pwc-archive/papers-with-abstracts huggingface dataset:
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning
Authors: Francis Tom, Debdoot Sheet
Date: 2017-12-21 00:00:00
Tasks: Generative Adversarial Network

Ultrasound imaging makes use of backscattering of waves during their
interaction with scatterers present in biological tissues. Simulation of
synthetic ultrasound images is a challenging problem on account of inability to
accurately model various factors of which some include intra-/inter scanline
interference, transducer to surface coupling, artifacts on transducer elements,
inhomogeneous shadowing and nonlinear attenuation. Current approaches typically
solve wave space equations making them computationally expensive and slow to
operate. We propose a generative adversarial network (GAN) inspired approach
for fast simulation of patho-realistic ultrasound images. We apply the
framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation
performed using pseudo B-mode ultrasound image simulator yields speckle mapping
of a digitally defined phantom. The stage I GAN subsequently refines them to
preserve tissue specific speckle intensities. The stage II GAN further refines
them to generate high resolution images with patho-realistic speckle profiles.
We evaluate patho-realism of simulated images with a visual Turing test
indicating an equivocal confusion in discriminating simulated from real. We
also quantify the shift in tissue specific intensity distributions of the real
and simulated images to prove their similarity.
Segmenting Sky Pixels in Images
Authors: Cecilia La Place, Aisha Urooj Khan, Ali Borji
Date: 2017-12-26 00:00:00
Tasks: Scene Parsing

Outdoor scene parsing models are often trained on ideal datasets and produce
quality results. However, this leads to a discrepancy when applied to the real
world. The quality of scene parsing, particularly sky classification, decreases
in night time images, images involving varying weather conditions, and scene
changes due to seasonal weather. This project focuses on approaching these
challenges by using a state-of-the-art model in conjunction with a non-ideal
dataset: SkyFinder and a subset from SUN database with Sky object. We focus
specifically on sky segmentation, the task of determining sky and not-sky
pixels, and improving upon an existing state-of-the-art model: RefineNet. As a
result of our efforts, we have seen an improvement of 10-15% in the average MCR
compared to the prior methods on SkyFinder dataset. We have also improved from
an off-the shelf-model in terms of average mIOU by nearly 35%. Further, we
analyze our trained models on images w.r.t two aspects: times of day and
weather, and find that, in spite of facing same challenges as prior methods,
our trained models significantly outperform them.
In-RDBMS Hardware Acceleration of Advanced Analytics
Authors: Divya Mahajan, Joon Kyung Kim, Jacob Sacks, Adel Ardalan, Arun Kumar, Hadi Esmaeilzadeh
Date: 2018-01-08 00:00:00

The data revolution is fueled by advances in machine learning, databases, and
hardware design. Programmable accelerators are making their way into each of
these areas independently. As such, there is a void of solutions that enables
hardware acceleration at the intersection of these disjoint fields. This paper
sets out to be the initial step towards a unifying solution for in-Database
Acceleration of Advanced Analytics (DAnA). Deploying specialized hardware, such
as FPGAs, for in-database analytics currently requires hand-designing the
hardware and manually routing the data. Instead, DAnA automatically maps a
high-level specification of advanced analytics queries to an FPGA accelerator.
The accelerator implementation is generated for a User Defined Function (UDF),
expressed as a part of an SQL query using a Python-embedded Domain-Specific
Language (DSL). To realize an efficient in-database integration, DAnA
accelerators contain a novel hardware structure, Striders, that directly
interface with the buffer pool of the database. Striders extract, cleanse, and
process the training data tuples that are consumed by a multi-threaded FPGA
engine that executes the analytics algorithm. We integrate DAnA with PostgreSQL
to generate hardware accelerators for a range of real-world and synthetic
datasets running diverse ML algorithms. Results show that DAnA-enhanced
PostgreSQL provides, on average, 8.3x end-to-end speedup for real datasets,
with a maximum of 28.2x. Moreover, DAnA-enhanced PostgreSQL is, on average,
4.0x faster than the multi-threaded Apache MADLib running on Greenplum. DAnA
provides these benefits while hiding the complexity of hardware design from
data scientists and allowing them to express the algorithm in =30-60 lines of
Python.
Online Cluster Validity Indices for Streaming Data
Authors: Masud Moshtaghi, James C. Bezdek, Sarah M. Erfani, Christopher Leckie, James Bailey
Date: 2018-01-08 00:00:00
Tasks: Clustering

