pwc-archive/papers-with-abstracts huggingface dataset:
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
Authors: Zheng Zhao, Simo Särkkä, Ali Bahrami Rad
Date: 2018-12-12 00:00:00
Tasks: Atrial Fibrillation Detection, ECG Classification, Rhythm

In this article, we propose a novel ECG classification framework for atrial
fibrillation (AF) detection using spectro-temporal representation (i.e., time
varying spectrum) and deep convolutional networks. In the first step we use a
Bayesian spectro-temporal representation based on the estimation of
time-varying coefficients of Fourier series using Kalman filter and smoother.
Next, we derive an alternative model based on a stochastic oscillator
differential equation to accelerate the estimation of the spectro-temporal
representation in lengthy signals. Finally, after comparative evaluations of
different convolutional architectures, we propose an efficient deep
convolutional neural network to classify the 2D spectro-temporal ECG data.
  The ECG spectro-temporal data are classified into four different classes: AF,
non-AF normal rhythm (Normal), non-AF abnormal rhythm (Other), and noisy
segments (Noisy). The performance of the proposed methods is evaluated and
scored with the PhysioNet/Computing in Cardiology (CinC) 2017 dataset. The
experimental results show that the proposed method achieves the overall F1
score of 80.2%, which is in line with the state-of-the-art algorithms.
Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity
Authors: Steve DiPaola, Liane Gabora, Graeme McCaig
Date: 2018-12-11 00:00:00

The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a 'seed incident' can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explorations in deep learn-ing convolutional neural net generative systems can inform our understanding of human creativity. We conclude with ideas for further cross-fertilization between AI based computational creativity and psychology of creativity.
Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)
Authors: Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada
Date: 2018-12-11 00:00:00

In this paper, we propose a new method of Bayesian measurement for spectral
deconvolution, which regresses spectral data into the sum of unimodal basis
function such as Gaussian or Lorentzian functions. Bayesian measurement is a
framework for considering not only the target physical model but also the
measurement model as a probabilistic model, and enables us to estimate the
parameter of a physical model with its confidence interval through a Bayesian
posterior distribution given a measurement data set. The measurement with
Poisson noise is one of the most effective system to apply our proposed method.
Since the measurement time is strongly related to the signal-to-noise ratio for
the Poisson noise model, Bayesian measurement with Poisson noise model enables
us to clarify the relationship between the measurement time and the limit of
estimation. In this study, we establish the probabilistic model with Poisson
noise for spectral deconvolution. Bayesian measurement enables us to perform
virtual and computer simulation for a certain measurement through the
established probabilistic model. This property is called "Virtual Measurement
Analytics(VMA)" in this paper. We also show that the relationship between the
measurement time and the limit of estimation can be extracted by using the
proposed method in a simulation of synthetic data and real data for XPS
measurement of MoS$_2$.
Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning for Cross-City Property Appraisal Framework
Authors: Yihan Guo, Shan Lin, Xiao Ma, Jay Bal, Chang-Tsun Li
Date: 2018-12-11 00:00:00

Most existing real estate appraisal methods focus on building accuracy and
reliable models from a given dataset but pay little attention to the
extensibility of their trained model. As different cities usually contain a
different set of location features (district names, apartment names), most
existing mass appraisal methods have to train a new model from scratch for
different cities or regions. As a result, these approaches require massive data
collection for each city and the total training time for a multi-city property
appraisal system will be extremely long. Besides, some small cities may not
have enough data for training a robust appraisal model. To overcome these
limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous
Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By
transferring partial neural network learning from a source city and fine-tuning
on the small amount of location information of a target city, our
semi-supervised model can achieve similar or even superior performance compared
to a fully supervised Artificial neural network (ANN) method.
A Probabilistic Model of the Bitcoin Blockchain
Authors: Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande
Date: 2018-11-07 00:00:00
Tasks: Decision Making Under Uncertainty, model

The Bitcoin transaction graph is a public data structure organized as
transactions between addresses, each associated with a logical entity. In this
work, we introduce a complete probabilistic model of the Bitcoin Blockchain. We
first formulate a set of conditional dependencies induced by the Bitcoin
protocol at the block level and derive a corresponding fully observed graphical
model of a Bitcoin block. We then extend the model to include hidden entity
attributes such as the functional category of the associated logical agent and
derive asymptotic bounds on the privacy properties implied by this model. At
the network level, we show evidence of complex transaction-to-transaction
behavior and present a relevant discriminative model of the agent categories.
Performance of both the block-based graphical model and the network-level
discriminative model is evaluated on a subset of the public Bitcoin Blockchain.
Next Item Recommendation with Self-Attention
Authors: Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun
Date: 2018-08-20 00:00:00
Tasks: Metric Learning

