pwc-archive/papers-with-abstracts huggingface dataset:
Topology and dynamics of narratives on Brexit propagated by UK press during 2016 and 2017
Authors: Jorge Louçã, António Fonseca
Date: 2019-02-22 00:00:00
Tasks: Articles

This article identifies and characterises political narratives regarding
Europe and broadcasted in UK press during 2016 and 2017. A new theoretical and
operational framework is proposed for typifying discourse narratives propagated
in the public opinion space, based on the social constructivism and structural
linguistics approaches, and the mathematical theory of hypernetworks, where
elementary units are aggregated into high-level entities. In this line of
thought, a narrative is understood as a social construct where a related and
coherent aggregate of terms within public discourse is repeated and propagated
on media until it can be identified as a communication pattern, embodying
meaning in a way that provides individuals some interpretation of their world.
An inclusive methodology, with state-of-the-art technologies on natural
language processing and network theory, implements this concept of narrative. A
corpus from the Observatorium database, including articles from six UK
newspapers and incorporating far-right, right-wing, and left-wing narratives,
is analysed. The research revealed clear distinctions between narratives along
the political spectrum. In 2016 far-right was particularly focused on
emigration and refugees. Namely, during the referendum campaign, Europe was
related to attacks on women and children, sexual offences, and terrorism.
Right-wing was manly focused on internal politics, while left-wing was
remarkably mentioning a diversity of non-political topics, such as sports, side
by side with economics. During 2017, in general terrorism was less mentioned,
and negotiations with EU, namely regarding economics, finance, and Ireland,
became central.
Towards end-to-end pulsed eddy current classification and regression with CNN
Authors: Xin Fu, Chengkai Zhang, Xiang Peng, Lihua Jian, Zheng Liu
Date: 2019-02-22 00:00:00
Tasks: General Classification, regression

Pulsed eddy current (PEC) is an effective electromagnetic non-destructive
inspection (NDI) technique for metal materials, which has already been widely
adopted in detecting cracking and corrosion in some multi-layer structures.
Automatically inspecting the defects in these structures would be conducive to
further analysis and treatment of them. In this paper, we propose an effective
end-to-end model using convolutional neural networks (CNN) to learn effective
features from PEC data. Specifically, we construct a multi-task generic model,
based on 1D CNN, to predict both the class and depth of flaws simultaneously.
Extensive experiments demonstrate our model is capable of handling both
classification and regression tasks on PEC data. Our proposed model obtains
higher accuracy and lower error compared to other standard methods.
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment
Authors: Ziqi Yang, Ee-Chien Chang, Zhenkai Liang
Date: 2019-02-22 00:00:00

The rise of deep learning technique has raised new privacy concerns about the
training data and test data. In this work, we investigate the model inversion
problem in the adversarial settings, where the adversary aims at inferring
information about the target model's training data and test data from the
model's prediction values. We develop a solution to train a second neural
network that acts as the inverse of the target model to perform the inversion.
The inversion model can be trained with black-box accesses to the target model.
We propose two main techniques towards training the inversion model in the
adversarial settings. First, we leverage the adversary's background knowledge
to compose an auxiliary set to train the inversion model, which does not
require access to the original training data. Second, we design a
truncation-based technique to align the inversion model to enable effective
inversion of the target model from partial predictions that the adversary
obtains on victim user's data. We systematically evaluate our inversion
approach in various machine learning tasks and model architectures on multiple
image datasets. Our experimental results show that even with no full knowledge
about the target model's training data, and with only partial prediction
values, our inversion approach is still able to perform accurate inversion of
the target model, and outperform previous approaches.
Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models
Authors: Xin Fu, Jia Yan, Cien Fan
Date: 2019-02-22 00:00:00
Tasks: image-classification, Image Classification, Scene Recognition

