pwc-archive/papers-with-abstracts huggingface dataset:
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
Authors: Srikrishna Varadarajan, Muktabh Mayank Srivastava, Monika Grewal, Pulkit Kumar
Date: 2017-10-25 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Relation

This work is an endeavor to develop a deep learning methodology for automated
anatomical labeling of a given region of interest (ROI) in brain computed
tomography (CT) scans. We combine both local and global context to obtain a
representation of the ROI. We then use Relation Networks (RNs) to predict the
corresponding anatomy of the ROI based on its relationship score for each
class. Further, we propose a novel strategy employing nearest neighbors
approach for training RNs. We train RNs to learn the relationship of the target
ROI with the joint representation of its nearest neighbors in each class
instead of all data-points in each class. The proposed strategy leads to better
training of RNs along with increased performance as compared to training
baseline RN network.
Towards Automated Tuberculosis detection using Deep Learning
Authors: Sonaal Kant, Muktabh Mayank Srivastava
Date: 2018-01-22 00:00:00
Tasks: Deep Learning, Sensitivity

Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to
480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost
US$340 Billion due to TB. This combined with the emergence of drug resistant
bacteria in India makes the problem worse. The government of India has hence
come up with a new strategy which requires a high-sensitivity microscopy based
TB diagnosis mechanism. We propose a new Deep Neural Network based drug
sensitive TB detection methodology with recall and precision of 83.78% and
67.55% respectively for bacillus detection. This method takes a microscopy
image with proper zoom level as input and returns location of suspected TB
germs as output. The high accuracy of our method gives it the potential to
evolve into a high sensitivity system to diagnose TB when trained at scale.
BiographyNet: Extracting Relations Between People and Events
Authors: Antske Fokkens, Serge ter Braake, Niels Ockeloen, Piek Vossen, Susan Legêne, Guus Schreiber, Victor de Boer
Date: 2018-01-22 00:00:00
Tasks: Management

This paper describes BiographyNet, a digital humanities project (2012-2016)
that brings together researchers from history, computational linguistics and
computer science. The project uses data from the Biography Portal of the
Netherlands (BPN), which contains approximately 125,000 biographies from a
variety of Dutch biographical dictionaries from the eighteenth century until
now, describing around 76,000 individuals. BiographyNet's aim is to strengthen
the value of the portal and comparable biographical datasets for historical
research, by improving the search options and the presentation of its outcome,
with a historically justified NLP pipeline that works through a user evaluated
demonstrator. The project's main target group are professional historians. The
project therefore worked with two key concepts: "provenance" -understood as a
term allowing for both historical source criticism and for references to
data-management and programming interventions in digitized sources; and
"perspective" interpreted as inherent uncertainty concerning the interpretation
of historical results.
Offline A/B testing for Recommender Systems
Authors: Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec, Alexandre Abraham, Simon Dollé
Date: 2018-01-22 00:00:00
Tasks: counterfactual, Product Recommendation, Recommendation Systems

Before A/B testing online a new version of a recommender system, it is usual
to perform some offline evaluations on historical data. We focus on evaluation
methods that compute an estimator of the potential uplift in revenue that could
generate this new technology. It helps to iterate faster and to avoid losing
money by detecting poor policies. These estimators are known as counterfactual
or off-policy estimators. We show that traditional counterfactual estimators
such as capped importance sampling and normalised importance sampling are
experimentally not having satisfying bias-variance compromises in the context
of personalised product recommendation for online advertising. We propose two
variants of counterfactual estimates with different modelling of the bias that
prove to be accurate in real-world conditions. We provide a benchmark of these
estimators by showing their correlation with business metrics observed by
running online A/B tests on a commercial recommender system.
Binary output layer of feedforward neural networks for solving multi-class classification problems
Authors: Sibo Yang, Chao Zhang, Wei Wu
Date: 2018-01-22 00:00:00
Tasks: General Classification, Multi-class Classification