In this paper, we propose a novel sequence-aware recommendation model. Our
model utilizes self-attention mechanism to infer the item-item relationship
from user's historical interactions. With self-attention, it is able to
estimate the relative weights of each item in user interaction trajectories to
learn better representations for user's transient interests. The model is
finally trained in a metric learning framework, taking both short-term and
long-term intentions into consideration. Experiments on a wide range of
datasets on different domains demonstrate that our approach outperforms the
state-of-the-art by a wide margin.
Learning Representations from Product Titles for Modeling Shopping Transactions
Authors: Binh Nguyen, Atsuhiro Takasu
Date: 2018-11-03 00:00:00
Tasks: Product Recommendation

Shopping transaction analysis is important for understanding the shopping behaviors of customers. Existing models such as association rules are poor at modeling products that have short purchase histories and cannot be applied to new products (the cold-start problem). In this paper, we propose BASTEXT, an efficient model of shopping baskets and the texts associated with the products (e.g., product titles). The model's goal is to learn the product representations from the textual contents to capture the relationships between the products in the baskets. Given the products already in a basket, a classifier identifies whether a potential product is relevant to the basket based on their vector representations. This relevancy enables us to learn high-quality representations of the products. The experiments demonstrate that BASTEXT can efficiently model millions of baskets and that it outperforms the state-of-the-art methods in the next product recommendation task. We also show that BASTEXT is a strong baseline for keyword-based product search.
Photometric Redshift Error Estimators
Authors: Hiroaki Oyaizu, Marcos Lima, Carlos E. Cunha, Huan Lin, Joshua Frieman
Date: 2007-11-06 00:00:00
Tasks: Astronomy, Survey

Photometric redshift (photo-z) estimates are playing an increasingly
important role in extragalactic astronomy and cosmology. Crucial to many
photo-z applications is the accurate quantification of photometric redshift
errors and their distributions, including identification of likely catastrophic
failures in photo-z estimates. We consider several methods of estimating
photo-z errors and propose new training-set based error estimators based on
spectroscopic training set data. Using data from the Sloan Digital Sky Survey
and simulations of the Dark Energy Survey as examples, we show that this method
provides a robust, relatively unbiased estimate of photo-z errors. We show that
culling objects with large, accurately estimated photo-z errors from a sample
can reduce the incidence of catastrophic photo-z failures.
Introduction to Machine Learning: Class Notes 67577
Authors: Amnon Shashua
Date: 2009-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, PAC learning

Introduction to Machine learning covering Statistical Inference (Bayes, EM,
ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering),
and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
Authors: Sendong Zhao, Ting Liu, Sicheng Zhao, Fei Wang
Date: 2018-12-14 00:00:00
Tasks: Medical Named Entity Recognition, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

State-of-the-art studies have demonstrated the superiority of joint modelling
over pipeline implementation for medical named entity recognition and
normalization due to the mutual benefits between the two processes. To exploit
these benefits in a more sophisticated way, we propose a novel deep neural
multi-task learning framework with explicit feedback strategies to jointly
model recognition and normalization. On one hand, our method benefits from the
general representations of both tasks provided by multi-task learning. On the
other hand, our method successfully converts hierarchical tasks into a parallel
multi-task setting while maintaining the mutual supports between tasks. Both of
these aspects improve the model performance. Experimental results demonstrate
that our method performs significantly better than state-of-the-art approaches
on two publicly available medical literature datasets.
Reconciling meta-learning and continual learning with online mixtures of tasks
Authors: Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine Heller
Date: 2018-12-14 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Continual Learning, Few-Shot Learning, Inductive Bias, Meta-Learning

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not advantageous, for instance, when tasks are considerably dissimilar or change over time. We use the connection between gradient-based meta-learning and hierarchical Bayes to propose a Dirichlet process mixture of hierarchical Bayesian models over the parameters of an arbitrary parametric model such as a neural network. In contrast to consolidating inductive biases into a single set of hyperparameters, our approach of task-dependent hyperparameter selection better handles latent distribution shift, as demonstrated on a set of evolving, image-based, few-shot learning benchmarks.
On Attention Modules for Audio-Visual Synchronization
Authors: Naji Khosravan, Shervin Ardeshir, Rohit Puri
Date: 2018-12-14 00:00:00
Tasks: Audio-Visual Synchronization

