pwc-archive/papers-with-abstracts huggingface dataset:
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Authors: Paul Jaeger, Sebastian Bickelhaupt, Frederik Bernd Laun, Wolfgang Lederer, Daniel Heidi, Tristan Anselm Kuder, Daniel Paech, David Bonekamp, Alexander Radbruch, Stefan Delorme, Heinz-Peter Schlemmer, Franziska Steudle, Klaus H. Maier-Hein
Date: 2017-02-27 00:00:00
Tasks: Decision Making, Representation Learning

Mammography screening for early detection of breast lesions currently suffers
from high amounts of false positive findings, which result in unnecessary
invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
of these false-positive findings prior to biopsy. Current approaches estimate
tissue properties by means of quantitative parameters taken from generative,
biophysical models fit to the q-space encoded signal under certain assumptions
regarding noise and spatial homogeneity. This process is prone to fitting
instability and partial information loss due to model simplicity. We reveal
unexplored potentials of the signal by integrating all data processing
components into a convolutional neural network (CNN) architecture that is
designed to propagate clinical target information down to the raw input images.
This approach enables simultaneous and target-specific optimization of image
normalization, signal exploitation, global representation learning and
classification. Using a multicentric data set of 222 patients, we demonstrate
that our approach significantly improves clinical decision making with respect
to the current state of the art.
Image-embodied Knowledge Representation Learning
Authors: Ruobing Xie, Zhiyuan Liu, Huanbo Luan, Maosong Sun
Date: 2016-09-22 00:00:00
Tasks: General Classification, Knowledge Graph Completion, Representation Learning, Triple Classification

Entity images could provide significant visual information for knowledge
representation learning. Most conventional methods learn knowledge
representations merely from structured triples, ignoring rich visual
information extracted from entity images. In this paper, we propose a novel
Image-embodied Knowledge Representation Learning model (IKRL), where knowledge
representations are learned with both triple facts and images. More
specifically, we first construct representations for all images of an entity
with a neural image encoder. These image representations are then integrated
into an aggregated image-based representation via an attention-based method. We
evaluate our IKRL models on knowledge graph completion and triple
classification. Experimental results demonstrate that our models outperform all
baselines on both tasks, which indicates the significance of visual information
for knowledge representations and the capability of our models in learning
knowledge representations with images.
Classification Using Proximity Catch Digraphs (Technical Report)
Authors: Artür Manukyan, Elvan Ceyhan
Date: 2017-05-22 00:00:00
Tasks: Classification, General Classification

We employ random geometric digraphs to construct semi-parametric classifiers.
These data-random digraphs are from parametrized random digraph families called
proximity catch digraphs (PCDs). A related geometric digraph family, class
cover catch digraph (CCCD), has been used to solve the class cover problem by
using its approximate minimum dominating set. CCCDs showed relatively good
performance in the classification of imbalanced data sets, and although CCCDs
have a convenient construction in $\mathbb{R}^d$, finding minimum dominating
sets is NP-hard and its probabilistic behaviour is not mathematically tractable
except for $d=1$. On the other hand, a particular family of PCDs, called
\emph{proportional-edge} PCDs (PE-PCDs), has mathematical tractable minimum
dominating sets in $\mathbb{R}^d$; however their construction in higher
dimensions may be computationally demanding. More specifically, we show that
the classifiers based on PE-PCDs are prototype-based classifiers such that the
exact minimum number of prototypes (equivalent to minimum dominating sets) are
found in polynomial time on the number of observations. We construct two types
of classifiers based on PE-PCDs. One is a family of hybrid classifiers depend
on the location of the points of the training data set, and another type is a
family of classifiers solely based on class covers. We assess the
classification performance of our PE-PCD based classifiers by extensive Monte
Carlo simulations, and compare them with that of other commonly used
classifiers. We also show that, similar to CCCD classifiers, our classifiers
are relatively better in classification in the presence of class imbalance.
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Authors: Hao Wang, Xingyu Lin, Yimeng Zhang, Tai Sing Lee
Date: 2017-05-22 00:00:00
Tasks: Object, Object Recognition

