pwc-archive/papers-with-abstracts huggingface dataset:
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Authors: Jooyeon Kim, Dongwoo Kim, Alice Oh
Date: 2017-06-02 00:00:00
Proceeding: TACL 2017 1

Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.
Learning-based Surgical Workflow Detection from Intra-Operative Signals
Authors: Ralf Stauder, Ergün Kayis, Nassir Navab
Date: 2017-06-02 00:00:00

A modern operating room (OR) provides a plethora of advanced medical devices.
In order to better facilitate the information offered by them, they need to
automatically react to the intra-operative context. To this end, the progress
of the surgical workflow must be detected and interpreted, so that the current
status can be given in machine-readable form. In this work, Random Forests (RF)
and Hidden Markov Models (HMM) are compared and combined to detect the surgical
workflow phase of a laparoscopic cholecystectomy. Various combinations of data
were tested, from using only raw sensor data to filtered and augmented
datasets. Achieved accuracies ranged from 64% to 72% for the RF approach, and
from 80% to 82% for the combination of RF and HMM.
Exception-Based Knowledge Updates
Authors: Martin Slota, Joao Leite
Date: 2017-06-02 00:00:00

Existing methods for dealing with knowledge updates differ greatly depending
on the underlying knowledge representation formalism. When Classical Logic is
used, updates are typically performed by manipulating the knowledge base on the
model-theoretic level. On the opposite side of the spectrum stand the semantics
for updating Answer-Set Programs that need to rely on rule syntax. Yet, a
unifying perspective that could embrace both these branches of research is of
great importance as it enables a deeper understanding of all involved methods
and principles and creates room for their cross-fertilisation, ripening and
further development.
  This paper bridges the seemingly irreconcilable approaches to updates. It
introduces a novel monotonic characterisation of rules, dubbed RE-models, and
shows it to be a more suitable semantic foundation for rule updates than
SE-models. Then it proposes a generic scheme for specifying semantic rule
update operators, based on the idea of viewing a program as the set of sets of
RE-models of its rules; updates are performed by introducing additional
interpretations - exceptions - to the sets of RE-models of rules in the
original program. The introduced scheme is used to define rule update operators
that are closely related to both classical update principles and traditional
approaches to rules updates, and serve as a basis for a solution to the
long-standing problem of state condensing, showing how they can be equivalently
defined as binary operators on some class of logic programs.
  Finally, the essence of these ideas is extracted to define an abstract
framework for exception-based update operators, viewing a knowledge base as the
set of sets of models of its elements, which can capture a wide range of both
model- and formula-based classical update operators, and thus serves as the
first firm formal ground connecting classical and rule updates.
Rank-One NMF-Based Initialization for NMF and Relative Error Bounds under a Geometric Assumption
Authors: Zhaoqiang Liu, Vincent Y. F. Tan
Date: 2016-12-27 00:00:00
Tasks: Clustering

We propose a geometric assumption on nonnegative data matrices such that
under this assumption, we are able to provide upper bounds (both deterministic
and probabilistic) on the relative error of nonnegative matrix factorization
(NMF). The algorithm we propose first uses the geometric assumption to obtain
an exact clustering of the columns of the data matrix; subsequently, it employs
several rank-one NMFs to obtain the final decomposition. When applied to data
matrices generated from our statistical model, we observe that our proposed
algorithm produces factor matrices with comparable relative errors vis-\`a-vis
classical NMF algorithms but with much faster speeds. On face image and
hyperspectral imaging datasets, we demonstrate that our algorithm provides an
excellent initialization for applying other NMF algorithms at a low
computational cost. Finally, we show on face and text datasets that the
combinations of our algorithm and several classical NMF algorithms outperform
other algorithms in terms of clustering performance.
Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization
Authors: Thomas Moreau, Joan Bruna
Date: 2017-06-02 00:00:00

