pwc-archive/papers-with-abstracts huggingface dataset:
Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources
Authors: Jason Naradowsky, Sebastian Riedel
Date: 2016-10-30 00:00:00
Tasks: Articles, Reading Comprehension

In order to extract event information from text, a machine reading model must
learn to accurately read and interpret the ways in which that information is
expressed. But it must also, as the human reader must, aggregate numerous
individual value hypotheses into a single coherent global analysis, applying
global constraints which reflect prior knowledge of the domain.
  In this work we focus on the task of extracting plane crash event information
from clusters of related news articles whose labels are derived via distant
supervision. Unlike previous machine reading work, we assume that while most
target values will occur frequently in most clusters, they may also be missing
or incorrect.
  We introduce a novel neural architecture to explicitly model the noisy nature
of the data and to deal with these aforementioned learning issues. Our models
are trained end-to-end and achieve an improvement of more than 12.1 F$_1$ over
previous work, despite using far less linguistic annotation. We apply factor
graph constraints to promote more coherent event analyses, with belief
propagation inference formulated within the transitions of a recurrent neural
network. We show this technique additionally improves maximum F$_1$ by up to
2.8 points, resulting in a relative improvement of $50\%$ over the previous
state-of-the-art.
Doubly Convolutional Neural Networks
Authors: Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
Date: 2016-10-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: image-classification, Image Classification

Building large models with parameter sharing accounts for most of the success
of deep convolutional neural networks (CNNs). In this paper, we propose doubly
convolutional neural networks (DCNNs), which significantly improve the
performance of CNNs by further exploring this idea. In stead of allocating a
set of convolutional filters that are independently learned, a DCNN maintains
groups of filters where filters within each group are translated versions of
each other. Practically, a DCNN can be easily implemented by a two-step
convolution procedure, which is supported by most modern deep learning
libraries. We perform extensive experiments on three image classification
benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently
outperform other competing architectures. We have also verified that replacing
a convolutional layer with a doubly convolutional layer at any depth of a CNN
can improve its performance. Moreover, various design choices of DCNNs are
demonstrated, which shows that DCNN can serve the dual purpose of building more
accurate models and/or reducing the memory footprint without sacrificing the
accuracy.
Real-Time Image Distortion Correction: Analysis and Evaluation of FPGA-Compatible Algorithms
Authors: Paolo Di Febbo, Stefano Mattoccia, Carlo Dal Mutto
Date: 2016-10-30 00:00:00
Tasks: distortion correction

Image distortion correction is a critical pre-processing step for a variety
of computer vision and image processing algorithms. Standard real-time software
implementations are generally not suited for direct hardware porting, so
appropriated versions need to be designed in order to obtain implementations
deployable on FPGAs. In this paper, hardware-compatible techniques for image
distortion correction are introduced and analyzed in details. The considered
solutions are compared in terms of output quality by using a
geometrical-error-based approach, with particular emphasis on robustness with
respect to increasing lens distortion. The required amount of hardware
resources is also estimated for each considered approach.
Feature-Augmented Neural Networks for Patient Note De-identification
Authors: Ji Young Lee, Franck Dernoncourt, Ozlem Uzuner, Peter Szolovits
Date: 2016-10-30 00:00:00
Proceeding: WS 2016 12
Tasks: De-identification

Patient notes contain a wealth of information of potentially great interest
to medical investigators. However, to protect patients' privacy, Protected
Health Information (PHI) must be removed from the patient notes before they can
be legally released, a process known as patient note de-identification. The
main objective for a de-identification system is to have the highest possible
recall. Recently, the first neural-network-based de-identification system has
been proposed, yielding state-of-the-art results. Unlike other systems, it does
not rely on human-engineered features, which allows it to be quickly deployed,
but does not leverage knowledge from human experts or from electronic health
records (EHRs). In this work, we explore a method to incorporate
human-engineered features as well as features derived from EHRs to a
neural-network-based de-identification system. Our results show that the
addition of features, especially the EHR-derived features, further improves the
state-of-the-art in patient note de-identification, including for some of the
most sensitive PHI types such as patient names. Since in a real-life setting
patient notes typically come with EHRs, we recommend developers of
de-identification systems to leverage the information EHRs contain.
A specialized face-processing network consistent with the representational geometry of monkey face patches
Authors: Amirhossein Farzmahdi, Karim Rajaei, Masoud Ghodrati, Reza Ebrahimpour, Seyed-Mahdi Khaligh-Razavi
Date: 2015-02-04 00:00:00