Cluster analysis is used to explore structure in unlabeled data sets in a
wide range of applications. An important part of cluster analysis is validating
the quality of computationally obtained clusters. A large number of different
internal indices have been developed for validation in the offline setting.
However, this concept has not been extended to the online setting. A key
challenge is to find an efficient incremental formulation of an index that can
capture both cohesion and separation of the clusters over potentially infinite
data streams. In this paper, we develop two online versions (with and without
forgetting factors) of the Xie-Beni and Davies-Bouldin internal validity
indices, and analyze their characteristics, using two streaming clustering
algorithms (sk-means and online ellipsoidal clustering), and illustrate their
use in monitoring evolving clusters in streaming data. We also show that
incremental cluster validity indices are capable of sending a distress signal
to online monitors when evolving clusters go awry. Our numerical examples
indicate that the incremental Xie-Beni index with forgetting factor is superior
to the other three indices tested.
Weighted Contrastive Divergence
Authors: Enrique Romero Merino, Ferran Mazzanti Castrillejo, Jordi Delgado Pin, David Buchaca Prats
Date: 2018-01-08 00:00:00

Learning algorithms for energy based Boltzmann architectures that rely on
gradient descent are in general computationally prohibitive, typically due to
the exponential number of terms involved in computing the partition function.
In this way one has to resort to approximation schemes for the evaluation of
the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its
learning algorithm Contrastive Divergence (CD). It is well-known that CD has a
number of shortcomings, and its approximation to the gradient has several
drawbacks. Overcoming these defects has been the basis of much research and new
algorithms have been devised, such as persistent CD. In this manuscript we
propose a new algorithm that we call Weighted CD (WCD), built from small
modifications of the negative phase in standard CD. However small these
modifications may be, experimental work reported in this paper suggest that WCD
provides a significant improvement over standard CD and persistent CD at a
small additional computational cost.
Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing
Authors: John McKay, Isaac Gerg, Vishal Monga
Date: 2018-01-08 00:00:00
Tasks: General Classification

There are many real-world classification problems wherein the issue of data
imbalance (the case when a data set contains substantially more samples for
one/many classes than the rest) is unavoidable. While under-sampling the
problematic classes is a common solution, this is not a compelling option when
the large data class is itself diverse and/or the limited data class is
especially small. We suggest a strategy based on recent work concerning limited
data problems which utilizes a supplemental set of images with similar
properties to the limited data class to aid in the training of a neural
network. We show results for our model against other typical methods on a
real-world synthetic aperture sonar data set. Code can be found at
github.com/JohnMcKay/dataImbalance.
Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)
Authors: Noel C. F. Codella, David Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, Allan Halpern
Date: 2017-10-13 00:00:00
Tasks: General Classification, Lesion Segmentation

This article describes the design, implementation, and results of the latest
installment of the dermoscopic image analysis benchmark challenge. The goal is
to support research and development of algorithms for automated diagnosis of
melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks:
lesion segmentation, feature detection, and disease classification.
Participation involved 593 registrations, 81 pre-submissions, 46 finalized
submissions (including a 4-page manuscript), and approximately 50 attendees,
making this the largest standardized and comparative study in this field to
date. While the official challenge duration and ranking of participants has
concluded, the dataset snapshots remain available for further research and
development.
Efficient EM Training of Gaussian Mixtures with Missing Data
Authors: Olivier Delalleau, Aaron Courville, Yoshua Bengio
Date: 2012-09-04 00:00:00
Tasks: Missing Values

In data-mining applications, we are frequently faced with a large fraction of
missing entries in the data matrix, which is problematic for most discriminant
machine learning algorithms. A solution that we explore in this paper is the
use of a generative model (a mixture of Gaussians) to compute the conditional
expectation of the missing variables given the observed variables. Since
training a Gaussian mixture with many different patterns of missing values can
be computationally very expensive, we introduce a spanning-tree based algorithm
that significantly speeds up training in these conditions. We also observe that
good results can be obtained by using the generative model to fill-in the
missing values for a separate discriminant learning algorithm.
Spiking memristor logic gates are a type of time-variant perceptron
Authors: Ella M. Gale
Date: 2018-01-08 00:00:00
Tasks: Vocal Bursts Type Prediction

Memristors are low-power memory-holding resistors thought to be useful for
neuromophic computing, which can compute via spike-interactions mediated
through the device's short-term memory. Using interacting spikes, it is
possible to build an AND gate that computes OR at the same time, similarly a
full adder can be built that computes the arithmetical sum of its inputs. Here
we show how these gates can be understood by modelling the memristors as a
novel type of perceptron: one which is sensitive to input order. The
memristor's memory can change the input weights for later inputs, and thus the
memristor gates cannot be accurately described by a single perceptron,
requiring either a network of time-invarient perceptrons or a complex
time-varying self-reprogrammable perceptron. This work demonstrates the high
functionality of memristor logic gates, and also that the addition of
theasholding could enable the creation of a standard perceptron in hardware,
which may have use in building neural net chips.
In silico generation of novel, drug-like chemical matter using the LSTM neural network
Authors: Peter Ertl, Richard Lewis, Eric Martin, Valery Polyakov
Date: 2017-12-20 00:00:00
Tasks: Drug Discovery

