pwc-archive/papers-with-abstracts huggingface dataset:
DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition
Authors: Abhishek Gangwar, Akanksha Joshi, Padmaja Joshi, R. Raghavendra
Date: 2019-02-06 00:00:00
Tasks: Iris Recognition, Iris Segmentation, Segmentation

We first, introduce a deep learning based framework named as DeepIrisNet2 for
visible spectrum and NIR Iris representation. The framework can work without
classical iris normalization step or very accurate iris segmentation; allowing
to work under non-ideal situation. The framework contains spatial transformer
layers to handle deformation and supervision branches after certain
intermediate layers to mitigate overfitting. In addition, we present a dual CNN
iris segmentation pipeline comprising of a iris/pupil bounding boxes detection
network and a semantic pixel-wise segmentation network. Furthermore, to get
compact templates, we present a strategy to generate binary iris codes using
DeepIrisNet2. Since, no ground truth dataset are available for CNN training for
iris segmentation, We build large scale hand labeled datasets and make them
public; i) iris, pupil bounding boxes, ii) labeled iris texture. The networks
are evaluated on challenging ND-IRIS-0405, UBIRIS.v2, MICHE-I, and CASIA v4
Interval datasets. Proposed approach significantly improves the
state-of-the-art and achieve outstanding performance surpassing all previous
methods.
Deep Learning for Bridge Load Capacity Estimation in Post-Disaster and -Conflict Zones
Authors: Arya Pamuncak, Weisi Guo, Ahmed Soliman Khaled, Irwanda Laory
Date: 2019-02-05 00:00:00
Tasks: Binary Classification, Capacity Estimation, General Classification

Many post-disaster and -conflict regions do not have sufficient data on their
transportation infrastructure assets, hindering both mobility and
reconstruction. In particular, as the number of aging and deteriorating bridges
increase, it is necessary to quantify their load characteristics in order to
inform maintenance and prevent failure. The load carrying capacity and the
design load are considered as the main aspects of any civil structures. Human
examination can be costly and slow when expertise is lacking in challenging
scenarios. In this paper, we propose to employ deep learning as method to
estimate the load carrying capacity from crowd sourced images. A new
convolutional neural network architecture is trained on data from over 6000
bridges, which will benefit future research and applications. We tackle
significant variations in the dataset (e.g. class interval, image completion,
image colour) and quantify their impact on the prediction accuracy, precision,
recall and F1 score. Finally, practical optimisation is performed by converting
multiclass classification into binary classification to achieve a promising
field use performance.
Multi-Kernel Prediction Networks for Denoising of Burst Images
Authors: Talmaj Marinč, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge, Wojciech Samek
Date: 2019-02-05 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising, Prediction

In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernels which are convolved with a set of successively taken images (burst) to obtain a clear image. We propose a deep neural network based approach called Multi-Kernel Prediction Networks (MKPN) for burst image denoising. MKPN predicts kernels of not just one size but of varying sizes and performs fusion of these different kernels resulting in one kernel per pixel. The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency. Experimental results reveal that MKPN outperforms state-of-the-art on our synthetic datasets with different noise levels.
Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network
Authors: Guo-Qiang Zhang, Haopeng Li, Fabian Wenger
Date: 2019-02-04 00:00:00
Tasks: Object, object-detection, Object Detection, Position

This paper considers object detection and 3D estimation using an FMCW radar.
The state-of-the-art deep learning framework is employed instead of using
traditional signal processing. In preparing the radar training data, the ground
truth of an object orientation in 3D space is provided by conducting image
analysis, of which the images are obtained through a coupled camera to the
radar device. To ensure successful training of a fully convolutional network
(FCN), we propose a normalization method, which is found to be essential to be
applied to the radar signal before feeding into the neural network. The system
after proper training is able to first detect the presence of an object in an
environment. If it does, the system then further produces an estimation of its
3D position. Experimental results show that the proposed system can be
successfully trained and employed for detecting a car and further estimating
its 3D position in a noisy environment.
Realistic Image Generation using Region-phrase Attention
Authors: Wanming Huang, Yida Xu, Ian Oppermann
Date: 2019-02-04 00:00:00
Tasks: Generative Adversarial Network, Image Generation, POS, Sentence

