pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Active Subspace Decomposition for Scalable and Efficient Tensor Robust Principal Component Analysis
Authors: Jonathan Q. Jiang, Michael K. Ng
Date: 2017-12-28 00:00:00

Tensor robust principal component analysis (TRPCA) has received a substantial
amount of attention in various fields. Most existing methods, normally relying
on tensor nuclear norm minimization, need to pay an expensive computational
cost due to multiple singular value decompositions (SVDs) at each iteration. To
overcome the drawback, we propose a scalable and efficient method, named
Parallel Active Subspace Decomposition (PASD), which divides the unfolding
along each mode of the tensor into a columnwise orthonormal matrix (active
subspace) and another small-size matrix in parallel. Such a transformation
leads to a nonconvex optimization problem in which the scale of nulcear norm
minimization is generally much smaller than that in the original problem.
Furthermore, we introduce an alternating direction method of multipliers (ADMM)
method to solve the reformulated problem and provide rigorous analyses for its
convergence and suboptimality. Experimental results on synthetic and real-world
data show that our algorithm is more accurate than the state-of-the-art
approaches, and is orders of magnitude faster.
Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories
Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan
Date: 2016-09-15 00:00:00

In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain specific knowledge. Although intuitive, recent work in deep learning has shown this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multi-channel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories we "fade" the player trajectories. We find that this approach is superior to a traditional FFN model. By using gradient ascent to create images using an already trained CNN, we discover what features the CNN filters learn. Last, we find that a combined CNN+FFN is the best performing network with an error rate of 39%.
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Authors: Xiangrui Zeng, Miguel Ricardo Leung, Tzviya Zeev-Ben-Mordehai, Min Xu
Date: 2017-06-15 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.
Declarative Statistics
Authors: Roberto Rossi, Özgür Akgün, Steven Prestwich, S. Armagan Tarim
Date: 2017-08-06 00:00:00

In this work we introduce declarative statistics, a suite of declarative
modelling tools for statistical analysis. Statistical constraints represent the
key building block of declarative statistics. First, we introduce a range of
relevant counting and matrix constraints and associated decompositions, some of
which novel, that are instrumental in the design of statistical constraints.
Second, we introduce a selection of novel statistical constraints and
associated decompositions, which constitute a self-contained toolbox that can
be used to tackle a wide range of problems typically encountered by
statisticians. Finally, we deploy these statistical constraints to a wide range
of application areas drawn from classical statistics and we contrast our
framework against established practices.
On Convergence of Emphatic Temporal-Difference Learning
Authors: Huizhen Yu
Date: 2015-06-08 00:00:00

We consider emphatic temporal-difference learning algorithms for policy
evaluation in discounted Markov decision processes with finite spaces. Such
algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an
improved solution to the problem of divergence of off-policy
temporal-difference learning with linear function approximation. We present in
this paper the first convergence proofs for two emphatic algorithms,
ETD($\lambda$) and ELSTD($\lambda$). We prove, under general off-policy
conditions, the convergence in $L^1$ for ELSTD($\lambda$) iterates, and the
almost sure convergence of the approximate value functions calculated by both
algorithms using a single infinitely long trajectory. Our analysis involves new
techniques with applications beyond emphatic algorithms leading, for example,
to the first proof that standard TD($\lambda$) also converges under off-policy
training for $\lambda$ sufficiently large.
Minimizing Polarization and Disagreement in Social Networks
Authors: Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
Date: 2017-12-28 00:00:00
Tasks: Recommendation Systems

The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given $n$ agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
  This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
$O(n/\epsilon^2)$ edges that is a $(1+\epsilon)$ approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
  We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a $\sim 60\,000$-fold reduction in polarization
and disagreement.
On the Challenges of Detecting Rude Conversational Behaviour
Authors: Karan Grewal, Khai N. Truong
Date: 2017-12-28 00:00:00
Tasks: BIG-bench Machine Learning