Considered in this short note is the design of output layer nodes of
feedforward neural networks for solving multi-class classification problems
with r (bigger than or equal to 3) classes of samples. The common and
conventional setting of output layer, called "one-to-one approach" in this
paper, is as follows: The output layer contains r output nodes corresponding to
the r classes. And for an input sample of the i-th class, the ideal output is 1
for the i-th output node, and 0 for all the other output nodes. We propose in
this paper a new "binary approach": Suppose r is (2^(q minus 1), 2^q] with q
bigger than or equal to 2, then we let the output layer contain q output nodes,
and let the ideal outputs for the r classes be designed in a binary manner.
Numerical experiments carried out in this paper show that our binary approach
does equally good job as, but uses less output nodes than, the traditional
one-to-one approach.
Multiplex model of mental lexicon reveals explosive learning in humans
Authors: Massimo Stella, Nicole M. Beckage, Markus Brede, Manlio De Domenico
Date: 2017-05-26 00:00:00
Tasks: Language Acquisition

Word similarities affect language acquisition and use in a multi-relational
way barely accounted for in the literature. We propose a multiplex network
representation of this mental lexicon of word similarities as a natural
framework for investigating large-scale cognitive patterns. Our representation
accounts for semantic, taxonomic, and phonological interactions and it
identifies a cluster of words which are used with greater frequency, are
identified, memorised, and learned more easily, and have more meanings than
expected at random. This cluster emerges around age 7 through an explosive
transition not reproduced by null models. We relate this explosive emergence to
polysemy -- redundancy in word meanings. Results indicate that the word cluster
acts as a core for the lexicon, increasing both lexical navigability and
robustness to linguistic degradation. Our findings provide quantitative
confirmation of existing conjectures about core structure in the mental lexicon
and the importance of integrating multi-relational word-word interactions in
psycholinguistic frameworks.
An Improved LPTC Neural Model for Background Motion Direction Estimation
Authors: Hongxin Wang, Jigen Peng, Shigang Yue
Date: 2018-01-22 00:00:00

A class of specialized neurons, called lobula plate tangential cells (LPTCs)
has been shown to respond strongly to wide-field motion. The classic model,
elementary motion detector (EMD) and its improved model, two-quadrant detector
(TQD) have been proposed to simulate LPTCs. Although EMD and TQD can percept
background motion, their outputs are so cluttered that it is difficult to
discriminate actual motion direction of the background. In this paper, we
propose a max operation mechanism to model a newly-found transmedullary neuron
Tm9 whose physiological properties do not map onto EMD and TQD. This proposed
max operation mechanism is able to improve the detection performance of TQD in
cluttered background by filtering out irrelevant motion signals. We will
demonstrate the functionality of this proposed mechanism in wide-field motion
perception.
Extreme Learning Machine with Local Connections
Authors: Feng Li, Sibo Yang, Huanhuan Huang, Wei Wu
Date: 2018-01-22 00:00:00

This paper is concerned with the sparsification of the input-hidden weights
of ELM (Extreme Learning Machine). For ordinary feedforward neural networks,
the sparsification is usually done by introducing certain regularization
technique into the learning process of the network. But this strategy can not
be applied for ELM, since the input-hidden weights of ELM are supposed to be
randomly chosen rather than to be learned. To this end, we propose a modified
ELM, called ELM-LC (ELM with local connections), which is designed for the
sparsification of the input-hidden weights as follows: The hidden nodes and the
input nodes are divided respectively into several corresponding groups, and an
input node group is fully connected with its corresponding hidden node group,
but is not connected with any other hidden node group. As in the usual ELM, the
hidden-input weights are randomly given, and the hidden-output weights are
obtained through a least square learning. In the numerical simulations on some
benchmark problems, the new ELM-CL behaves better than the traditional ELM.
Mitigating Unwanted Biases with Adversarial Learning
Authors: Brian Hu Zhang, Blake Lemoine, Margaret Mitchell
Date: 2018-01-22 00:00:00
Tasks: Fairness, General Classification