Deep convolutional neural networks have recently achieved great success on
image aesthetics assessment task. In this paper, we propose an efficient method
which takes the global, local and scene-aware information of images into
consideration and exploits the composite features extracted from corresponding
pretrained deep learning models to classify the derived features with support
vector machine. Contrary to popular methods that require fine-tuning or
training a new model from scratch, our training-free method directly takes the
deep features generated by off-the-shelf models for image classification and
scene recognition. Also, we analyzed the factors that could influence the
performance from two aspects: the architecture of the deep neural network and
the contribution of local and scene-aware information. It turns out that deep
residual network could produce more aesthetics-aware image representation and
composite features lead to the improvement of overall performance. Experiments
on common large-scale aesthetics assessment benchmarks demonstrate that our
method outperforms the state-of-the-art results in photo aesthetics assessment.
A Graph-Based Machine Learning Approach for Bot Detection
Authors: Abbas Abou Daya, Mohammad A. Salahuddin, Noura Limam, Raouf Boutaba
Date: 2019-02-22 00:00:00
Tasks: BIG-bench Machine Learning

Bot detection using machine learning (ML), with network flow-level features,
has been extensively studied in the literature. However, existing flow-based
approaches typically incur a high computational overhead and do not completely
capture the network communication patterns, which can expose additional aspects
of malicious hosts. Recently, bot detection systems which leverage
communication graph analysis using ML have gained attention to overcome these
limitations. A graph-based approach is rather intuitive, as graphs are true
representations of network communications. In this paper, we propose a
two-phased, graph-based bot detection system which leverages both unsupervised
and supervised ML. The first phase prunes presumable benign hosts, while the
second phase achieves bot detection with high precision. Our system detects
multiple types of bots and is robust to zero-day attacks. It also accommodates
different network topologies and is suitable for large-scale data.
Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data
Authors: Xiaoxiao He, Chaowei Tan, Yuting Qiao, Virak Tan, Dimitris Metaxas, Kang Li
Date: 2019-02-22 00:00:00
Tasks: Segmentation

For the initial shoulder preoperative diagnosis, it is essential to obtain a
three-dimensional (3D) bone mask from medical images, e.g., magnetic resonance
(MR). However, obtaining high-resolution and dense medical scans is both costly
and time-consuming. In addition, the imaging parameters for each 3D scan may
vary from time to time and thus increase the variance between images.
Therefore, it is practical to consider the bone extraction on low-resolution
data which may influence imaging contrast and make the segmentation work
difficult. In this paper, we present a joint segmentation for the humerus and
scapula bones on a small dataset with low-contrast and high-shape-variability
3D MR images. The proposed network has a deep end-to-end architecture to obtain
the initial 3D bone masks. Because the existing scarce and inaccurate
human-labeled ground truth, we design a self-reinforced learning strategy to
increase performance. By comparing with the non-reinforced segmentation and a
classical multi-atlas method with joint label fusion, the proposed approach
obtains better results.
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Authors: Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer
Date: 2019-02-22 00:00:00
Tasks: Gaussian Processes, Numerical Integration, regression

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we avoid the discretization schemes of classical approaches. This leads to significant improvements in parameter accuracy and robustness given random initial guesses. On four established benchmark systems, we compare the performance of our algorithms to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.
Model-based clustering in very high dimensions via adaptive projections
Authors: Bernd Taschler, Frank Dondelinger, Sach Mukherjee
Date: 2019-02-22 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