The Generative Adversarial Network (GAN) has recently been applied to
generate synthetic images from text. Despite significant advances, most current
state-of-the-art algorithms are regular-grid region based; when attention is
used, it is mainly applied between individual regular-grid regions and a word.
These approaches are sufficient to generate images that contain a single object
in its foreground, such as a "bird" or "flower". However, natural languages
often involve complex foreground objects and the background may also constitute
a variable portion of the generated image. Therefore, the regular-grid based
image attention weights may not necessarily concentrate on the intended
foreground region(s), which in turn, results in an unnatural looking image.
Additionally, individual words such as "a", "blue" and "shirt" do not
necessarily provide a full visual context unless they are applied together. For
this reason, in our paper, we proposed a novel method in which we introduced an
additional set of attentions between true-grid regions and word phrases. The
true-grid region is derived using a set of auxiliary bounding boxes. These
auxiliary bounding boxes serve as superior location indicators to where the
alignment and attention should be drawn with the word phrases. Word phrases are
derived from analysing Part-of-Speech (POS) results. We perform experiments on
this novel network architecture using the Microsoft Common Objects in Context
(MSCOCO) dataset and the model generates $256 \times 256$ conditioned on a
short sentence description. Our proposed approach is capable of generating more
realistic images compared with the current state-of-the-art algorithms.
An Algorithm Unrolling Approach to Deep Image Deblurring
Authors: Yuelong Li, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar
Date: 2019-02-09 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring, Rolling Shutter Correction

While neural networks have achieved vastly enhanced performance over
traditional iterative methods in many cases, they are generally empirically
designed and the underlying structures are difficult to interpret. The
algorithm unrolling approach has helped connect iterative algorithms to neural
network architectures. However, such connections have not been made yet for
blind image deblurring. In this paper, we propose a neural network architecture
that advances this idea. We first present an iterative algorithm that may be
considered a generalization of the traditional total-variation regularization
method on the gradient domain, and subsequently unroll the half-quadratic
splitting algorithm to construct a neural network. Our proposed deep network
achieves significant practical performance gains while enjoying
interpretability at the same time. Experimental results show that our approach
outperforms many state-of-the-art methods.
Data-Driven Vehicle Trajectory Forecasting
Authors: Shayan Jawed, Eya Boumaiza, Josif Grabocka, Lars Schmidt-Thieme
Date: 2019-02-09 00:00:00
Tasks: Trajectory Forecasting

An active area of research is to increase the safety of self-driving
vehicles. Although safety cannot be guarenteed completely, the capability of a
vehicle to predict the future trajectories of its surrounding vehicles could
help ensure this notion of safety to a greater deal. We cast the trajectory
forecast problem in a multi-time step forecasting problem and develop a
Convolutional Neural Network based approach to learn from trajectory sequences
generated from completely raw dataset in real-time. Results show improvement
over baselines.
Improving Deep Image Clustering With Spatial Transformer Layers
Authors: Thiago V. M. Souza, Cleber Zanchettin
Date: 2019-02-09 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Deep Learning, Image Clustering

Image clustering is an important but challenging task in machine learning. As in most image processing areas, the latest improvements came from models based on the deep learning approach. However, classical deep learning methods have problems to deal with spatial image transformations like scale and rotation. In this paper, we propose the use of visual attention techniques to reduce this problem in image clustering methods. We evaluate the combination of a deep image clustering model called Deep Adaptive Clustering (DAC) with the Spatial Transformer Networks (STN). The proposed model is evaluated in the datasets MNIST and FashionMNIST and outperformed the baseline model.
Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering
Authors: James M. Murphy, Mauro Maggioni
Date: 2019-02-08 00:00:00
Tasks: Clustering, Density Estimation, hyperspectral image clustering, Image Clustering

An unsupervised learning algorithm to cluster hyperspectral image (HSI) data
is proposed that exploits spatially-regularized random walks. Markov diffusions
are defined on the space of HSI spectra with transitions constrained to near
spatial neighbors. The explicit incorporation of spatial regularity into the
diffusion construction leads to smoother random processes that are more adapted
for unsupervised machine learning than those based on spectra alone. The
regularized diffusion process is subsequently used to embed the
high-dimensional HSI into a lower dimensional space through diffusion
distances. Cluster modes are computed using density estimation and diffusion
distances, and all other points are labeled according to these modes. The
proposed method has low computational complexity and performs competitively
against state-of-the-art HSI clustering algorithms on real data. In particular,
the proposed spatial regularization confers an empirical advantage over
non-regularized methods.
Instance Segmentation as Image Segmentation Annotation
Authors: Thomio Watanabe, Denis Wolf
Date: 2019-02-01 00:00:00
Tasks: Decoder, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