In this study, we aim to identify moments of rudeness between two
individuals. In particular, we segment all occurrences of rudeness in
conversations into three broad, distinct categories and try to identify each.
We show how machine learning algorithms can be used to identify rudeness based
on acoustic and semantic signals extracted from conversations. Furthermore, we
make note of our shortcomings in this task and highlight what makes this
problem inherently difficult. Finally, we provide next steps which are needed
to ensure further success in identifying rudeness in conversations.
Automated Conjecturing VII: The Graph Brain Project & Big Mathematics
Authors: N. Bushaw, C. E. Larson, N. Van Cleemput
Date: 2017-12-28 00:00:00
Tasks: Relation

The Graph Brain Project is an experiment in how the use of automated
mathematical discovery software, databases, large collaboration, and systematic
investigation provide a model for how mathematical research might proceed in
the future.
  Our Project began with the development of a program that can be used to
generate invariant-relation and property-relation conjectures in many areas of
mathematics. This program can produce conjectures which are not implied by
existing (published) theorems. Here we propose a new approach to push forward
existing mathematical research goals---using automated mathematical discovery
software. We suggest how to initiate and harness large-scale collaborative
mathematics. We envision mathematical research labs similar to what exist in
other sciences, new avenues for funding, new opportunities for training
students, and a more efficient and effective use of published mathematical
research.
  And our experiment in graph theory can be imitated in many other areas of
mathematical research. Big Mathematics is the idea of large, systematic,
collaborative research on problems of existing mathematical interest. What is
possible when we put our skills, tools, and results together systematically?
What do we need to build explainable AI systems for the medical domain?
Authors: Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, Douglas B. Kell
Date: 2017-12-28 00:00:00
Tasks: Autonomous Driving, Game of Go, Recommendation Systems, Reinforcement Learning, speech-recognition, Speech Recognition

Artificial intelligence (AI) generally and machine learning (ML) specifically
demonstrate impressive practical success in many different application domains,
e.g. in autonomous driving, speech recognition, or recommender systems. Deep
learning approaches, trained on extremely large data sets or using
reinforcement learning methods have even exceeded human performance in visual
tasks, particularly on playing games such as Atari, or mastering the game of
Go. Even in the medical domain there are remarkable results. The central
problem of such models is that they are regarded as black-box models and even
if we understand the underlying mathematical principles, they lack an explicit
declarative knowledge representation, hence have difficulty in generating the
underlying explanatory structures. This calls for systems enabling to make
decisions transparent, understandable and explainable. A huge motivation for
our approach are rising legal and privacy aspects. The new European General
Data Protection Regulation entering into force on May 25th 2018, will make
black-box approaches difficult to use in business. This does not imply a ban on
automatic learning approaches or an obligation to explain everything all the
time, however, there must be a possibility to make the results re-traceable on
demand. In this paper we outline some of our research topics in the context of
the relatively new area of explainable-AI with a focus on the application in
medicine, which is a very special domain. This is due to the fact that medical
professionals are working mostly with distributed heterogeneous and complex
sources of data. In this paper we concentrate on three sources: images, *omics
data and text. We argue that research in explainable-AI would generally help to
facilitate the implementation of AI/ML in the medical domain, and specifically
help to facilitate transparency and trust.
Evolutionary algorithms
Authors: Anton V. Eremeev
Date: 2015-11-22 00:00:00
Tasks: Evolutionary Algorithms

This manuscript contains an outline of lectures course "Evolutionary Algorithms" read by the author. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming, tabu search and the class of evolutionary algorithms in general. Some facts, such as the Rotation Property of crossover, the Schemata Theorem, GA performance as a local search and "almost surely" convergence of evolutionary algorithms are given with complete proofs. The text is in Russian.
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Authors: Itay Safran, Ohad Shamir
Date: 2017-12-24 00:00:00
Proceeding: ICML 2018 7
Tasks: Vocal Bursts Valence Prediction