With the development of media and networking technologies, multimedia
applications ranging from feature presentation in a cinema setting to video on
demand to interactive video conferencing are in great demand. Good
synchronization between audio and video modalities is a key factor towards
defining the quality of a multimedia presentation. The audio and visual signals
of a multimedia presentation are commonly managed by independent workflows -
they are often separately authored, processed, stored and even delivered to the
playback system. This opens up the possibility of temporal misalignment between
the two modalities - such a tendency is often more pronounced in the case of
produced content (such as movies).
  To judge whether audio and video signals of a multimedia presentation are
synchronized, we as humans often pay close attention to discriminative
spatio-temporal blocks of the video (e.g. synchronizing the lip movement with
the utterance of words, or the sound of a bouncing ball at the moment it hits
the ground). At the same time, we ignore large portions of the video in which
no discriminative sounds exist (e.g. background music playing in a movie).
Inspired by this observation, we study leveraging attention modules for
automatically detecting audio-visual synchronization. We propose neural network
based attention modules, capable of weighting different portions
(spatio-temporal blocks) of the video based on their respective discriminative
power. Our experiments indicate that incorporating attention modules yields
state-of-the-art results for the audio-visual synchronization classification
problem.
The Boosted DC Algorithm for nonsmooth functions
Authors: Francisco J. Aragón Artacho, Phan T. Vuong
Date: 2018-12-14 00:00:00
Tasks: Clustering

The Boosted Difference of Convex functions Algorithm (BDCA) was recently proposed for minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence of the classical Difference of Convex functions Algorithm (DCA) thanks to an additional line search step. The purpose of this paper is twofold. Firstly, to show that this scheme can be generalized and successfully applied to certain types of nonsmooth DC functions, namely, those that can be expressed as the difference of a smooth function and a possibly nonsmooth one. Secondly, to show that there is complete freedom in the choice of the trial step size for the line search, which is something that can further improve its performance. We prove that any limit point of the BDCA iterative sequence is a critical point of the problem under consideration, and that the corresponding objective value is monotonically decreasing and convergent. The global convergence and convergent rate of the iterations are obtained under the Kurdyka-Lojasiewicz property. Applications and numerical experiments for two problems in data science are presented, demonstrating that BDCA outperforms DCA. Specifically, for the Minimum Sum-of-Squares Clustering problem, BDCA was on average sixteen times faster than DCA, and for the Multidimensional Scaling problem, BDCA was three times faster than DCA.
Non-Factorised Variational Inference in Dynamical Systems
Authors: Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Date: 2018-12-14 00:00:00
Tasks: Variational Inference

We focus on variational inference in dynamical systems where the discrete
time transition function (or evolution rule) is modelled by a Gaussian process.
The dominant approach so far has been to use a factorised posterior
distribution, decoupling the transition function from the system states. This
is not exact in general and can lead to an overconfident posterior over the
transition function as well as an overestimation of the intrinsic stochasticity
of the system (process noise). We propose a new method that addresses these
issues and incurs no additional computational costs.
Discrete minimax estimation with trees
Authors: Luc Devroye, Tommy Reddad
Date: 2018-12-14 00:00:00

We propose a simple recursive data-based partitioning scheme which produces piecewise-constant or piecewise-linear density estimates on intervals, and show how this scheme can determine the optimal $L_1$ minimax rate for some discrete nonparametric classes.
Automatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI
Authors: Ariel H. Curiale, Flavio D. Colavecchia, German Mato
Date: 2018-12-14 00:00:00

Objective: This paper proposes a novel approach for automatic left ventricle
(LV) quantification using convolutional neural networks (CNN).
  Methods: The general framework consists of one CNN for detecting the LV, and
another for tissue classification. Also, three new deep learning architectures
were proposed for LV quantification. These new CNNs introduce the ideas of
sparsity and depthwise separable convolution into the U-net architecture, as
well as, a residual learning strategy level-to-level. To this end, we extend
the classical U-net architecture and use the generalized Jaccard distance as
optimization objective function.
  Results: The CNNs were trained and evaluated with 140 patients from two
public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas
Project) by using a 5-fold cross-validation strategy. Our results demonstrate a
suitable accuracy for myocardial segmentation ($\sim$0.9 Dice's coefficient),
and a strong correlation with the most relevant physiological measures: 0.99
for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass,
0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac
output.
  Conclusion: Our simulation and clinical evaluation results demonstrate the
capability and merits of the proposed CNN to estimate different structural and
functional features such as LV mass and EF which are commonly used for both
diagnosis and treatment of different pathologies.
  Significance: This paper suggests a new approach for automatic LV
quantification based on deep learning where errors are comparable to the inter-
and intra-operator ranges for manual contouring. Also, this approach may have
important applications on motion quantification.
Transfer learning to model inertial confinement fusion experiments
Authors: K. D. Humbird, J. L. Peterson, R. G. McClarren
Date: 2018-12-14 00:00:00
Tasks: Transfer Learning