Ample evidence suggests that face processing in human and non-human primates
is performed differently compared with other objects. Converging reports, both
physiologically and psychophysically, indicate that faces are processed in
specialized neural networks in the brain -i.e. face patches in monkeys and the
fusiform face area (FFA) in humans. We are all expert face-processing agents,
and able to identify very subtle differences within the category of faces,
despite substantial visual and featural similarities. Identification is
performed rapidly and accurately after viewing a whole face, while
significantly drops if some of the face configurations (e.g. inversion,
misalignment) are manipulated or if partial views of faces are shown due to
occlusion. This refers to a hotly-debated, yet highly-supported concept, known
as holistic face processing. We built a hierarchical computational model of
face-processing based on evidence from recent neuronal and behavioural studies
on faces processing in primates. Representational geometries of the last three
layers of the model have characteristics similar to those observed in monkey
face patches (posterior, middle and anterior patches). Furthermore, several
face-processing-related phenomena reported in the literature automatically
emerge as properties of this model. The representations are evolved through
several computational layers, using biologically plausible learning rules. The
model satisfies face inversion effect, composite face effect, other race
effect, view and identity selectivity, and canonical face views. To our
knowledge, no models have so far been proposed with this performance and
agreement with biological data.
Strong Neutrosophic Graphs and Subgraph Topological Subspaces
Authors: W. B. Vasantha Kandasamy, Ilanthenral K, Florentin Smarandache
Date: 2016-10-30 00:00:00

In this book authors for the first time introduce the notion of strong
neutrosophic graphs. They are very different from the usual graphs and
neutrosophic graphs. Using these new structures special subgraph topological
spaces are defined. Further special lattice graph of subgraphs of these graphs
are defined and described. Several interesting properties using subgraphs of a
strong neutrosophic graph are obtained. Several open conjectures are proposed.
These new class of strong neutrosophic graphs will certainly find applications
in Neutrosophic Cognitive Maps (NCM), Neutrosophic Relational Maps (NRM) and
Neutrosophic Relational Equations (NRE) with appropriate modifications.
Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering
Authors: Gautier Marti, Sebastien Andler, Frank Nielsen, Philippe Donnat
Date: 2016-10-30 00:00:00
Tasks: Clustering

We propose a methodology to explore and measure the pairwise correlations
that exist between variables in a dataset. The methodology leverages copulas
for encoding dependence between two variables, state-of-the-art optimal
transport for providing a relevant geometry to the copulas, and clustering for
summarizing the main dependence patterns found between the variables. Some of
the clusters centers can be used to parameterize a novel dependence coefficient
which can target or forget specific dependence patterns. Finally, we illustrate
and benchmark the methodology on several datasets. Code and numerical
experiments are available online for reproducible research.
Visual Tracking via Boolean Map Representations
Authors: Kaihua Zhang, Qingshan Liu, Ming-Hsuan Yang
Date: 2016-10-30 00:00:00
Tasks: Visual Tracking