Sparse coding is a core building block in many data analysis and machine
learning pipelines. Typically it is solved by relying on generic optimization
techniques, such as the Iterative Soft Thresholding Algorithm and its
accelerated version (ISTA, FISTA). These methods are optimal in the class of
first-order methods for non-smooth, convex functions. However, they do not
exploit the particular structure of the problem at hand nor the input data
distribution. An acceleration using neural networks, coined LISTA, was proposed
in Gregor and Le Cun (2010), which showed empirically that one could achieve
high quality estimates with few iterations by modifying the parameters of the
proximal splitting appropriately.
  In this paper we study the reasons for such acceleration. Our mathematical
analysis reveals that it is related to a specific matrix factorization of the
Gram kernel of the dictionary, which attempts to nearly diagonalise the kernel
with a basis that produces a small perturbation of the $\ell_1$ ball. When this
factorization succeeds, we prove that the resulting splitting algorithm enjoys
an improved convergence bound with respect to the non-adaptive version.
Moreover, our analysis also shows that conditions for acceleration occur mostly
at the beginning of the iterative process, consistent with numerical
experiments. We further validate our analysis by showing that on dictionaries
where this factorization does not exist, adaptive acceleration fails.
Abstract Argumentation / Persuasion / Dynamics
Authors: Ryuta Arisaka, Ken Satoh
Date: 2017-05-29 00:00:00
Tasks: Abstract Argumentation

The act of persuasion, a key component in rhetoric argumentation, may be
viewed as a dynamics modifier. We extend Dung's frameworks with acts of
persuasion among agents, and consider interactions among attack, persuasion and
defence that have been largely unheeded so far. We characterise basic notions
of admissibilities in this framework, and show a way of enriching them through,
effectively, CTL (computation tree logic) encoding, which also permits
importation of the theoretical results known to the logic into our
argumentation frameworks. Our aim is to complement the growing interest in
coordination of static and dynamic argumentation.
Object-Level Context Modeling For Scene Classification with Context-CNN
Authors: Syed Ashar Javed, Anil Kumar Nelakanti
Date: 2017-05-11 00:00:00
Tasks: Classification, General Classification, Object, Scene Classification

Convolutional Neural Networks (CNNs) have been used extensively for computer
vision tasks and produce rich feature representation for objects or parts of an
image. But reasoning about scenes requires integration between the low-level
feature representations and the high-level semantic information. We propose a
deep network architecture which models the semantic context of scenes by
capturing object-level information. We use Long Short Term Memory(LSTM) units
in conjunction with object proposals to incorporate object-object relationship
and object-scene relationship in an end-to-end trainable manner. We evaluate
our model on the LSUN dataset and achieve results comparable to the
state-of-art. We further show visualization of the learned features and analyze
the model with experiments to verify our model's ability to model context.
SAR Image Despeckling Using a Convolutional Neural Network
Authors: Puyang Wang, He Zhang, Vishal M. Patel
Date: 2017-06-02 00:00:00
Tasks: Sar Image Despeckling

Synthetic Aperture Radar (SAR) images are often contaminated by a
multiplicative noise known as speckle. Speckle makes the processing and
interpretation of SAR images difficult. We propose a deep learning-based
approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for
automatically removing speckle from the input noisy images. In particular,
ID-CNN uses a set of convolutional layers along with batch normalization and
rectified linear unit (ReLU) activation function and a component-wise division
residual layer to estimate speckle and it is trained in an end-to-end fashion
using a combination of Euclidean loss and Total Variation (TV) loss. Extensive
experiments on synthetic and real SAR images show that the proposed method
achieves significant improvements over the state-of-the-art speckle reduction
methods.
Bias-Variance Tradeoff of Graph Laplacian Regularizer
Authors: Pin-Yu Chen, Sijia Liu
Date: 2017-06-02 00:00:00

This paper presents a bias-variance tradeoff of graph Laplacian regularizer,
which is widely used in graph signal processing and semi-supervised learning
tasks. The scaling law of the optimal regularization parameter is specified in
terms of the spectral graph properties and a novel signal-to-noise ratio
parameter, which suggests selecting a mediocre regularization parameter is
often suboptimal. The analysis is applied to three applications, including
random, band-limited, and multiple-sampled graph signals. Experiments on
synthetic and real-world graphs demonstrate near-optimal performance of the
established analysis.
An Efficient Algorithm for Bayesian Nearest Neighbours
Authors: Giuseppe Nuti
Date: 2017-05-26 00:00:00
Tasks: Change Point Detection, General Classification, regression