Recurrent feedback connections in the mammalian visual system have been
hypothesized to play a role in synthesizing input in the theoretical framework
of analysis by synthesis. The comparison of internally synthesized
representation with that of the input provides a validation mechanism during
perceptual inference and learning. Inspired by these ideas, we proposed that
the synthesis machinery can compose new, unobserved images by imagination to
train the network itself so as to increase the robustness of the system in
novel scenarios. As a proof of concept, we investigated whether images composed
by imagination could help an object recognition system to deal with occlusion,
which is challenging for the current state-of-the-art deep convolutional neural
networks. We fine-tuned a network on images containing objects in various
occlusion scenarios, that are imagined or self-generated through a deep
generator network. Trained on imagined occluded scenarios under the object
persistence constraint, our network discovered more subtle and localized image
features that were neglected by the original network for object classification,
obtaining better separability of different object classes in the feature space.
This leads to significant improvement of object recognition under occlusion for
our network relative to the original network trained only on un-occluded
images. In addition to providing practical benefits in object recognition under
occlusion, this work demonstrates the use of self-generated composition of
visual scenes through the synthesis loop, combined with the object persistence
constraint, can provide opportunities for neural networks to discover new
relevant patterns in the data, and become more flexible in dealing with novel
situations.
Improved Clustering with Augmented k-means
Authors: J. Andrew Howe
Date: 2017-05-22 00:00:00
Tasks: Clustering, regression

Identifying a set of homogeneous clusters in a heterogeneous dataset is one
of the most important classes of problems in statistical modeling. In the realm
of unsupervised partitional clustering, k-means is a very important algorithm
for this. In this technical report, we develop a new k-means variant called
Augmented k-means, which is a hybrid of k-means and logistic regression. During
each iteration, logistic regression is used to predict the current cluster
labels, and the cluster belonging probabilities are used to control the
subsequent re-estimation of cluster means. Observations which can't be firmly
identified into clusters are excluded from the re-estimation step. This can be
valuable when the data exhibit many characteristics of real datasets such as
heterogeneity, non-sphericity, substantial overlap, and high scatter. Augmented
k-means frequently outperforms k-means by more accurately classifying
observations into known clusters and / or converging in fewer iterations. We
demonstrate this on both simulated and real datasets. Our algorithm is
implemented in Python and will be available with this report.
An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions
Authors: Yuping Shen, Hassan Foroosh
Date: 2017-05-22 00:00:00

In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is inspired by the strong evidence in the applied perception
community that humans perform recognition in a foveated manner, that is they
recognize events or objects by only focusing on visually significant aspects.
An important contribution of our method is that the computation of the weights
assigned to body parts is invariant to viewing directions and camera parameters
in the input data. We have performed extensive experiments to validate the
proposed approach and demonstrate its significance. In particular, results show
that considerable improvement in performance is gained by taking into account
the relative importance of different body parts as defined by our approach.
Note on Evolution and Forecasting of Requirements: Communications Example
Authors: Mark Sh. Levin
Date: 2017-05-22 00:00:00
Tasks: Multiple-choice

Combinatorial evolution and forecasting of system requirements is examined.
The morphological model is used for a hierarchical requirements system (i.e.,
system parts, design alternatives for the system parts, ordinal estimates for
the alternatives). A set of system changes involves changes of the system
structure, component alternatives and their estimates. The composition process
of the forecast is based on combinatorial synthesis (knapsack problem, multiple
choice problem, hierarchical morphological design). An illustrative numerical
example for four-phase evolution and forecasting of requirements to
communications is described.
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
Authors: Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim, Juhan Nam
Date: 2017-03-06 00:00:00
Tasks: Music Auto-Tagging, Music Classification

Recently, the end-to-end approach that learns hierarchical representations
from raw data using deep convolutional neural networks has been successfully
explored in the image, text and speech domains. This approach was applied to
musical signals as well but has been not fully explored yet. To this end, we
propose sample-level deep convolutional neural networks which learn
representations from very small grains of waveforms (e.g. 2 or 3 samples)
beyond typical frame-level input representations. Our experiments show how deep
architectures with sample-level filters improve the accuracy in music
auto-tagging and they provide results comparable to previous state-of-the-art
performances for the Magnatagatune dataset and Million Song Dataset. In
addition, we visualize filters learned in a sample-level DCNN in each layer to
identify hierarchically learned features and show that they are sensitive to
log-scaled frequency along layer, such as mel-frequency spectrogram that is
widely used in music classification systems.
Multiple Object Tracking: A Literature Review
Authors: Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Tae-Kyun Kim
Date: 2014-09-26 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking

Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent advances in various aspects and propose some interesting directions for future research. To the best of our knowledge, there has not been any extensive review on this topic in the community. We endeavor to provide a thorough review on the development of this problem in recent decades. The main contributions of this review are fourfold: 1) Key aspects in an MOT system, including formulation, categorization, key principles, evaluation of MOT are discussed; 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks; 3) We examine experiments of existing publications and summarize results on popular datasets to provide quantitative and comprehensive comparisons. By analyzing the results from different perspectives, we have verified some basic agreements in the field; and 4) We provide a discussion about issues of MOT research, as well as some interesting directions which will become potential research effort in the future.
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
Authors: Renbo Zhao, William B. Haskell, Jiashi Feng
Date: 2017-05-19 00:00:00

We propose a unified framework to speed up the existing stochastic matrix
factorization (SMF) algorithms via variance reduction. Our framework is general
and it subsumes several well-known SMF formulations in the literature. We
perform a non-asymptotic convergence analysis of our framework and derive
computational and sample complexities for our algorithm to converge to an
$\epsilon$-stationary point in expectation. In addition, extensive experiments
for a wide class of SMF formulations demonstrate that our framework
consistently yields faster convergence and a more accurate output dictionary
vis-\`a-vis state-of-the-art frameworks.
Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization
Authors: Yue Yu, Longbo Huang
Date: 2017-05-11 00:00:00
Tasks: BIG-bench Machine Learning

We consider the stochastic composition optimization problem proposed in
\cite{wang2017stochastic}, which has applications ranging from estimation to
statistical and machine learning. We propose the first ADMM-based algorithm
named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly
convex and Lipschitz smooth objectives, and has a convergence rate of $O( \log
S/S)$, which improves upon the $O(S^{-4/9})$ rate in
\cite{wang2016accelerating} when the objective is convex and Lipschitz smooth.
Moreover, com-SVR-ADMM possesses a rate of $O(1/\sqrt{S})$ when the objective
is convex but without Lipschitz smoothness. We also conduct experiments and
show that it outperforms existing algorithms.
Classification and Retrieval of Digital Pathology Scans: A New Dataset
Authors: Morteza Babaie, Shivam Kalra, Aditya Sriram, Christopher Mitcheltree, Shujin Zhu, Amin Khatami, Shahryar Rahnamayan, H. R. Tizhoosh
Date: 2017-05-22 00:00:00
Tasks: Benchmarking, General Classification, image-classification, Image Classification, Retrieval

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according
to preferences of algorithm designer and can range from approximately 27,000 to
over 50,000 patches if the preset parameters are adopted. We propose a compound
patch-and-scan accuracy measurement that makes achieving high accuracies quite
challenging. In addition, we set the benchmarking line by applying LBP,
dictionary approach and convolutional neural nets (CNNs) and report their
results. The highest accuracy was 41.80\% for CNN.
Coalition Formability Semantics with Conflict-Eliminable Sets of Arguments
Authors: Ryuta Arisaka, Ken Satoh
Date: 2016-05-02 00:00:00
Tasks: Abstract Argumentation

We consider abstract-argumentation-theoretic coalition formability in this
work. Taking a model from political alliance among political parties, we will
contemplate profitability, and then formability, of a coalition. As is commonly
understood, a group forms a coalition with another group for a greater good,
the goodness measured against some criteria. As is also commonly understood,
however, a coalition may deliver benefits to a group X at the sacrifice of
something that X was able to do before coalition formation, which X may be no
longer able to do under the coalition. Use of the typical conflict-free sets of
arguments is not very fitting for accommodating this aspect of coalition, which
prompts us to turn to a weaker notion, conflict-eliminability, as a property
that a set of arguments should primarily satisfy. We require numerical
quantification of attack strengths as well as of argument strengths for its
characterisation. We will first analyse semantics of profitability of a given
conflict-eliminable set forming a coalition with another conflict-eliminable
set, and will then provide four coalition formability semantics, each of which
formalises certain utility postulate(s) taking the coalition profitability into
account.
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention
Authors: Feiyun Zhu, Peng Liao
Date: 2017-04-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online reinforcement learning (RL) is increasingly popular for the
personalized mobile health (mHealth) intervention. It is able to personalize
the type and dose of interventions according to user's ongoing statuses and
changing needs. However, at the beginning of online learning, there are usually
too few samples to support the RL updating, which leads to poor performances. A
delay in good performance of the online learning algorithms can be especially
detrimental in the mHealth, where users tend to quickly disengage with the
mHealth app. To address this problem, we propose a new online RL methodology
that focuses on an effective warm start. The main idea is to make full use of
the data accumulated and the decision rule achieved in a former study. As a
result, we can greatly enrich the data size at the beginning of online learning
in our method. Such case accelerates the online learning process for new users
to achieve good performances not only at the beginning of online learning but
also through the whole online learning process. Besides, we use the decision
rules achieved in a previous study to initialize the parameter in our online RL
model for new users. It provides a good initialization for the proposed online
RL algorithm. Experiment results show that promising improvements have been
achieved by our method compared with the state-of-the-art method.
Annealed Generative Adversarial Networks
Authors: Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi
Date: 2017-05-21 00:00:00