Mixture models are a standard approach to dealing with heterogeneous data
with non-i.i.d. structure. However, when the dimension $p$ is large relative to
sample size $n$ and where either or both of means and covariances/graphical
models may differ between the latent groups, mixture models face statistical
and computational difficulties and currently available methods cannot
realistically go beyond $p \! \sim \! 10^4$ or so. We propose an approach
called Model-based Clustering via Adaptive Projections (MCAP). Instead of
estimating mixtures in the original space, we work with a low-dimensional
representation obtained by linear projection. The projection dimension itself
plays an important role and governs a type of bias-variance tradeoff with
respect to recovery of the relevant signals. MCAP sets the projection dimension
automatically in a data-adaptive manner, using a proxy for the assignment risk.
Combining a full covariance formulation with the adaptive projection allows
detection of both mean and covariance signals in very high dimensional
problems. We show real-data examples in which covariance signals are reliably
detected in problems with $p \! \sim \! 10^4$ or more, and simulations going up
to $p = 10^6$. In some examples, MCAP performs well even when the mean signal
is entirely removed, leaving differential covariance structure in the
high-dimensional space as the only signal. Across a number of regimes, MCAP
performs as well or better than a range of existing methods, including a
recently-proposed $\ell_1$-penalized approach; and performance remains broadly
stable with increasing dimension. MCAP can be run "out of the box" and is fast
enough for interactive use on large-$p$ problems using standard desktop
computing resources.
Diversity of Ensembles for Data Stream Classification
Authors: Mohamed Souhayel Abassi
Date: 2019-02-22 00:00:00
Tasks: Classification, Diversity, Ensemble Learning, General Classification

When constructing a classifier ensemble, diversity among the base classifiers
is one of the important characteristics. Several studies have been made in the
context of standard static data, in particular, when analyzing the relationship
between a high ensemble predictive performance and the diversity of its
components. Besides, ensembles of learning machines have been performed to
learn in the presence of concept drift and adapt to it. However, diversity
measures have not received much research interest in evolving data streams.
Only a few researchers directly consider promoting diversity while constructing
an ensemble or rebuilding them in the moment of detecting drifts. In this
paper, we present a theoretical analysis of different diversity measures and
relate them to the success of ensemble learning algorithms for streaming data.
The analysis provides a deeper understanding of the concept of diversity and
its impact on online ensemble Learning in the presence of concept drift. More
precisely, we are interested in answering the following research question;
Which commonly used diversity measures are used in the context of static-data
ensembles and how far are they applicable in the context of streaming data
ensembles?
Fine-grained Search Space Classification for Hard Enumeration Variants of Subset Problems
Authors: Juho Lauri, Sourav Dutta
Date: 2019-02-22 00:00:00
Tasks: General Classification

We propose a simple, powerful, and flexible machine learning framework for
(i) reducing the search space of computationally difficult enumeration variants
of subset problems and (ii) augmenting existing state-of-the-art solvers with
informative cues arising from the input distribution. We instantiate our
framework for the problem of listing all maximum cliques in a graph, a central
problem in network analysis, data mining, and computational biology. We
demonstrate the practicality of our approach on real-world networks with
millions of vertices and edges by not only retaining all optimal solutions, but
also aggressively pruning the input instance size resulting in several fold
speedups of state-of-the-art algorithms. Finally, we explore the limits of
scalability and robustness of our proposed framework, suggesting that
supervised learning is viable for tackling NP-hard problems in practice.
Nonconvex sampling with the Metropolis-adjusted Langevin algorithm
Authors: Oren Mangoubi, Nisheeth K. Vishnoi
Date: 2019-02-22 00:00:00