In this paper, we present a simple yet effective Boolean map based
representation that exploits connectivity cues for visual tracking. We describe
a target object with histogram of oriented gradients and raw color features, of
which each one is characterized by a set of Boolean maps generated by uniformly
thresholding their values. The Boolean maps effectively encode multi-scale
connectivity cues of the target with different granularities. The fine-grained
Boolean maps capture spatially structural details that are effective for
precise target localization while the coarse-grained ones encode global shape
information that are robust to large target appearance variations. Finally, all
the Boolean maps form together a robust representation that can be approximated
by an explicit feature map of the intersection kernel, which is fed into a
logistic regression classifier with online update, and the target location is
estimated within a particle filter framework. The proposed representation
scheme is computationally efficient and facilitates achieving favorable
performance in terms of accuracy and robustness against the state-of-the-art
tracking methods on a large benchmark dataset of 50 image sequences.
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search
Authors: Shicong Liu, Hongtao Lu
Date: 2016-10-30 00:00:00
Tasks: Quantization

Recent advance of large scale similarity search involves using deeply learned
representations to improve the search accuracy and use vector quantization
methods to increase the search speed. However, how to learn deep
representations that strongly preserve similarities between data pairs and can
be accurately quantized via vector quantization remains a challenging task.
Existing methods simply leverage quantization loss and similarity loss, which
result in unexpectedly biased back-propagating gradients and affect the search
performances. To this end, we propose a novel gradient snapping layer (GSL) to
directly regularize the back-propagating gradient towards a neighboring
codeword, the generated gradients are un-biased for reducing similarity loss
and also propel the learned representations to be accurately quantized. Joint
deep representation and vector quantization learning can be easily performed by
alternatively optimize the quantization codebook and the deep neural network.
The proposed framework is compatible with various existing vector quantization
approaches. Experimental results demonstrate that the proposed framework is
effective, flexible and outperforms the state-of-the-art large scale similarity
search methods.
Adaptive Ensemble Learning with Confidence Bounds
Authors: Cem Tekin, Jinsung Yoon, Mihaela van der Schaar
Date: 2015-12-23 00:00:00
Tasks: Ensemble Learning, Meta-Learning

Extracting actionable intelligence from distributed, heterogeneous,
correlated and high-dimensional data sources requires run-time processing and
learning both locally and globally. In the last decade, a large number of
meta-learning techniques have been proposed in which local learners make online
predictions based on their locally-collected data instances, and feed these
predictions to an ensemble learner, which fuses them and issues a global
prediction. However, most of these works do not provide performance guarantees
or, when they do, these guarantees are asymptotic. None of these existing works
provide confidence estimates about the issued predictions or rate of learning
guarantees for the ensemble learner. In this paper, we provide a systematic
ensemble learning method called Hedged Bandits, which comes with both long run
(asymptotic) and short run (rate of learning) performance guarantees. Moreover,
our approach yields performance guarantees with respect to the optimal local
prediction strategy, and is also able to adapt its predictions in a data-driven
manner. We illustrate the performance of Hedged Bandits in the context of
medical informatics and show that it outperforms numerous online and offline
ensemble learning methods.
Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy
Authors: Menglong Ye, Edward Johns, Benjamin Walter, Alexander Meining, Guang-Zhong Yang
Date: 2016-05-18 00:00:00

For early diagnosis of malignancies in the gastrointestinal tract,
surveillance endoscopy is increasingly used to monitor abnormal tissue changes
in serial examinations of the same patient. Despite successes with optical
biopsy for in vivo and in situ tissue characterisation, biopsy retargeting for
serial examinations is challenging because tissue may change in appearance
between examinations. In this paper, we propose an inter-examination
retargeting framework for optical biopsy, based on an image descriptor designed
for matching between endoscopic scenes over significant time intervals. Each
scene is described by a hierarchy of regional intensity comparisons at various
scales, offering tolerance to long-term change in tissue appearance whilst
remaining discriminative. Binary coding is then used to compress the descriptor
via a novel random forests approach, providing fast comparisons in Hamming
space and real-time retargeting. Extensive validation conducted on 13 in vivo
gastrointestinal videos, collected from six patients, show that our approach
outperforms state-of-the-art methods.
Real-time 3D Tracking of Articulated Tools for Robotic Surgery
Authors: Menglong Ye, Lin Zhang, Stamatia Giannarou, Guang-Zhong Yang
Date: 2016-05-11 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation, Skills Assessment