Inertial confinement fusion (ICF) experiments are designed using computer
simulations that are approximations of reality, and therefore must be
calibrated to accurately predict experimental observations. In this work, we
propose a novel nonlinear technique for calibrating from simulations to
experiments, or from low fidelity simulations to high fidelity simulations, via
"transfer learning". Transfer learning is a commonly used technique in the
machine learning community, in which models trained on one task are partially
retrained to solve a separate, but related task, for which there is a limited
quantity of data. We introduce the idea of hierarchical transfer learning, in
which neural networks trained on low fidelity models are calibrated to high
fidelity models, then to experimental data. This technique essentially
bootstraps the calibration process, enabling the creation of models which
predict high fidelity simulations or experiments with minimal computational
cost. We apply this technique to a database of ICF simulations and experiments
carried out at the Omega laser facility. Transfer learning with deep neural
networks enables the creation of models that are more predictive of Omega
experiments than simulations alone. The calibrated models accurately predict
future Omega experiments, and are used to search for new, optimal implosion
designs.
Inferring the size of the causal universe: features and fusion of causal attribution networks
Authors: Daniel Berenberg, James P. Bagrow
Date: 2018-12-14 00:00:00

Cause-and-effect reasoning, the attribution of effects to causes, is one of
the most powerful and unique skills humans possess. Multiple surveys are
mapping out causal attributions as networks, but it is unclear how well these
efforts can be combined. Further, the total size of the collective causal
attribution network held by humans is currently unknown, making it challenging
to assess the progress of these surveys. Here we study three causal attribution
networks to determine how well they can be combined into a single network.
Combining these networks requires dealing with ambiguous nodes, as nodes
represent written descriptions of causes and effects and different descriptions
may exist for the same concept. We introduce NetFUSES, a method for combining
networks with ambiguous nodes. Crucially, treating the different causal
attributions networks as independent samples allows us to use their overlap to
estimate the total size of the collective causal attribution network. We find
that existing surveys capture 5.77% $\pm$ 0.781% of the $\approx$293 000 causes
and effects estimated to exist, and 0.198% $\pm$ 0.174% of the $\approx$10 200
000 attributed cause-effect relationships.
Factorization of Dempster-Shafer Belief Functions Based on Data
Authors: Andrzej Matuszewski, Mieczysław A. Kłopotek
Date: 2018-12-14 00:00:00

One important obstacle in applying Dempster-Shafer Theory (DST) is its
relationship to frequencies. In particular, there exist serious difficulties in
finding factorizations of belief functions from data.
  In probability theory factorizations are usually related to notion of
(conditional) independence and their possibility tested accordingly. However,
in DST conditional belief distributions prove to be non-proper belief functions
(that is ones connected with negative "frequencies"). This makes statistical
testing of potential conditional independencies practically impossible, as no
coherent interpretation could be found so far for negative belief function
values.
  In this paper a novel attempt is made to overcome this difficulty. In the
proposal no conditional beliefs are calculated, but instead a new measure F is
introduced within the framework of DST, closely related to conditional
independence, allowing to apply conventional statistical tests for detection of
dependence/independence.
Fast Mitochondria Detection for Connectomics
Authors: Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn
Date: 2018-12-14 00:00:00
Proceeding: MIDL 2019 7

High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar. However, manual analysis is not feasible since datasets can be petabytes in size. We present a fully automatic mitochondria detector based on a modified U-Net architecture that yields high accuracy and fast processing times. We evaluate our method on multiple real-world connectomics datasets, including an improved version of the EPFL mitochondria benchmark. Our results show an Jaccard index of up to 0.90 with inference times lower than 16ms for a 512x512px image tile. This speed is faster than the acquisition speed of modern electron microscopes, enabling mitochondria detection in real-time. Our detector ranks first for real-time detection when compared to previous works and data, results, and code are openly available.