pwc-archive/papers-with-abstracts huggingface dataset:
syGlass: Interactive Exploration of Multidimensional Images Using Virtual Reality Head-mounted Displays
Authors: Stanislav Pidhorskyi, Michael Morehead, Quinn Jones, George Spirou, Gianfranco Doretto
Date: 2018-04-23 00:00:00

The quest for deeper understanding of biological systems has driven the
acquisition of increasingly larger multidimensional image datasets. Inspecting
and manipulating data of this complexity is very challenging in traditional
visualization systems. We developed syGlass, a software package capable of
visualizing large scale volumetric data with inexpensive virtual reality
head-mounted display technology. This allows leveraging stereoscopic vision to
significantly improve perception of complex 3D structures, and provides
immersive interaction with data directly in 3D. We accomplished this by
developing highly optimized data flow and volume rendering pipelines, tested on
datasets up to 16TB in size, as well as tools available in a virtual reality
GUI to support advanced data exploration, annotation, and cataloguing.
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Authors: Jian Wu, Peter I. Frazier
Date: 2016-06-14 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Optimization

In many applications of black-box optimization, one can evaluate multiple
points simultaneously, e.g. when evaluating the performances of several
different neural network architectures in a parallel computing environment. In
this paper, we develop a novel batch Bayesian optimization algorithm --- the
parallel knowledge gradient method. By construction, this method provides the
one-step Bayes-optimal batch of points to sample. We provide an efficient
strategy for computing this Bayes-optimal batch of points, and we demonstrate
that the parallel knowledge gradient method finds global optima significantly
faster than previous batch Bayesian optimization algorithms on both synthetic
test functions and when tuning hyperparameters of practical machine learning
algorithms, especially when function evaluations are noisy.
Advancing Tabu and Restart in Local Search for Maximum Weight Cliques
Authors: Yi Fan, Nan Li, Chengqian Li, Zongjie Ma, Longin Jan Latecki, Kaile Su
Date: 2018-04-22 00:00:00

The tabu and restart are two fundamental strategies for local search. In this
paper, we improve the local search algorithms for solving the Maximum Weight
Clique (MWC) problem by introducing new tabu and restart strategies. Both the
tabu and restart strategies proposed are based on the notion of a local search
scenario, which involves not only a candidate solution but also the tabu status
and unlocking relationship. Compared to the strategy of configuration checking,
our tabu mechanism discourages forming a cycle of unlocking operations. Our new
restart strategy is based on the re-occurrence of a local search scenario
instead of that of a candidate solution. Experimental results show that the
resulting MWC solver outperforms several state-of-the-art solvers on the
DIMACS, BHOSLIB, and two benchmarks from practical applications.
Automatic Language Identification in Texts: A Survey
Authors: Tommi Jauhiainen, Marco Lui, Marcos Zampieri, Timothy Baldwin, Krister Lindén
Date: 2018-04-22 00:00:00
Tasks: Language Identification, Survey

Language identification (LI) is the problem of determining the natural
language that a document or part thereof is written in. Automatic LI has been
extensively researched for over fifty years. Today, LI is a key part of many
text processing pipelines, as text processing techniques generally assume that
the language of the input text is known. Research in this area has recently
been especially active. This article provides a brief history of LI research,
and an extensive survey of the features and methods used so far in the LI
literature. For describing the features and methods we introduce a unified
notation. We discuss evaluation methods, applications of LI, as well as
off-the-shelf LI systems that do not require training by the end user. Finally,
we identify open issues, survey the work to date on each issue, and propose
future directions for research in LI.
Uncharted Forest a Technique for Exploratory Data Analysis
Authors: Casey Kneale, Steven D. Brown
Date: 2018-02-11 00:00:00
Tasks: Clustering, General Classification

Exploratory data analysis is crucial for developing and understanding
classification models from high-dimensional datasets. We explore the utility of
a new unsupervised tree ensemble called uncharted forest for visualizing class
associations, sample-sample associations, class heterogeneity, and
uninformative classes for provenance studies. The uncharted forest algorithm
can be used to partition data using random selections of variables and metrics
based on statistical spread. After each tree is grown, a tally of the samples
that arrive at every terminal node is maintained. Those tallies are stored in
single sample association matrix and a likelihood measure for each sample being
partitioned with one another can be made. That matrix may be readily viewed as
a heat map, and the probabilities can be quantified via new metrics that
account for class or cluster membership. We display the advantages and
limitations of using this technique by applying it to two classification
datasets and three provenance study datasets. Two of the metrics presented in
this paper are also compared with widely used metrics from two algorithms that
have variance-based clustering mechanisms.
Interleaver Design for Deep Neural Networks
Authors: Sourya Dey, Peter A. Beerel, Keith M. Chugg
Date: 2017-11-18 00:00:00
Tasks: Mathematical Proofs

