pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
Authors: Cecilia S. Lee, Doug M. Baughman, Aaron Y. Lee
Date: 2016-12-15 00:00:00
Tasks: General Classification, Retinal OCT Disease Classification, Specificity

Objective: The advent of Electronic Medical Records (EMR) with large
electronic imaging databases along with advances in deep neural networks with
machine learning has provided a unique opportunity to achieve milestones in
automated image analysis. Optical coherence tomography (OCT) is the most
commonly obtained imaging modality in ophthalmology and represents a dense and
rich dataset when combined with labels derived from the EMR. We sought to
determine if deep learning could be utilized to distinguish normal OCT images
from images from patients with Age-related Macular Degeneration (AMD). Methods:
Automated extraction of an OCT imaging database was performed and linked to
clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg
Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted
from EPIC. The central 11 images were selected from each OCT scan of two
cohorts of patients: normal and AMD. Cross-validation was performed using a
random subset of patients. Area under receiver operator curves (auROC) were
constructed at an independent image level, macular OCT level, and patient
level. Results: Of an extraction of 2.6 million OCT images linked to clinical
datapoints from the EMR, 52,690 normal and 48,312 AMD macular OCT images were
selected. A deep neural network was trained to categorize images as either
normal or AMD. At the image level, we achieved an auROC of 92.78% with an
accuracy of 87.63%. At the macula level, we achieved an auROC of 93.83% with an
accuracy of 88.98%. At a patient level, we achieved an auROC of 97.45% with an
accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were
92.64% and 93.69% respectively. Conclusions: Deep learning techniques are
effective for classifying OCT images. These findings have important
implications in utilizing OCT in automated screening and computer aided
diagnosis tools.
Crowdsourced Outcome Determination in Prediction Markets
Authors: Rupert Freeman, Sebastien Lahaie, David M. Pennock
Date: 2016-12-14 00:00:00
Tasks: Prediction

A prediction market is a useful means of aggregating information about a
future event. To function, the market needs a trusted entity who will verify
the true outcome in the end. Motivated by the recent introduction of
decentralized prediction markets, we introduce a mechanism that allows for the
outcome to be determined by the votes of a group of arbiters who may themselves
hold stakes in the market. Despite the potential conflict of interest, we
derive conditions under which we can incentivize arbiters to vote truthfully by
using funds raised from market fees to implement a peer prediction mechanism.
Finally, we investigate what parameter values could be used in a real-world
implementation of our mechanism.
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
Authors: Memo Akten, Mick Grierson
Date: 2016-12-14 00:00:00

We investigate a human-machine collaborative drawing environment in which an
autonomous agent sketches images while optionally allowing a user to directly
influence the agent's trajectory. We combine Monte Carlo Tree Search with image
classifiers and test both shallow models (e.g. multinomial logistic regression)
and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found
that using the shallow model, the agent produces a limited variety of images,
which are noticably recogonisable by humans. However, using the deeper models,
the agent produces a more diverse range of images, and while the agent remains
very confident (99.99%) in having achieved its objective, to humans they mostly
resemble unrecognisable 'random' noise. We relate this to recent research which
also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015}
and we discuss possible solutions and future directions for the research.
Robust Local Scaling using Conditional Quantiles of Graph Similarities
Authors: Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura
Date: 2016-12-14 00:00:00
Tasks: Clustering, graph construction

Spectral analysis of neighborhood graphs is one of the most widely used
techniques for exploratory data analysis, with applications ranging from
machine learning to social sciences. In such applications, it is typical to
first encode relationships between the data samples using an appropriate
similarity function. Popular neighborhood construction techniques such as
k-nearest neighbor (k-NN) graphs are known to be very sensitive to the choice
of parameters, and more importantly susceptible to noise and varying densities.
In this paper, we propose the use of quantile analysis to obtain local scale
estimates for neighborhood graph construction. To this end, we build an
auto-encoding neural network approach for inferring conditional quantiles of a
similarity function, which are subsequently used to obtain robust estimates of
the local scales. In addition to being highly resilient to noise or outlying
data, the proposed approach does not require extensive parameter tuning unlike
several existing methods. Using applications in spectral clustering and
single-example label propagation, we show that the proposed neighborhood graphs
outperform existing locally scaled graph construction approaches.
Border-Peeling Clustering
Authors: Hadar Averbuch-Elor, Nadav Bar, Daniel Cohen-Or
Date: 2016-12-14 00:00:00
Tasks: Clustering