K-Nearest Neighbours (k-NN) is a popular classification and regression
algorithm, yet one of its main limitations is the difficulty in choosing the
number of neighbours. We present a Bayesian algorithm to compute the posterior
probability distribution for k given a target point within a data-set,
efficiently and without the use of Markov Chain Monte Carlo (MCMC) methods or
simulation - alongside an exact solution for distributions within the
exponential family. The central idea is that data points around our target are
generated by the same probability distribution, extending outwards over the
appropriate, though unknown, number of neighbours. Once the data is projected
onto a distance metric of choice, we can transform the choice of k into a
change-point detection problem, for which there is an efficient solution: we
recursively compute the probability of the last change-point as we move towards
our target, and thus de facto compute the posterior probability distribution
over k. Applying this approach to both a classification and a regression UCI
data-sets, we compare favourably and, most importantly, by removing the need
for simulation, we are able to compute the posterior probability of k exactly
and rapidly. As an example, the computational time for the Ripley data-set is a
few milliseconds compared to a few hours when using a MCMC approach.
PixelGAN Autoencoders
Authors: Alireza Makhzani, Brendan Frey
Date: 2017-06-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Decoder, Generative Adversarial Network, Unsupervised Image Classification, Unsupervised MNIST

In this paper, we describe the "PixelGAN autoencoder", a generative
autoencoder in which the generative path is a convolutional autoregressive
neural network on pixels (PixelCNN) that is conditioned on a latent code, and
the recognition path uses a generative adversarial network (GAN) to impose a
prior distribution on the latent code. We show that different priors result in
different decompositions of information between the latent code and the
autoregressive decoder. For example, by imposing a Gaussian distribution as the
prior, we can achieve a global vs. local decomposition, or by imposing a
categorical distribution as the prior, we can disentangle the style and content
information of images in an unsupervised fashion. We further show how the
PixelGAN autoencoder with a categorical prior can be directly used in
semi-supervised settings and achieve competitive semi-supervised classification
results on the MNIST, SVHN and NORB datasets.
Integrated Deep and Shallow Networks for Salient Object Detection
Authors: Jing Zhang, Bo Li, Yuchao Dai, Fatih Porikli, Mingyi He
Date: 2017-06-02 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation

Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifically, our method takes results of unsupervised saliency
(Robust Background Detection, RBD) and normalized color images as inputs, and
directly learns an end-to-end mapping between inputs and the corresponding
saliency maps. The color images are fed into a Fully Convolutional Neural
Networks (FCNN) adapted from semantic segmentation to exploit high-level
semantic cues for salient object detection. Then the results from deep FCNN and
RBD are concatenated to feed into a shallow network to map the concatenated
feature maps to saliency maps. Finally, to obtain a spatially consistent
saliency map with sharp object boundaries, we fuse superpixel level saliency
map at multi-scale. Extensive experimental results on 8 benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches with a margin.
Active classification with comparison queries
Authors: Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang
Date: 2017-04-11 00:00:00
Tasks: Active Learning, Classification, General Classification

We study an extension of active learning in which the learning algorithm may
ask the annotator to compare the distances of two examples from the boundary of
their label-class. For example, in a recommendation system application (say for
restaurants), the annotator may be asked whether she liked or disliked a
specific restaurant (a label query); or which one of two restaurants did she
like more (a comparison query).
  We focus on the class of half spaces, and show that under natural
assumptions, such as large margin or bounded bit-description of the input
examples, it is possible to reveal all the labels of a sample of size $n$ using
approximately $O(\log n)$ queries. This implies an exponential improvement over
classical active learning, where only label queries are allowed. We complement
these results by showing that if any of these assumptions is removed then, in
the worst case, $\Omega(n)$ queries are required.
  Our results follow from a new general framework of active learning with
additional queries. We identify a combinatorial dimension, called the
\emph{inference dimension}, that captures the query complexity when each
additional query is determined by $O(1)$ examples (such as comparison queries,
each of which is determined by the two compared examples). Our results for half
spaces follow by bounding the inference dimension in the cases discussed above.
A Vision System for Multi-View Face Recognition
Authors: M. Y. Shams, A. S. Tolba, S. H. Sarhan
Date: 2017-06-01 00:00:00
Tasks: Face Recognition, Quantization