The instance segmentation problem intends to precisely detect and delineate
objects in images. Most of the current solutions rely on deep convolutional
neural networks but despite this fact proposed solutions are very diverse. Some
solutions approach the problem as a network problem, where they use several
networks or specialize a single network to solve several tasks. A different
approach tries to solve the problem as an annotation problem, where the
instance information is encoded in a mathematical representation. This work
proposes a solution based in the DCME technique to solve the instance
segmentation with a single segmentation network. Different from others, the
segmentation network decoder is not specialized in a multi-task network.
Instead, the network encoder is repurposed to classify image objects, reducing
the computational cost of the solution.
Estimating Individualized Treatment Regimes from Crossover Designs
Authors: Crystal T. Nguyen, Daniel J. Luckett, Anna R. Kahkoska, Grace E. Shearrer, Donna Spruijt-Metz, Jaimie N. Davis, Michael R. Kosorok
Date: 2019-02-05 00:00:00

The field of precision medicine aims to tailor treatment based on
patient-specific factors in a reproducible way. To this end, estimating an
optimal individualized treatment regime (ITR) that recommends treatment
decisions based on patient characteristics to maximize the mean of a
pre-specified outcome is of particular interest. Several methods have been
proposed for estimating an optimal ITR from clinical trial data in the parallel
group setting where each subject is randomized to a single intervention.
However, little work has been done in the area of estimating the optimal ITR
from crossover study designs. Such designs naturally lend themselves to
precision medicine, because they allow for observing the response to multiple
treatments for each patient. In this paper, we introduce a method for
estimating the optimal ITR using data from a 2x2 crossover study with or
without carryover effects. The proposed method is similar to policy search
methods such as outcome weighted learning; however, we take advantage of the
crossover design by using the difference in responses under each treatment as
the observed reward. We establish Fisher and global consistency, present
numerical experiments, and analyze data from a feeding trial to demonstrate the
improved performance of the proposed method compared to standard methods for a
parallel study design.
On the CVP for the root lattices via folding with deep ReLU neural networks
Authors: Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, Loic Brunel
Date: 2019-02-06 00:00:00
Tasks: General Classification

Point lattices and their decoding via neural networks are considered in this
paper. Lattice decoding in Rn, known as the closest vector problem (CVP),
becomes a classification problem in the fundamental parallelotope with a
piecewise linear function defining the boundary. Theoretical results are
obtained by studying root lattices. We show how the number of pieces in the
boundary function reduces dramatically with folding, from exponential to
linear. This translates into a two-layer ReLU network requiring a number of
neurons growing exponentially in n to solve the CVP, whereas this complexity
becomes polynomial in n for a deep ReLU network.
Variational Bayesian Phylogenetic Inference
Authors: Cheng Zhang, Frederick A. Matsen IV
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Variational Inference

Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We approximate the true posterior using an expressive graphical model for tree distributions, called a subsplit Bayesian network, together with appropriate branch length distributions. We train the variational approximation via stochastic gradient ascent and adopt multi-sample based gradient estimators for different latent variables separately to handle the composite latent space of phylogenetic models. We show that our structured variational approximations are flexible enough to provide comparable posterior estimation to MCMC, while requiring less computation due to a more efficient tree exploration mechanism enabled by variational inference. Moreover, the variational approximations can be readily used for further statistical analysis such as marginal likelihood estimation for model comparison via importance sampling. Experiments on both synthetic data and real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Authors: Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell
Date: 2010-11-02 00:00:00
Tasks: Imitation Learning, Structured Prediction