In this paper, we present a novel non-parametric clustering technique. Our technique is based on the notion that each latent cluster is comprised of layers that surround its core, where the external layers, or border points, implicitly separate the clusters. Unlike previous techniques, such as DBSCAN, where the cores of the clusters are defined directly by their densities, here the latent cores are revealed by a progressive peeling of the border points. Analyzing the density of the local neighborhoods allows identifying the border points and associating them with points of inner layers. We show that the peeling process adapts to the local densities and characteristics to successfully separate adjacent clusters (of possibly different densities). We extensively tested our technique on large sets of labeled data, including high-dimensional datasets of deep features that were trained by a convolutional neural network. We show that our technique is competitive to other state-of-the-art non-parametric methods using a fixed set of parameters throughout the experiments.
Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences
Authors: I. Lopez-Gazpio, M. Maritxalar, A. Gonzalez-Agirre, G. Rigau, L. Uria, E. Agirre
Date: 2016-12-14 00:00:00
Tasks: Relation, Semantic Textual Similarity, Sentence, STS

User acceptance of artificial intelligence agents might depend on their
ability to explain their reasoning, which requires adding an interpretability
layer that fa- cilitates users to understand their behavior. This paper focuses
on adding an in- terpretable layer on top of Semantic Textual Similarity (STS),
which measures the degree of semantic equivalence between two sentences. The
interpretability layer is formalized as the alignment between pairs of segments
across the two sentences, where the relation between the segments is labeled
with a relation type and a similarity score. We present a publicly available
dataset of sentence pairs annotated following the formalization. We then
develop a system trained on this dataset which, given a sentence pair, explains
what is similar and different, in the form of graded and typed segment
alignments. When evaluated on the dataset, the system performs better than an
informed baseline, showing that the dataset and task are well-defined and
feasible. Most importantly, two user studies show how the system output can be
used to automatically produce explanations in natural language. Users performed
better when having access to the explanations, pro- viding preliminary evidence
that our dataset and method to automatically produce explanations is useful in
real applications.
Bayesian Optimization for Machine Learning : A Practical Guidebook
Authors: Ian Dewancker, Michael McCourt, Scott Clark
Date: 2016-12-14 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications.
Temporal-Needle: A view and appearance invariant video descriptor
Authors: Michal Yarom, Michal Irani
Date: 2016-12-14 00:00:00
Tasks: Action Detection, Clustering, Unsupervised Video Clustering

The ability to detect similar actions across videos can be very useful for
real-world applications in many fields. However, this task is still challenging
for existing systems, since videos that present the same action, can be taken
from significantly different viewing directions, performed by different actors
and backgrounds and under various video qualities. Video descriptors play a
significant role in these systems. In this work we propose the
"temporal-needle" descriptor which captures the dynamic behavior, while being
invariant to viewpoint and appearance. The descriptor is computed using multi
temporal scales of the video and by computing self-similarity for every patch
through time in every temporal scale. The descriptor is computed for every
pixel in the video. However, to find similar actions across videos, we consider
only a small subset of the descriptors - the statistical significant
descriptors. This allow us to find good correspondences across videos more
efficiently. Using the descriptor, we were able to detect the same behavior
across videos in a variety of scenarios. We demonstrate the use of the
descriptor in tasks such as temporal and spatial alignment, action detection
and even show its potential in unsupervised video clustering into categories.
In this work we handled only videos taken with stationary cameras, but the
descriptor can be extended to handle moving camera as well.
Constraint Selection in Metric Learning
Authors: Hoel Le Capitaine
Date: 2016-12-14 00:00:00
Tasks: Metric Learning

A number of machine learning algorithms are using a metric, or a distance, in
order to compare individuals. The Euclidean distance is usually employed, but
it may be more efficient to learn a parametric distance such as Mahalanobis
metric. Learning such a metric is a hot topic since more than ten years now,
and a number of methods have been proposed to efficiently learn it. However,
the nature of the problem makes it quite difficult for large scale data, as
well as data for which classes overlap. This paper presents a simple way of
improving accuracy and scalability of any iterative metric learning algorithm,
where constraints are obtained prior to the algorithm. The proposed approach
relies on a loss-dependent weighted selection of constraints that are used for
learning the metric. Using the corresponding dedicated loss function, the
method clearly allows to obtain better results than state-of-the-art methods,
both in terms of accuracy and time complexity. Some experimental results on
real world, and potentially large, datasets are demonstrating the effectiveness
of our proposition.
The BIN_COUNTS Constraint: Filtering and Applications
Authors: Roberto Rossi, Özgür Akgün, Steven Prestwich, Armagan Tarim
Date: 2016-11-28 00:00:00
Tasks: ARC