Multimodal biometric identification has been grown a great attention in the
most interests in the security fields. In the real world there exist modern
system devices that are able to detect, recognize, and classify the human
identities with reliable and fast recognition rates. Unfortunately most of
these systems rely on one modality, and the reliability for two or more
modalities are further decreased. The variations of face images with respect to
different poses are considered as one of the important challenges in face
recognition systems. In this paper, we propose a multimodal biometric system
that able to detect the human face images that are not only one view face
image, but also multi-view face images. Each subject entered to the system
adjusted their face at front of the three cameras, and then the features of the
face images are extracted based on Speeded Up Robust Features (SURF) algorithm.
We utilize Multi-Layer Perceptron (MLP) and combined classifiers based on both
Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) for
classification purposes. The proposed system has been tested using SDUMLA-HMT,
and CASIA datasets. Furthermore, we collected a database of multi-view face
images by which we take the additive white Gaussian noise into considerations.
The results indicated the reliability, robustness of the proposed system with
different poses and variations including noise images.
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages
Authors: Onur Gungor, Eray Yildiz, Suzan Uskudarli, Tunga Gungor
Date: 2017-06-01 00:00:00
Tasks: Morphological Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In this work, we present new state-of-the-art results of 93.59,% and 79.59,%
for Turkish and Czech named entity recognition based on the model of (Lample et
al., 2016). We contribute by proposing several schemes for representing the
morphological analysis of a word in the context of named entity recognition. We
show that a concatenation of this representation with the word and character
embeddings improves the performance. The effect of these representation schemes
on the tagging performance is also investigated.
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Authors: Melvin Wong, Bilal Farooq, Guillaume-Alexandre Bilodeau
Date: 2017-06-01 00:00:00

Conventional methods of estimating latent behaviour generally use attitudinal
questions which are subjective and these survey questions may not always be
available. We hypothesize that an alternative approach can be used for latent
variable estimation through an undirected graphical models. For instance,
non-parametric artificial neural networks. In this study, we explore the use of
generative non-parametric modelling methods to estimate latent variables from
prior choice distribution without the conventional use of measurement
indicators. A restricted Boltzmann machine is used to represent latent
behaviour factors by analyzing the relationship information between the
observed choices and explanatory variables. The algorithm is adapted for latent
behaviour analysis in discrete choice scenario and we use a graphical approach
to evaluate and understand the semantic meaning from estimated parameter vector
values. We illustrate our methodology on a financial instrument choice dataset
and perform statistical analysis on parameter sensitivity and stability. Our
findings show that through non-parametric statistical tests, we can extract
useful latent information on the behaviour of latent constructs through machine
learning methods and present strong and significant influence on the choice
process. Furthermore, our modelling framework shows robustness in input
variability through sampling and validation.
Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks
Authors: Alberto Delmas, Patrick Judd, Sayeh Sharify, Andreas Moshovos
Date: 2017-06-01 00:00:00

Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial
computation to offer performance that is proportional to the fixed-point
precision of the activation values. The fixed-point precisions are determined a
priori using profiling and are selected at a per layer granularity. This paper
presents Dynamic Stripes, an extension to Stripes that detects precision
variance at runtime and at a finer granularity. This extra level of precision
reduction increases performance by 41% over Stripes.
Deep Relaxation: partial differential equations for optimizing deep neural networks
Authors: Pratik Chaudhari, Adam Oberman, Stanley Osher, Stefano Soatto, Guillaume Carlier
Date: 2017-04-17 00:00:00

In this paper we establish a connection between non-convex optimization
methods for training deep neural networks and nonlinear partial differential
equations (PDEs). Relaxation techniques arising in statistical physics which
have already been used successfully in this context are reinterpreted as
solutions of a viscous Hamilton-Jacobi PDE. Using a stochastic control
interpretation allows we prove that the modified algorithm performs better in
expectation that stochastic gradient descent. Well-known PDE regularity results
allow us to analyze the geometry of the relaxed energy landscape, confirming
empirical evidence. The PDE is derived from a stochastic homogenization
problem, which arises in the implementation of the algorithm. The algorithms
scale well in practice and can effectively tackle the high dimensionality of
modern neural networks.
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Authors: Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Date: 2017-06-01 00:00:00
Tasks: feature selection, Prediction

Tumor growth prediction, a highly challenging task, has long been viewed as a
mathematical modeling problem, where the tumor growth pattern is personalized
based on imaging and clinical data of a target patient. Though mathematical
models yield promising results, their prediction accuracy may be limited by the
absence of population trend data and personalized clinical characteristics. In
this paper, we propose a statistical group learning approach to predict the
tumor growth pattern that incorporates both the population trend and
personalized data, in order to discover high-level features from multimodal
imaging data. A deep convolutional neural network approach is developed to
model the voxel-wise spatio-temporal tumor progression. The deep features are
combined with the time intervals and the clinical factors to feed a process of
feature selection. Our predictive model is pretrained on a group data set and
personalized on the target patient data to estimate the future spatio-temporal
progression of the patient's tumor. Multimodal imaging data at multiple time
points are used in the learning, personalization and inference stages. Our
method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on
a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD
13.9% +- 9.8% obtained by a previous state-of-the-art model-based method.