The exploration of novel chemical spaces is one of the most important tasks
of cheminformatics when supporting the drug discovery process. Properly
designed and trained deep neural networks can provide a viable alternative to
brute-force de novo approaches or various other machine-learning techniques for
generating novel drug-like molecules. In this article we present a method to
generate molecules using a long short-term memory (LSTM) neural network and
provide an analysis of the results, including a virtual screening test. Using
the network one million drug-like molecules were generated in 2 hours. The
molecules are novel, diverse (contain numerous novel chemotypes), have good
physicochemical properties and have good synthetic accessibility, even though
these qualities were not specific constraints. Although novel, their structural
features and functional groups remain closely within the drug-like space
defined by the bioactive molecules from ChEMBL. Virtual screening using the
profile QSAR approach confirms that the potential of these novel molecules to
show bioactivity is comparable to the ChEMBL set from which they were derived.
The molecule generator written in Python used in this study is available on
request.
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
Authors: Yupei Wang, Xin Zhao, Yin Li, Kaiqi Huang
Date: 2018-01-08 00:00:00
Tasks: Edge Detection, Object Proposal Generation, Optical Flow Estimation, Semantic Segmentation

Edge detection has made significant progress with the help of deep
Convolutional Networks (ConvNet). These ConvNet based edge detectors have
approached human level performance on standard benchmarks. We provide a
systematical study of these detectors' outputs. We show that the detection
results did not accurately localize edge pixels, which can be adversarial for
tasks that require crisp edge inputs. As a remedy, we propose a novel
refinement architecture to address the challenging problem of learning a crisp
edge detector using ConvNet. Our method leverages a top-down backward
refinement pathway, and progressively increases the resolution of feature maps
to generate crisp edges. Our results achieve superior performance, surpassing
human accuracy when using standard criteria on BSDS500, and largely
outperforming state-of-the-art methods when using more strict criteria. More
importantly, we demonstrate the benefit of crisp edge maps for several
important applications in computer vision, including optical flow estimation,
object proposal generation and semantic segmentation.
What Looks Good with my Sofa: Multimodal Search Engine for Interior Design
Authors: Ivona Tautkute, Aleksandra Możejko, Wojciech Stokowiec, Tomasz Trzciński, Łukasz Brocki, Krzysztof Marasek
Date: 2017-07-21 00:00:00

In this paper, we propose a multi-modal search engine for interior design
that combines visual and textual queries. The goal of our engine is to retrieve
interior objects, e.g. furniture or wall clocks, that share visual and
aesthetic similarities with the query. Our search engine allows the user to
take a photo of a room and retrieve with a high recall a list of items
identical or visually similar to those present in the photo. Additionally, it
allows to return other items that aesthetically and stylistically fit well
together. To achieve this goal, our system blends the results obtained using
textual and visual modalities. Thanks to this blending strategy, we increase
the average style similarity score of the retrieved items by 11%. Our work is
implemented as a Web-based application and it is planned to be opened to the
public.
DeepStyle: Multimodal Search Engine for Fashion and Interior Design
Authors: Ivona Tautkute, Tomasz Trzcinski, Aleksander Skorupa, Lukasz Brocki, Krzysztof Marasek
Date: 2018-01-08 00:00:00
Tasks: Retrieval

In this paper, we propose a multimodal search engine that combines visual and
textual cues to retrieve items from a multimedia database aesthetically similar
to the query. The goal of our engine is to enable intuitive retrieval of
fashion merchandise such as clothes or furniture. Existing search engines treat
textual input only as an additional source of information about the query image
and do not correspond to the real-life scenario where the user looks for 'the
same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those
shortcomings by using a joint neural network architecture to model contextual
dependencies between features of different modalities. We prove the robustness
of this approach on two different challenging datasets of fashion items and
furniture where our DeepStyle engine outperforms baseline methods by 18-21% on
the tested datasets. Our search engine is commercially deployed and available
through a Web-based application.
Between collective intelligence and semantic web : hypermediating sites. Contribution to technologies of intelligence
Authors: Lise Verlaet, Sidonie Gallot
Date: 2018-01-08 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Form

In this paper we present a new form of access to knowledge through what we
call "hypermediator websites". These hypermediator sites are intermediate
between information devices that just scan the book culture and a "real"
hypertext writing format.
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Authors: Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan
Date: 2018-01-08 00:00:00
Tasks: Classification, Computed Tomography (CT), Data Augmentation, Diversity, General Classification, Lesion Classification, Sensitivity, Specificity