Machine learning is a tool for building models that accurately represent
input training data. When undesired biases concerning demographic groups are in
the training data, well-trained models will reflect those biases. We present a
framework for mitigating such biases by including a variable for the group of
interest and simultaneously learning a predictor and an adversary. The input to
the network X, here text or census data, produces a prediction Y, such as an
analogy completion or income bracket, while the adversary tries to model a
protected variable Z, here gender or zip code.
  The objective is to maximize the predictor's ability to predict Y while
minimizing the adversary's ability to predict Z. Applied to analogy completion,
this method results in accurate predictions that exhibit less evidence of
stereotyping Z. When applied to a classification task using the UCI Adult
(Census) Dataset, it results in a predictive model that does not lose much
accuracy while achieving very close to equality of odds (Hardt, et al., 2016).
The method is flexible and applicable to multiple definitions of fairness as
well as a wide range of gradient-based learning models, including both
regression and classification tasks.
MRI Cross-Modality NeuroImage-to-NeuroImage Translation
Authors: Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I-Chao Chang, Yan Xu
Date: 2018-01-22 00:00:00
Tasks: MRI segmentation, Segmentation, Translation

We present a cross-modality generation framework that learns to generate
translated modalities from given modalities in MR images without real
acquisition. Our proposed method performs NeuroImage-to-NeuroImage translation
(abbreviated as N2N) by means of a deep learning model that leverages
conditional generative adversarial networks (cGANs). Our framework jointly
exploits the low-level features (pixel-wise information) and high-level
representations (e.g. brain tumors, brain structure like gray matter, etc.)
between cross modalities which are important for resolving the challenging
complexity in brain structures. Our framework can serve as an auxiliary method
in clinical diagnosis and has great application potential. Based on our
proposed framework, we first propose a method for cross-modality registration
by fusing the deformation fields to adopt the cross-modality information from
translated modalities. Second, we propose an approach for MRI segmentation,
translated multichannel segmentation (TMS), where given modalities, along with
translated modalities, are segmented by fully convolutional networks (FCN) in a
multichannel manner. Both of these two methods successfully adopt the
cross-modality information to improve the performance without adding any extra
data. Experiments demonstrate that our proposed framework advances the
state-of-the-art on five brain MRI datasets. We also observe encouraging
results in cross-modality registration and segmentation on some widely adopted
brain datasets. Overall, our work can serve as an auxiliary method in clinical
diagnosis and be applied to various tasks in medical fields.
  Keywords: image-to-image, cross-modality, registration, segmentation, brain
MRI
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
Authors: Jianshu Zhang, Yixing Zhu, Jun Du, Li-Rong Dai
Date: 2018-01-22 00:00:00
Tasks: Decoder

Recently, great progress has been made for online handwritten Chinese
character recognition due to the emergence of deep learning techniques.
However, previous research mostly treated each Chinese character as one class
without explicitly considering its inherent structure, namely the radical
components with complicated geometry. In this study, we propose a novel
trajectory-based radical analysis network (TRAN) to firstly identify radicals
and analyze two-dimensional structures among radicals simultaneously, then
recognize Chinese characters by generating captions of them based on the
analysis of their internal radicals. The proposed TRAN employs recurrent neural
networks (RNNs) as both an encoder and a decoder. The RNN encoder makes full
use of online information by directly transforming handwriting trajectory into
high-level features. The RNN decoder aims at generating the caption by
detecting radicals and spatial structures through an attention model. The
manner of treating a Chinese character as a two-dimensional composition of
radicals can reduce the size of vocabulary and enable TRAN to possess the
capability of recognizing unseen Chinese character classes, only if the
corresponding radicals have been seen. Evaluated on CASIA-OLHWDB database, the
proposed approach significantly outperforms the state-of-the-art
whole-character modeling approach with a relative character error rate (CER)
reduction of 10%. Meanwhile, for the case of recognition of 500 unseen Chinese
characters, TRAN can achieve a character accuracy of about 60% while the
traditional whole-character method has no capability to handle them.
A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations
Authors: Dong Liu, Chenyang Yang
Date: 2018-01-22 00:00:00

