveness of the proposed approach, which can achieve competitive
performance with current action detection algorithms via much fewer proposals.
Inference of Haemoglobin Concentration From Stereo RGB
Authors: Geoffrey Jones, Neil T. Clancy, Yusuf Helo, Simon Arridge, Daniel S. Elson, Danail Stoyanov
Date: 2016-07-11 00:00:00
Tasks: Computational Efficiency

Multispectral imaging (MSI) can provide information about tissue oxygenation,
perfusion and potentially function during surgery. In this paper we present a
novel, near real-time technique for intrinsic measurements of total haemoglobin
(THb) and blood oxygenation (SO2) in tissue using only RGB images from a stereo
laparoscope. The high degree of spectral overlap between channels makes
inference of haemoglobin concentration challenging, non-linear and under
constrained. We decompose the problem into two constrained linear sub-problems
and show that with Tikhonov regularisation the estimation significantly
improves, giving robust estimation of the Thb. We demonstrate by using the
co-registered stereo image data from two cameras it is possible to get robust
SO2 estimation as well. Our method is closed from, providing computational
efficiency even with multiple cameras. The method we present requires only
spectral response calibration of each camera, without modification of existing
laparoscopic imaging hardware. We validate our technique on synthetic data from
Monte Carlo simulation % of light transport through soft tissue containing
submerged blood vessels and further, in vivo, on a multispectral porcine data
set.
Automatic Quality Estimation for ASR System Combination
Authors: Shahab Jalalvand, Matteo Negri, Daniele Falavigna, Marco Matassoni, Marco Turchi
Date: 2017-06-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%.