In robotic surgery, tool tracking is important for providing safe tool-tissue
interaction and facilitating surgical skills assessment. Despite recent
advances in tool tracking, existing approaches are faced with major
difficulties in real-time tracking of articulated tools. Most algorithms are
tailored for offline processing with pre-recorded videos. In this paper, we
propose a real-time 3D tracking method for articulated tools in robotic
surgery. The proposed method is based on the CAD model of the tools as well as
robot kinematics to generate online part-based templates for efficient 2D
matching and 3D pose estimation. A robust verification approach is incorporated
to reject outliers in 2D detections, which is then followed by fusing inliers
with robot kinematic readings for 3D pose estimation of the tool. The proposed
method has been validated with phantom data, as well as ex vivo and in vivo
experiments. The results derived clearly demonstrate the performance advantage
of the proposed method when compared to the state-of-the-art.
Compact Deep Convolutional Neural Networks With Coarse Pruning
Authors: Sajid Anwar, Wonyong Sung
Date: 2016-10-30 00:00:00
Tasks: Network Pruning

The learning capability of a neural network improves with increasing depth at
higher computational costs. Wider layers with dense kernel connectivity
patterns furhter increase this cost and may hinder real-time inference. We
propose feature map and kernel level pruning for reducing the computational
complexity of a deep convolutional neural network. Pruning feature maps reduces
the width of a layer and hence does not need any sparse representation.
Further, kernel pruning converts the dense connectivity pattern into a sparse
one. Due to coarse nature, these pruning granularities can be exploited by GPUs
and VLSI based implementations. We propose a simple and generic strategy to
choose the least adversarial pruning masks for both granularities. The pruned
networks are retrained which compensates the loss in accuracy. We obtain the
best pruning ratios when we prune a network with both granularities.
Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be
induced in the convolution layers with less than 1% increase in the
missclassification rate of the baseline network.
Discovering containment: from infants to machines
Authors: Shimon Ullman, Nimrod Dorfman, Daniel Harari
Date: 2016-10-30 00:00:00

Current artificial learning systems can recognize thousands of visual
categories, or play Go at a champion"s level, but cannot explain infants
learning, in particular the ability to learn complex concepts without guidance,
in a specific order. A notable example is the category of 'containers' and the
notion of containment, one of the earliest spatial relations to be learned,
starting already at 2.5 months, and preceding other common relations (e.g.,
support). Such spontaneous unsupervised learning stands in contrast with
current highly successful computational models, which learn in a supervised
manner, that is, by using large data sets of labeled examples. How can
meaningful concepts be learned without guidance, and what determines the
trajectory of infant learning, making some notions appear consistently earlier
than others?
Compressed Learning: A Deep Neural Network Approach
Authors: Amir Adler, Michael Elad, Michael Zibulevsky
Date: 2016-10-30 00:00:00
Tasks: General Classification, image-classification, Image Classification

Compressed Learning (CL) is a joint signal processing and machine learning
framework for inference from a signal, using a small number of measurements
obtained by linear projections of the signal. In this paper we present an
end-to-end deep learning approach for CL, in which a network composed of
fully-connected layers followed by convolutional layers perform the linear
sensing and non-linear inference stages. During the training phase, the sensing
matrix and the non-linear inference operator are jointly optimized, and the
proposed approach outperforms state-of-the-art for the task of image
classification. For example, at a sensing rate of 1% (only 8 measurements of 28
X 28 pixels images), the classification error for the MNIST handwritten digits
dataset is 6.46% compared to 41.06% with state-of-the-art.
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
Authors: Keyu Lu, Jian Li, Xiangjing An, Hangen He
Date: 2016-10-30 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection, Real-Time Object Detection, regression