Recommendation system is able to shape user demands, which can be used for
boosting caching gain. In this paper, we jointly optimize content caching and
recommendation at base stations to maximize the caching gain meanwhile not
compromising the user preference. We first propose a model to capture the
impact of recommendation on user demands, which is controlled by a
user-specific psychological threshold. We then formulate a joint caching and
recommendation problem maximizing the successful offloading probability, which
is a mixed integer programming problem. We develop a hierarchical iterative
algorithm to solve the problem when the threshold is known. Since the user
threshold is unknown in practice, we proceed to propose an $\varepsilon$-greedy
algorithm to find the solution by learning the threshold via interactions with
users. Simulation results show that the proposed algorithms improve the
successful offloading probability compared with prior works with/without
recommendation. The $\varepsilon$-greedy algorithm learns the user threshold
quickly, and achieves more than $1-\varepsilon$ of the performance obtained by
the algorithm with known threshold.
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice
Authors: Girish Joshi, Girish Chowdhary
Date: 2018-01-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In this paper, we present a new approach to Transfer Learning (TL) in
Reinforcement Learning (RL) for cross-domain tasks. Many of the available
techniques approach the transfer architecture as a method of speeding up the
target task learning. We propose to adapt and reuse the mapped source task
optimal-policy directly in related domains. We show the optimal policy from a
related source task can be near optimal in target domain provided an adaptive
policy accounts for the model error between target and source. The main benefit
of this policy augmentation is generalizing policies across multiple related
domains without having to re-learn the new tasks. Our results show that this
architecture leads to better sample efficiency in the transfer, reducing sample
complexity of target task learning to target apprentice learning.
Hierarchical Label Inference for Video Classification
Authors: Nelson Nauata, Jonathan Smith, Greg Mori
Date: 2017-06-15 00:00:00
Tasks: Classification, General Classification, Video Classification

Videos are a rich source of high-dimensional structured data, with a wide
range of interacting components at varying levels of granularity. In order to
improve understanding of unconstrained internet videos, it is important to
consider the role of labels at separate levels of abstraction. In this paper,
we consider the use of the Bidirectional Inference Neural Network (BINN) for
performing graph-based inference in label space for the task of video
classification. We take advantage of the inherent hierarchy between labels at
increasing granularity. The BINN is evaluated on the first and second release
of the YouTube-8M large scale multilabel video dataset. Our results demonstrate
the effectiveness of BINN, achieving significant improvements against baseline
models.
(Quasi)Periodicity Quantification in Video Data, Using Topology
Authors: Christopher J. Tralie, Jose A. Perea
Date: 2017-04-26 00:00:00
Tasks: Object Tracking, Time Series, Time Series Analysis

This work introduces a novel framework for quantifying the presence and
strength of recurrent dynamics in video data. Specifically, we provide
continuous measures of periodicity (perfect repetition) and quasiperiodicity
(superposition of periodic modes with non-commensurate periods), in a way which
does not require segmentation, training, object tracking or 1-dimensional
surrogate signals. Our methodology operates directly on video data. The
approach combines ideas from nonlinear time series analysis (delay embeddings)
and computational topology (persistent homology), by translating the problem of
finding recurrent dynamics in video data, into the problem of determining the
circularity or toroidality of an associated geometric space. Through extensive
testing, we show the robustness of our scores with respect to several noise
models/levels, we show that our periodicity score is superior to other methods
when compared to human-generated periodicity rankings, and furthermore, we show
that our quasiperiodicity score clearly indicates the presence of biphonation
in videos of vibrating vocal folds, which has never before been accomplished
end to end quantitatively.
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Date: 2017-12-24 00:00:00
Tasks: Causal Inference, counterfactual, Selection bias