In this paper, we present a data augmentation method that generates synthetic
medical images using Generative Adversarial Networks (GANs). We propose a
training scheme that first uses classical data augmentation to enlarge the
training set and then further enlarges the data size and its diversity by
applying GAN techniques for synthetic data augmentation. Our method is
demonstrated on a limited dataset of computed tomography (CT) images of 182
liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification
performance using only classic data augmentation yielded 78.6% sensitivity and
88.4% specificity. By adding the synthetic data augmentation the results
significantly increased to 85.7% sensitivity and 92.4% specificity.
Attacking Speaker Recognition With Deep Generative Models
Authors: Wilson Cai, Anish Doshi, Rafael Valle
Date: 2018-01-08 00:00:00
Tasks: Speaker Recognition

In this paper we investigate the ability of generative adversarial networks
(GANs) to synthesize spoofing attacks on modern speaker recognition systems. We
first show that samples generated with SampleRNN and WaveNet are unable to fool
a CNN-based speaker recognition system. We propose a modification of the
Wasserstein GAN objective function to make use of data that is real but not
from the class being learned. Our semi-supervised learning method is able to
perform both targeted and untargeted attacks, raising questions related to
security in speaker authentication systems.
Toward predictive machine learning for active vision
Authors: Emmanuel Daucé
Date: 2017-10-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning

We develop a comprehensive description of the active inference framework, as
proposed by Friston (2010), under a machine-learning compliant perspective.
Stemming from a biological inspiration and the auto-encoding principles, the
sketch of a cognitive architecture is proposed that should provide ways to
implement estimation-oriented control policies. Computer simulations illustrate
the effectiveness of the approach through a foveated inspection of the input
data. The pros and cons of the control policy are analyzed in detail, showing
interesting promises in terms of processing compression. Though optimizing
future posterior entropy over the actions set is shown enough to attain locally
optimal action selection, offline calculation using class-specific saliency
maps is shown better for it saves processing costs through saccades pathways
pre-processing, with a negligible effect on the recognition/compression rates.
Transformation Forests
Authors: Torsten Hothorn, Achim Zeileis
Date: 2017-01-09 00:00:00
Tasks: Prediction Intervals, quantile regression, regression

Regression models for supervised learning problems with a continuous target
are commonly understood as models for the conditional mean of the target given
predictors. This notion is simple and therefore appealing for interpretation
and visualisation. Information about the whole underlying conditional
distribution is, however, not available from these models. A more general
understanding of regression models as models for conditional distributions
allows much broader inference from such models, for example the computation of
prediction intervals. Several random forest-type algorithms aim at estimating
conditional distributions, most prominently quantile regression forests
(Meinshausen, 2006, JMLR). We propose a novel approach based on a parametric
family of distributions characterised by their transformation function. A
dedicated novel "transformation tree" algorithm able to detect distributional
changes is developed. Based on these transformation trees, we introduce
"transformation forests" as an adaptive local likelihood estimator of
conditional distribution functions. The resulting models are fully parametric
yet very general and allow broad inference procedures, such as the model-based
bootstrap, to be applied in a straightforward way.
Learning with Feature Evolvable Streams
Authors: Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Date: 2017-06-16 00:00:00
Proceeding: NeurIPS 2017 12

Learning with streaming data has attracted much attention during the past few
years. Though most studies consider data stream with fixed features, in real
practice the features may be evolvable. For example, features of data gathered
by limited-lifespan sensors will change when these sensors are substituted by
new ones. In this paper, we propose a novel learning paradigm: \emph{Feature
Evolvable Streaming Learning} where old features would vanish and new features
would occur. Rather than relying on only the current features, we attempt to
recover the vanished features and exploit it to improve performance.
Specifically, we learn two models from the recovered features and the current
features, respectively. To benefit from the recovered features, we develop two
ensemble methods. In the first method, we combine the predictions from two
models and theoretically show that with the assistance of old features, the
performance on new features can be improved. In the second approach, we
dynamically select the best single prediction and establish a better
performance guarantee when the best model switches. Experiments on both
synthetic and real data validate the effectiveness of our proposal.
On Enhancing Genetic Algorithms Using New Crossovers
Authors: Ahmad B. A. Hassanat, Esra'a Alkafaween
Date: 2018-01-08 00:00:00

This paper investigates the use of more than one crossover operator to
enhance the performance of genetic algorithms. Novel crossover operators are
proposed such as the Collision crossover, which is based on the physical rules
of elastic collision, in addition to proposing two selection strategies for the
crossover operators, one of which is based on selecting the best crossover
operator and the other randomly selects any operator. Several experiments on
some Travelling Salesman Problems (TSP) have been conducted to evaluate the
proposed methods, which are compared to the well-known Modified crossover
operator and partially mapped Crossover (PMX) crossover. The results show the
importance of some of the proposed methods, such as the collision crossover, in
addition to the significant enhancement of the genetic algorithms performance,
particularly when using more than one crossover operator.