Sequential prediction problems such as imitation learning, where future
observations depend on previous predictions (actions), violate the common
i.i.d. assumptions made in statistical learning. This leads to poor performance
in theory and often in practice. Some recent approaches provide stronger
guarantees in this setting, but remain somewhat unsatisfactory as they train
either non-stationary or stochastic policies and require a large number of
iterations. In this paper, we propose a new iterative algorithm, which trains a
stationary deterministic policy, that can be seen as a no regret algorithm in
an online learning setting. We show that any such no regret algorithm, combined
with additional reduction assumptions, must find a policy with good performance
under the distribution of observations it induces in such sequential settings.
We demonstrate that this new approach outperforms previous approaches on two
challenging imitation learning problems and a benchmark sequence labeling
problem.
Recent progress in semantic image segmentation
Authors: Xiaolong Liu, Zhidong Deng, Yuhan Yang
Date: 2018-09-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Semantic image segmentation, which becomes one of the key applications in
image processing and computer vision domain, has been used in multiple domains
such as medical area and intelligent transportation. Lots of benchmark datasets
are released for researchers to verify their algorithms. Semantic segmentation
has been studied for many years. Since the emergence of Deep Neural Network
(DNN), segmentation has made a tremendous progress. In this paper, we divide
semantic image segmentation methods into two categories: traditional and recent
DNN method. Firstly, we briefly summarize the traditional method as well as
datasets released for segmentation, then we comprehensively investigate recent
methods based on DNN which are described in the eight aspects: fully
convolutional network, upsample ways, FCN joint with CRF methods, dilated
convolution approaches, progresses in backbone network, pyramid methods,
Multi-level feature and multi-stage method, supervised, weakly-supervised and
unsupervised methods. Finally, a conclusion in this area is drawn.
Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
Authors: Christian L. Vestergaard, Mathieu Génois
Date: 2015-04-03 00:00:00

Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images
Authors: Taha J. Alhindi, Shivam Kalra, Ka Hin Ng, Anika Afrin, Hamid. R. Tizhoosh
Date: 2018-05-03 00:00:00
Tasks: Classification, image-classification, Image Classification, Medical Image Analysis

Medical image analysis has become a topic under the spotlight in recent
years. There is a significant progress in medical image research concerning the
usage of machine learning. However, there are still numerous questions and
problems awaiting answers and solutions, respectively. In the present study,
comparison of three classification models is conducted using features extracted
using local binary patterns, the histogram of gradients, and a pre-trained deep
network. Three common image classification methods, including support vector
machines, decision trees, and artificial neural networks are used to classify
feature vectors obtained by different feature extractors. We use KIMIA Path960,
a publicly available dataset of $960$ histopathology images extracted from $20$
different tissue scans to test the accuracy of classification and feature
extractions models used in the study, specifically for the histopathology
images. SVM achieves the highest accuracy of $90.52\%$ using local binary
patterns as features which surpasses the accuracy obtained by deep features,
namely $81.14\%$.
BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
Authors: Shunfu Mao, Yihan Jiang, Edwin Basil Mathew, Sreeram Kannan
Date: 2019-02-14 00:00:00
Tasks: Bayesian Optimization

High throughput sequencing of RNA (RNA-Seq) can provide us with millions of
short fragments of RNA transcripts from a sample. How to better recover the
original RNA transcripts from those fragments (RNA-Seq assembly) is still a
difficult task. For example, RNA-Seq assembly tools typically require
hyper-parameter tuning to achieve good performance for particular datasets.
This kind of tuning is usually unintuitive and time-consuming. Consequently,
users often resort to default parameters, which do not guarantee consistent
good performance for various datasets.
  Here we propose BOAssembler (https://github.com/olivomao/boassembler), a
framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based
on Bayesian Optimization principles. Experiments show this data-driven approach
is effective to improve the overall assembly performance. The approach would be
helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly,
for future bioinformatics benchmark studies.
A Convolutional Neural Network for Modelling Sentences
Authors: Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
Date: 2014-04-08 00:00:00
Proceeding: ACL 2014 6
Tasks: Sentence

The ability to accurately represent sentences is central to language
understanding. We describe a convolutional architecture dubbed the Dynamic
Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of
sentences. The network uses Dynamic k-Max Pooling, a global pooling operation
over linear sequences. The network handles input sentences of varying length
and induces a feature graph over the sentence that is capable of explicitly
capturing short and long-range relations. The network does not rely on a parse
tree and is easily applicable to any language. We test the DCNN in four
experiments: small scale binary and multi-class sentiment prediction, six-way
question classification and Twitter sentiment prediction by distant
supervision. The network achieves excellent performance in the first three
tasks and a greater than 25% error reduction in the last task with respect to
the strongest baseline.