We investigate the problem of estimating the causal effect of a treatment on
individual subjects from observational data, this is a central problem in
various application domains, including healthcare, social sciences, and online
advertising. Within the Neyman Rubin potential outcomes model, we use the
Kullback Leibler (KL) divergence between the estimated and true distributions
as a measure of accuracy of the estimate, and we define the information rate of
the Bayesian causal inference procedure as the (asymptotic equivalence class of
the) expected value of the KL divergence between the estimated and true
distributions as a function of the number of samples. Using Fano method, we
establish a fundamental limit on the information rate that can be achieved by
any Bayesian estimator, and show that this fundamental limit is independent of
the selection bias in the observational data. We characterize the Bayesian
priors on the potential (factual and counterfactual) outcomes that achieve the
optimal information rate. As a consequence, we show that a particular class of
priors that have been widely used in the causal inference literature cannot
achieve the optimal information rate. On the other hand, a broader class of
priors can achieve the optimal information rate. We go on to propose a prior
adaptation procedure (which we call the information based empirical Bayes
procedure) that optimizes the Bayesian prior by maximizing an information
theoretic criterion on the recovered causal effects rather than maximizing the
marginal likelihood of the observed (factual) data. Building on our analysis,
we construct an information optimal Bayesian causal inference algorithm.
Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling
Authors: Liang Ding, Di Chang, Russell Malmberg, Aaron Martinez, David Robinson, Matthew Wicker, Hongfei Yan, Liming Cai
Date: 2018-01-21 00:00:00

The seminal work of Chow and Liu (1968) shows that approximation of a finite
probabilistic system by Markov trees can achieve the minimum information loss
with the topology of a maximum spanning tree. Our current paper generalizes the
result to Markov networks of tree width $\leq k$, for every fixed $k\geq 2$. In
particular, we prove that approximation of a finite probabilistic system with
such Markov networks has the minimum information loss when the network topology
is achieved with a maximum spanning $k$-tree. While constructing a maximum
spanning $k$-tree is intractable for even $k=2$, we show that polynomial
algorithms can be ensured by a sufficient condition accommodated by many
meaningful applications. In particular, we prove an efficient algorithm for
learning the optimal topology of higher order correlations among random
variables that belong to an underlying linear structure.
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Authors: Yinhao Zhu, Nicholas Zabaras
Date: 2018-01-21 00:00:00
Tasks: Bayesian Inference, Decoder, Gaussian Processes, Image-to-Image Regression, regression, Uncertainty Quantification

We are interested in the development of surrogate models for uncertainty
quantification and propagation in problems governed by stochastic PDEs using a
deep convolutional encoder-decoder network in a similar fashion to approaches
considered in deep learning for image-to-image regression tasks. Since normal
neural networks are data intensive and cannot provide predictive uncertainty,
we propose a Bayesian approach to convolutional neural nets. A recently
introduced variational gradient descent algorithm based on Stein's method is
scaled to deep convolutional networks to perform approximate Bayesian inference
on millions of uncertain network parameters. This approach achieves state of
the art performance in terms of predictive accuracy and uncertainty
quantification in comparison to other approaches in Bayesian neural networks as
well as techniques that include Gaussian processes and ensemble methods even
when the training data size is relatively small. To evaluate the performance of
this approach, we consider standard uncertainty quantification benchmark
problems including flow in heterogeneous media defined in terms of limited
data-driven permeability realizations. The performance of the surrogate model
developed is very good even though there is no underlying structure shared
between the input (permeability) and output (flow/pressure) fields as is often
the case in the image-to-image regression models used in computer vision
problems. Studies are performed with an underlying stochastic input
dimensionality up to $4,225$ where most other uncertainty quantification
methods fail. Uncertainty propagation tasks are considered and the predictive
output Bayesian statistics are compared to those obtained with Monte Carlo
estimates.