Vision-based object detection is one of the fundamental functions in numerous
traffic scene applications such as self-driving vehicle systems and advance
driver assistance systems (ADAS). However, it is also a challenging task due to
the diversity of traffic scene and the storage, power and computing source
limitations of the platforms for traffic scene applications. This paper
presents a generalized Haar filter based deep network which is suitable for the
object detection tasks in traffic scene. In this approach, we first decompose a
object detection task into several easier local regression tasks. Then, we
handle the local regression tasks by using several tiny deep networks which
simultaneously output the bounding boxes, categories and confidence scores of
detected objects. To reduce the consumption of storage and computing resources,
the weights of the deep networks are constrained to the form of generalized
Haar filter in training phase. Additionally, we introduce the strategy of
sparse windows generation to improve the efficiency of the algorithm. Finally,
we perform several experiments to validate the performance of our proposed
approach. Experimental results demonstrate that the proposed approach is both
efficient and effective in traffic scene compared with the state-of-the-art.
A Theoretical Study of The Relationship Between Whole An ELM Network and Its Subnetworks
Authors: Enmei Tu, Guanghao Zhang, Lily Rachmawati, Eshan Rajabally, Guang-Bin Huang
Date: 2016-10-30 00:00:00

A biological neural network is constituted by numerous subnetworks and
modules with different functionalities. For an artificial neural network, the
relationship between a network and its subnetworks is also important and useful
for both theoretical and algorithmic research, i.e. it can be exploited to
develop incremental network training algorithm or parallel network training
algorithm. In this paper we explore the relationship between an ELM neural
network and its subnetworks. To the best of our knowledge, we are the first to
prove a theorem that shows an ELM neural network can be scattered into
subnetworks and its optimal solution can be constructed recursively by the
optimal solutions of these subnetworks. Based on the theorem we also present
two algorithms to train a large ELM neural network efficiently: one is a
parallel network training algorithm and the other is an incremental network
training algorithm. The experimental results demonstrate the usefulness of the
theorem and the validity of the developed algorithms.
Covariate-assisted spectral clustering
Authors: Norbert Binkiewicz, Joshua T. Vogelstein, Karl Rohe
Date: 2014-11-08 00:00:00
Tasks: Clustering, Diffusion  MRI

Biological and social systems consist of myriad interacting units. The
interactions can be represented in the form of a graph or network. Measurements
of these graphs can reveal the underlying structure of these interactions,
which provides insight into the systems that generated the graphs. Moreover, in
applications such as connectomics, social networks, and genomics, graph data
are accompanied by contextualizing measures on each node. We utilize these node
covariates to help uncover latent communities in a graph, using a modification
of spectral clustering. Statistical guarantees are provided under a joint
mixture model that we call the node-contextualized stochastic blockmodel,
including a bound on the mis-clustering rate. The bound is used to derive
conditions for achieving perfect clustering. For most simulated cases,
covariate-assisted spectral clustering yields results superior to regularized
spectral clustering without node covariates and to an adaptation of canonical
correlation analysis. We apply our clustering method to large brain graphs
derived from diffusion MRI data, using the node locations or neurological
region membership as covariates. In both cases, covariate-assisted spectral
clustering yields clusters that are easier to interpret neurologically.
Distance Metric Learning for Aspect Phrase Grouping
Authors: Shufeng Xiong, Yue Zhang, Donghong Ji, Yinxia Lou
Date: 2016-04-29 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Metric Learning, Sentiment Analysis, Word Embeddings

Aspect phrase grouping is an important task in aspect-level sentiment
analysis. It is a challenging problem due to polysemy and context dependency.
We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by
considering aspect phrase representation as well as context representation.
First, leveraging the characteristics of the review text, we automatically
generate aspect phrase sample pairs for distant supervision. Second, we feed
word embeddings of aspect phrases and their contexts into an attention-based
neural network to learn feature representation of contexts. Both aspect phrase
embedding and context embedding are used to learn a deep feature subspace for
measure the distances between aspect phrases for K-means clustering.
Experiments on four review datasets show that the proposed method outperforms
state-of-the-art strong baseline methods.