The Langevin Markov chain algorithms are widely deployed methods to sample
from distributions in challenging high-dimensional and non-convex statistics
and machine learning applications. Despite this, current bounds for the
Langevin algorithms are slower than those of competing algorithms in many
important situations, for instance when sampling from weakly log-concave
distributions, or when sampling or optimizing non-convex log-densities. In this
paper, we obtain improved bounds in many of these situations, showing that the
Metropolis-adjusted Langevin algorithm (MALA) is faster than the best bounds
for its competitor algorithms when the target distribution satisfies weak
third- and fourth- order regularity properties associated with the input data.
In many settings, our regularity conditions are weaker than the usual Euclidean
operator norm regularity properties, allowing us to show faster bounds for a
much larger class of distributions than would be possible with the usual
Euclidean operator norm approach, including in statistics and machine learning
applications where the data satisfy a certain incoherence condition. In
particular, we show that using our regularity conditions one can obtain faster
bounds for applications which include sampling problems in Bayesian logistic
regression with weakly convex priors, and the nonconvex optimization problem of
learning linear classifiers with zero-one loss functions.
  Our main technical contribution in this paper is our analysis of the
Metropolis acceptance probability of MALA in terms of its "energy-conservation
error," and our bound for this error in terms of third- and fourth- order
regularity conditions. Our combination of this higher-order analysis of the
energy conservation error with the conductance method is key to obtaining
bounds which have a sub-linear dependence on the dimension $d$ in the
non-strongly logconcave setting.
Robust Graph Embedding with Noisy Link Weights
Authors: Akifumi Okuno, Hidetoshi Shimodaira
Date: 2019-02-22 00:00:00
Tasks: Graph Embedding

We propose $\beta$-graph embedding for robustly learning feature vectors from
data vectors and noisy link weights. A newly introduced empirical moment
$\beta$-score reduces the influence of contamination and robustly measures the
difference between the underlying correct expected weights of links and the
specified generative model. The proposed method is computationally tractable;
we employ a minibatch-based efficient stochastic algorithm and prove that this
algorithm locally minimizes the empirical moment $\beta$-score. We conduct
numerical experiments on synthetic and real-world datasets.
Online Meta-Learning
Authors: Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
Date: 2019-02-22 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Lifelong learning, Meta-Learning

A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an $\mathcal{O}(\log T)$ regret guarantee with only one additional higher order smoothness assumption in comparison to the standard online setting. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.
The Multi-Lane Capsule Network (MLCN)
Authors: Vanderson Martins do Rosario, Edson Borin, Mauricio Breternitz Jr
Date: 2019-02-22 00:00:00

We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and
resource efficient organization of Capsule Networks (CapsNet) that allows
parallel processing, while achieving high accuracy at reduced cost. A MLCN is
composed of a number of (distinct) parallel lanes, each contributing to a
dimension of the result, trained using the routing-by-agreement organization of
CapsNet. Our results indicate similar accuracy with a much reduced cost in
number of parameters for the Fashion-MNIST and Cifar10 datsets. They also
indicate that the MLCN outperforms the original CapsNet when using a proposed
novel configuration for the lanes. MLCN also has faster training and inference
times, being more than two-fold faster than the original CapsNet in the same
accelerator.
Adversarial Attacks on Graph Neural Networks via Meta Learning
Authors: Daniel Zügner, Stephan Günnemann
Date: 2019-02-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Meta-Learning, Node Classification

Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation
Authors: Yajing Zheng, Shanshan Jia, Zhaofei Yu, Tiejun Huang, Jian. K. Liu, Yonghong Tian
Date: 2019-02-22 00:00:00
Tasks: Denoising, Image Denoising, valid

Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilistic inference can be implemented by a network of spiking neurons in the brain. Previous studies have tried to relate the inference equation of binary Markov random fields to the dynamic equation of spiking neural networks through belief propagation algorithm and reparameterization, but they are valid only for Markov random fields with limited network structure. In this paper, we propose a spiking neural network model that can implement inference of arbitrary binary Markov random fields. Specifically, we design a spiking recurrent neural network and prove that its neuronal dynamics are mathematically equivalent to the inference process of Markov random fields by adopting mean-field theory. Furthermore, our mean-field approach unifies previous works. Theoretical analysis and experimental results, together with the application to image denoising, demonstrate that our proposed spiking neural network can get comparable results to that of mean-field inference.
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Authors: Elena Pastorelli, Cristiano Capone, Francesco Simula, Maria V. Sanchez-Vives, Paolo del Giudice, Maurizio Mattia, Pier Stanislao Paolucci
Date: 2019-02-22 00:00:00

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.