We consider the optimization problem associated with training simple ReLU
neural networks of the form $\mathbf{x}\mapsto
\sum_{i=1}^{k}\max\{0,\mathbf{w}_i^\top \mathbf{x}\}$ with respect to the
squared loss. We provide a computer-assisted proof that even if the input
distribution is standard Gaussian, even if the dimension is arbitrarily large,
and even if the target values are generated by such a network, with orthonormal
parameter vectors, the problem can still have spurious local minima once $6\le
k\le 20$. By a concentration of measure argument, this implies that in high
input dimensions, \emph{nearly all} target networks of the relevant sizes lead
to spurious local minima. Moreover, we conduct experiments which show that the
probability of hitting such local minima is quite high, and increasing with the
network size. On the positive side, mild over-parameterization appears to
drastically reduce such local minima, indicating that an over-parameterization
assumption is necessary to get a positive result in this setting.
Improved Inception-Residual Convolutional Neural Network for Object Recognition
Authors: Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
Date: 2017-12-28 00:00:00
Tasks: Object, Object Recognition

Machine learning and computer vision have driven many of the greatest
advances in the modeling of Deep Convolutional Neural Networks (DCNNs).
Nowadays, most of the research has been focused on improving recognition
accuracy with better DCNN models and learning approaches. The recurrent
convolutional approach is not applied very much, other than in a few DCNN
architectures. On the other hand, Inception-v4 and Residual networks have
promptly become popular among computer the vision community. In this paper, we
introduce a new DCNN model called the Inception Recurrent Residual
Convolutional Neural Network (IRRCNN), which utilizes the power of the
Recurrent Convolutional Neural Network (RCNN), the Inception network, and the
Residual network. This approach improves the recognition accuracy of the
Inception-residual network with same number of network parameters. In addition,
this proposed architecture generalizes the Inception network, the RCNN, and the
Residual network with significantly improved training accuracy. We have
empirically evaluated the performance of the IRRCNN model on different
benchmarks including CIFAR-10, CIFAR-100, TinyImageNet-200, and CU3D-100. The
experimental results show higher recognition accuracy against most of the
popular DCNN models including the RCNN. We have also investigated the
performance of the IRRCNN approach against the Equivalent Inception Network
(EIN) and the Equivalent Inception Residual Network (EIRN) counterpart on the
CIFAR-100 dataset. We report around 4.53%, 4.49% and 3.56% improvement in
classification accuracy compared with the RCNN, EIN, and EIRN on the CIFAR-100
dataset respectively. Furthermore, the experiment has been conducted on the
TinyImageNet-200 and CU3D-100 datasets where the IRRCNN provides better testing
accuracy compared to the Inception Recurrent CNN (IRCNN), the EIN, and the
EIRN.
Connecting Software Metrics across Versions to Predict Defects
Authors: Yibin Liu, Yanhui Li, Jianbo Guo, Yuming Zhou, Baowen Xu
Date: 2017-12-28 00:00:00
Tasks: Prediction

Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models.
A Syntactic Approach to Domain-Specific Automatic Question Generation
Authors: Guy Danon, Mark Last
Date: 2017-12-28 00:00:00
Tasks: Question Answering, Question Generation, Question-Generation, Sentence

Factoid questions are questions that require short fact-based answers.
Automatic generation (AQG) of factoid questions from a given text can
contribute to educational activities, interactive question answering systems,
search engines, and other applications. The goal of our research is to generate
factoid source-question-answer triplets based on a specific domain. We propose
a four-component pipeline, which obtains as input a training corpus of
domain-specific documents, along with a set of declarative sentences from the
same domain, and generates as output a set of factoid questions that refer to
the source sentences but are slightly different from them, so that a
question-answering system or a person can be asked a question that requires a
deeper understanding and knowledge than a simple word-matching. Contrary to
existing domain-specific AQG systems that utilize the template-based approach
to question generation, we propose to transform each source sentence into a set
of questions by applying a series of domain-independent rules (a
syntactic-based approach). Our pipeline was evaluated in the domain of cyber
security using a series of experiments on each component of the pipeline
separately and on the end-to-end system. The proposed approach generated a
higher percentage of acceptable questions than a prior state-of-the-art AQG
system.
Accurate Bayesian Data Classification without Hyperparameter Cross-validation
Authors: M Sheikh, A. C. C. Coolen
Date: 2017-12-28 00:00:00
Tasks: Classification, General Classification