We introduce the BIN_COUNTS constraint, which deals with the problem of
counting the number of decision variables in a set which are assigned values
that lie in given bins. We illustrate a decomposition and a filtering algorithm
that achieves generalised arc consistency. We contrast the filtering power of
these two approaches and we discuss a number of applications. We show that
BIN_COUNTS can be employed to develop a decomposition for the $\chi^2$ test
constraint, a new statistical constraint that we introduce in this work. We
also show how this new constraint can be employed in the context of the
Balanced Academic Curriculum Problem and of the Balanced Nursing Workload
Problem. For both these problems we carry out numerical studies involving our
reformulations. Finally, we present a further application of the $\chi^2$ test
constraint in the context of confidence interval analysis.
Uncovering the Dynamics of Crowdlearning and the Value of Knowledge
Authors: Utkarsh Upadhyay, Isabel Valera, Manuel Gomez-Rodriguez
Date: 2016-12-14 00:00:00

Learning from the crowd has become increasingly popular in the Web and social
media. There is a wide variety of crowdlearning sites in which, on the one
hand, users learn from the knowledge that other users contribute to the site,
and, on the other hand, knowledge is reviewed and curated by the same users
using assessment measures such as upvotes or likes.
  In this paper, we present a probabilistic modeling framework of
crowdlearning, which uncovers the evolution of a user's expertise over time by
leveraging other users' assessments of her contributions. The model allows for
both off-site and on-site learning and captures forgetting of knowledge. We
then develop a scalable estimation method to fit the model parameters from
millions of recorded learning and contributing events. We show the
effectiveness of our model by tracing activity of ~25 thousand users in Stack
Overflow over a 4.5 year period. We find that answers with high knowledge value
are rare. Newbies and experts tend to acquire less knowledge than users in the
middle range. Prolific learners tend to be also proficient contributors that
post answers with high knowledge value.
Spectral video construction from RGB video: Application to Image Guided Neurosurgery
Authors: Md. Abul Hasnat, Jussi Parkkinen, Markku Hauta-Kasari
Date: 2016-12-14 00:00:00
Tasks: Video Generation

Spectral imaging has received enormous interest in the field of medical
imaging modalities. It provides a powerful tool for the analysis of different
organs and non-invasive tissues. Therefore, significant amount of research has
been conducted to explore the possibility of using spectral imaging in
biomedical applications. To observe spectral image information in real time
during surgery and monitor the temporal changes in the organs and tissues is a
demanding task. Available spectral imaging devices are not sufficient to
accomplish this task with an acceptable spatial and spectral resolution. A
solution to this problem is to estimate the spectral video from RGB video and
perform visualization with the most prominent spectral bands. In this research,
we propose a framework to generate neurosurgery spectral video from RGB video.
A spectral estimation technique is applied on each RGB video frames. The RGB
video is captured using a digital camera connected with an operational
microscope dedicated to neurosurgery. A database of neurosurgery spectral
images is used to collect training data and evaluate the estimation accuracy. A
searching technique is used to identify the best training set. Five different
spectrum estimation techniques are experimented to indentify the best method.
Although this framework is established for neurosurgery spectral video
generation, however, the methodology outlined here would also be applicable to
other similar research.
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method
Authors: Asaf Shabtai
Date: 2016-12-14 00:00:00
Tasks: Anomaly Detection

The rapid growth in stored time-oriented data necessitates the development of
new methods for handling, processing, and interpreting large amounts of
temporal data. One important example of such processing is detecting anomalies
in time-oriented data. The Knowledge-Based Temporal Abstraction method was
previously proposed for intelligent interpretation of temporal data based on
predefined domain knowledge. In this study we propose a framework that
integrates the KBTA method with a temporal pattern mining process for anomaly
detection. According to the proposed method a temporal pattern mining process
is applied on a dataset of basic temporal abstraction database in order to
extract patterns representing normal behavior. These patterns are then analyzed
in order to identify abnormal time periods characterized by a significantly
small number of normal patterns. The proposed approach was demonstrated using a
dataset collected from a real server.
Registering large volume serial-section electron microscopy image sets for neural circuit reconstruction using FFT signal whitening
Authors: Arthur W. Wetzel, Jennifer Bakal, Markus Dittrich, David G. C. Hildebrand, Josh L. Morgan, Jeff W. Lichtman
Date: 2016-12-14 00:00:00
Tasks: Anatomy, Image Registration