We introduce a novel framework for adversarial training where the target
distribution is annealed between the uniform distribution and the data
distribution. We posited a conjecture that learning under continuous annealing
in the nonparametric regime is stable irrespective of the divergence measures
in the objective function and proposed an algorithm, dubbed {\ss}-GAN, in
corollary. In this framework, the fact that the initial support of the
generative network is the whole ambient space combined with annealing are key
to balancing the minimax game. In our experiments on synthetic data, MNIST, and
CelebA, {\ss}-GAN with a fixed annealing schedule was stable and did not suffer
from mode collapse.
Parallel and in-process compilation of individuals for genetic programming on GPU
Authors: Hakan Ayral, Songül Albayrak
Date: 2017-05-21 00:00:00
Tasks: GPU

Three approaches to implement genetic programming on GPU hardware are
compilation, interpretation and direct generation of machine code. The compiled
approach is known to have a prohibitive overhead compared to other two. This
paper investigates methods to accelerate compilation of individuals for genetic
programming on GPU hardware. We apply in-process compilation to minimize the
compilation overhead at each generation; and we investigate ways to parallelize
in-process compilation. In-process compilation doesn't lend itself to trivial
parallelization with threads; we propose a multiprocess parallelization using
memory sharing and operating systems interprocess communication primitives.
With parallelized compilation we achieve further reductions on compilation
overhead. Another contribution of this work is the code framework we built in
C# for the experiments. The framework makes it possible to build arbitrary
grammatical genetic programming experiments that run on GPU with minimal extra
coding effort, and is available as open source.
Improved Algorithms for Matrix Recovery from Rank-One Projections
Authors: Mohammadreza Soltani, Chinmay Hegde
Date: 2017-05-21 00:00:00

We consider the problem of estimation of a low-rank matrix from a limited
number of noisy rank-one projections. In particular, we propose two fast,
non-convex \emph{proper} algorithms for matrix recovery and support them with
rigorous theoretical analysis. We show that the proposed algorithms enjoy
linear convergence and that their sample complexity is independent of the
condition number of the unknown true low-rank matrix. By leveraging recent
advances in low-rank matrix approximation techniques, we show that our
algorithms achieve computational speed-ups over existing methods. Finally, we
complement our theory with some numerical experiments.
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features
Authors: Cheng-Yaw Low, Andrew Beng-Jin Teoh
Date: 2017-03-04 00:00:00
Tasks: Object Recognition

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
network (DNN) resemblance in terms of its very deep, feedforward network
architecture. The typical S-DNN aggregates a variable number of individually
learnable modules in series to assemble a DNN-alike alternative to the targeted
object recognition tasks. This work likewise devises an S-DNN instantiation,
dubbed deep analytic network (DAN), on top of the spectral histogram (SH)
features. The DAN learning principle relies on ridge regression, and some key
DNN constituents, specifically, rectified linear unit, fine-tuning, and
normalization. The DAN aptitude is scrutinized on three repositories of varying
domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10
(natural objects). The empirical results unveil that DAN escalates the SH
baseline performance over a sufficiently deep layer.
Experience enrichment based task independent reward model
Authors: Min Xu
Date: 2017-05-21 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

For most reinforcement learning approaches, the learning is performed by
maximizing an accumulative reward that is expectedly and manually defined for
specific tasks. However, in real world, rewards are emergent phenomena from the
complex interactions between agents and environments. In this paper, we propose
an implicit generic reward model for reinforcement learning. Unlike those
rewards that are manually defined for specific tasks, such implicit reward is
task independent. It only comes from the deviation from the agents' previous
experiences.