We extend the standard Bayesian multivariate Gaussian generative data
classifier by considering a generalization of the conjugate, normal-Wishart
prior distribution and by deriving the hyperparameters analytically via
evidence maximization. The behaviour of the optimal hyperparameters is explored
in the high-dimensional data regime. The classification accuracy of the
resulting generalized model is competitive with state-of-the art Bayesian
discriminant analysis methods, but without the usual computational burden of
cross-validation.
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Authors: Qiangqiang Yuan, Yancong Wei, Xiangchao Meng, Huanfeng Shen, Liangpei Zhang
Date: 2017-12-28 00:00:00

Pan-sharpening is a fundamental and significant task in the field of remote
sensing imagery processing, in which high-resolution spatial details from
panchromatic images are employed to enhance the spatial resolution of
multi-spectral (MS) images. As the transformation from low spatial resolution
MS image to high-resolution MS image is complex and highly non-linear, inspired
by the powerful representation for non-linear relationships of deep neural
networks, we introduce multi-scale feature extraction and residual learning
into the basic convolutional neural network (CNN) architecture and propose the
multi-scale and multi-depth convolutional neural network (MSDCNN) for the
pan-sharpening of remote sensing imagery. Both the quantitative assessment
results and the visual assessment confirm that the proposed network yields
high-resolution MS images that are superior to the images produced by the
compared state-of-the-art methods.
Learning to Customize Network Security Rules
Authors: Michael Bargury, Roy Levin, Royi Ronen
Date: 2017-12-28 00:00:00
Tasks: Blocking, Cloud Computing

Security is a major concern for organizations who wish to leverage cloud
computing. In order to reduce security vulnerabilities, public cloud providers
offer firewall functionalities. When properly configured, a firewall protects
cloud networks from cyber-attacks. However, proper firewall configuration
requires intimate knowledge of the protected system, high expertise and
on-going maintenance.
  As a result, many organizations do not use firewalls effectively, leaving
their cloud resources vulnerable. In this paper, we present a novel supervised
learning method, and prototype, which compute recommendations for firewall
rules. Recommendations are based on sampled network traffic meta-data (NetFlow)
collected from a public cloud provider. Labels are extracted from firewall
configurations deemed to be authored by experts. NetFlow is collected from
network routers, avoiding expensive collection from cloud VMs, as well as
relieving privacy concerns.
  The proposed method captures network routines and dependencies between
resources and firewall configuration. The method predicts IPs to be allowed by
the firewall. A grouping algorithm is subsequently used to generate a
manageable number of IP ranges. Each range is a parameter for a firewall rule.
  We present results of experiments on real data, showing ROC AUC of 0.92,
compared to 0.58 for an unsupervised baseline. The results prove the hypothesis
that firewall rules can be automatically generated based on router data, and
that an automated method can be effective in blocking a high percentage of
malicious traffic.
Siamese LSTM based Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation
Authors: Shreyas Malakarjun Patil, Aditya Nigam, Arnav Bhavsar, Chiranjoy Chattopadhyay
Date: 2017-12-28 00:00:00
Tasks: Segmentation

In this paper, we propose a novel deep learning architecture combining
stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for
segmentation of brain fibers, obtained from tractography data, into
anatomically meaningful clusters. The proposed network learns the structural
difference between fibers of different classes, which enables it to classify
fibers with high accuracy. Importantly, capturing such deep inter and intra
class structural relationship also ensures that the segmentation is robust to
relative rotation among test and training data, hence can be used with
unregistered data. Our extensive experimentation over order of
hundred-thousands of fibers show that the proposed model achieves
state-of-the-art results, even in cases of large relative rotations between
test and training data.