The detailed reconstruction of neural anatomy for connectomics studies
requires a combination of resolution and large three-dimensional data capture
provided by serial section electron microscopy (ssEM). The convergence of high
throughput ssEM imaging and improved tissue preparation methods now allows ssEM
capture of complete specimen volumes up to cubic millimeter scale. The
resulting multi-terabyte image sets span thousands of serial sections and must
be precisely registered into coherent volumetric forms in which neural circuits
can be traced and segmented. This paper introduces a Signal Whitening Fourier
Transform Image Registration approach (SWiFT-IR) under development at the
Pittsburgh Supercomputing Center and its use to align mouse and zebrafish brain
datasets acquired using the wafer mapper ssEM imaging technology recently
developed at Harvard University. Unlike other methods now used for ssEM
registration, SWiFT-IR modifies its spatial frequency response during image
matching to maximize a signal-to-noise measure used as its primary indicator of
alignment quality. This alignment signal is more robust to rapid variations in
biological content and unavoidable data distortions than either phase-only or
standard Pearson correlation, thus allowing more precise alignment and
statistical confidence. These improvements in turn enable an iterative
registration procedure based on projections through multiple sections rather
than more typical adjacent-pair matching methods. This projection approach,
when coupled with known anatomical constraints and iteratively applied in a
multi-resolution pyramid fashion, drives the alignment into a smooth form that
properly represents complex and widely varying anatomical content such as the
full cross-section zebrafish data.
Quantum Monte Carlo simulation of a particular class of non-stoquastic Hamiltonians in quantum annealing
Authors: Masayuki Ohzeki
Date: 2016-12-14 00:00:00

Quantum annealing is a generic solver of the optimization problem that uses
fictitious quantum fluctuation. Its simulation in classical computing is often
performed using the quantum Monte Carlo simulation via the Suzuki--Trotter
decomposition. However, the negative sign problem sometimes emerges in the
simulation of quantum annealing with an elaborate driver Hamiltonian, since it
belongs to a class of non-stoquastic Hamiltonians. In the present study, we
propose an alternative way to avoid the negative sign problem involved in a
particular class of the non-stoquastic Hamiltonians. To check the validity of
the method, we demonstrate our method by applying it to a simple problem that
includes the anti-ferromagnetic XX interaction, which is a typical instance of
the non-stoquastic Hamiltonians.
Semantic Amodal Segmentation
Authors: Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollár
Date: 2015-09-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: object-detection, Object Detection, Segmentation, Semantic Segmentation

Common visual recognition tasks such as classification, object detection, and
semantic segmentation are rapidly reaching maturity, and given the recent rate
of progress, it is not unreasonable to conjecture that techniques for many of
these problems will approach human levels of performance in the next few years.
In this paper we look to the future: what is the next frontier in visual
recognition?
  We offer one possible answer to this question. We propose a detailed image
annotation that captures information beyond the visible pixels and requires
complex reasoning about full scene structure. Specifically, we create an amodal
segmentation of each image: the full extent of each region is marked, not just
the visible pixels. Annotators outline and name all salient regions in the
image and specify a partial depth order. The result is a rich scene structure,
including visible and occluded portions of each region, figure-ground edge
information, semantic labels, and object overlap.
  We create two datasets for semantic amodal segmentation. First, we label 500
images in the BSDS dataset with multiple annotators per image, allowing us to
study the statistics of human annotations. We show that the proposed full scene
annotation is surprisingly consistent between annotators, including for regions
and edges. Second, we annotate 5000 images from COCO. This larger dataset
allows us to explore a number of algorithmic ideas for amodal segmentation and
depth ordering. We introduce novel metrics for these tasks, and along with our
strong baselines, define concrete new challenges for the community.
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Authors: Xu Xu, Sinisa Todorovic
Date: 2016-12-14 00:00:00
Tasks: 3D Shape Classification, 3D Shape Recognition

This paper addresses 3D shape recognition. Recent work typically represents a
3D shape as a set of binary variables corresponding to 3D voxels of a uniform
3D grid centered on the shape, and resorts to deep convolutional neural
networks(CNNs) for modeling these binary variables. Robust learning of such
CNNs is currently limited by the small datasets of 3D shapes available, an
order of magnitude smaller than other common datasets in computer vision.
Related work typically deals with the small training datasets using a number of
ad hoc, hand-tuning strategies. To address this issue, we formulate CNN
learning as a beam search aimed at identifying an optimal CNN architecture,
namely, the number of layers, nodes, and their connectivity in the network, as
well as estimating parameters of such an optimal CNN. Each state of the beam
search corresponds to a candidate CNN. Two types of actions are defined to add
new convolutional filters or new convolutional layers to a parent CNN, and thus
transition to children states. The utility function of each action is
efficiently computed by transferring parameter values of the parent CNN to its
children, thereby enabling an efficient beam search. Our experimental
evaluation on the 3D ModelNet dataset demonstrates that our model pursuit using
the beam search yields a CNN with superior performance on 3D shape
classification than the state of the art.