We propose a class of interleavers for a novel deep neural network (DNN)
architecture that uses algorithmically pre-determined, structured sparsity to
significantly lower memory and computational requirements, and speed up
training. The interleavers guarantee clash-free memory accesses to eliminate
idle operational cycles, optimize spread and dispersion to improve network
performance, and are designed to ease the complexity of memory address
computations in hardware. We present a design algorithm with mathematical
proofs for these properties. We also explore interleaver variations and analyze
the behavior of neural networks as a function of interleaver metrics.
Large Receptive Field Networks for High-Scale Image Super-Resolution
Authors: George Seif, Dimitrios Androutsos
Date: 2018-04-22 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution, Vocal Bursts Intensity Prediction

Convolutional Neural Networks have been the backbone of recent rapid progress
in Single-Image Super-Resolution. However, existing networks are very deep with
many network parameters, thus having a large memory footprint and being
challenging to train. We propose Large Receptive Field Networks which strive to
directly expand the receptive field of Super-Resolution networks without
increasing depth or parameter count. In particular, we use two different
methods to expand the network receptive field: 1-D separable kernels and atrous
convolutions. We conduct considerable experiments to study the performance of
various arrangement schemes of the 1-D separable kernels and atrous convolution
in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing
on the more challenging high upscaling factors. Extensive benchmark evaluations
demonstrate the effectiveness of our approach.
RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud
Authors: Jianfeng Chen, Tim Menzies
Date: 2017-08-27 00:00:00
Tasks: Cloud Computing, Scheduling

Cloud computing provides engineers or scientists a place to run complex
computing tasks. Finding a workflow's deployment configuration in a cloud
environment is not easy. Traditional workflow scheduling algorithms were based
on some heuristics, e.g. reliability greedy, cost greedy, cost-time balancing,
etc., or more recently, the meta-heuristic methods, such as genetic algorithms.
These methods are very slow and not suitable for rescheduling in the dynamic
cloud environment. This paper introduces RIOT (Randomized Instance Order
Types), a stochastic based method for workflow scheduling. RIOT groups the
tasks in the workflow into virtual machines via a probability model and then
uses an effective surrogate-based method to assess a large amount of potential
scheduling. Experiments in dozens of study cases showed that RIOT executes tens
of times faster than traditional methods while generating comparable results to
other methods.
A Deep Convolutional Neural Network for Lung Cancer Diagnostic
Authors: Mehdi Fatan Serj, Bahram Lavi, Gabriela Hoff, Domenec Puig Valls
Date: 2018-04-22 00:00:00
Tasks: Binary Classification, Classification, Deep Learning, Diagnostic, General Classification, Medical Image Analysis

In this paper, we examine the strength of deep learning technique for
diagnosing lung cancer on medical image analysis problem. Convolutional neural
networks (CNNs) models become popular among the pattern recognition and
computer vision research area because of their promising outcome on generating
high-level image representations. We propose a new deep learning architecture
for learning high-level image representation to achieve high classification
accuracy with low variance in medical image binary classification tasks. We aim
to learn discriminant compact features at beginning of our deep convolutional
neural network. We evaluate our model on Kaggle Data Science Bowl 2017 (KDSB17)
data set, and compare it with some related works proposed in the Kaggle
competition.
Non-Adversarial Unsupervised Word Translation
Authors: Yedid Hoshen, Lior Wolf
Date: 2018-01-18 00:00:00
Proceeding: EMNLP 2018 10
Tasks: CPU, Translation, Word Translation

Unsupervised word translation from non-parallel inter-lingual corpora has
attracted much research interest. Very recently, neural network methods trained
with adversarial loss functions achieved high accuracy on this task. Despite
the impressive success of the recent techniques, they suffer from the typical
drawbacks of generative adversarial models: sensitivity to hyper-parameters,
long training time and lack of interpretability. In this paper, we make the
observation that two sufficiently similar distributions can be aligned
correctly with iterative matching methods. We present a novel method that first
aligns the second moment of the word distributions of the two languages and
then iteratively refines the alignment. Extensive experiments on word
translation of European and Non-European languages show that our method
achieves better performance than recent state-of-the-art deep adversarial
approaches and is competitive with the supervised baseline. It is also
efficient, easy to parallelize on CPU and interpretable.
Word Embedding Perturbation for Sentence Classification
Authors: Dongxu Zhang, Zhichao Yang
Date: 2018-04-22 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Sentence, Sentence Classification

In this technique report, we aim to mitigate the overfitting problem of
natural language by applying data augmentation methods. Specifically, we
attempt several types of noise to perturb the input word embedding, such as
Gaussian noise, Bernoulli noise, and adversarial noise, etc. We also apply
several constraints on different types of noise. By implementing these proposed
data augmentation methods, the baseline models can gain improvements on several
sentence classification tasks.
MoDL: Model Based Deep Learning Architecture for Inverse Problems
Authors: Hemant Kumar Aggarwal, Merry P. Mani, Mathews Jacob
Date: 2017-12-07 00:00:00
Tasks: Deep Learning, GPU, Image Reconstruction

We introduce a model-based image reconstruction framework with a convolution neural network (CNN) based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to black-box deep learning approaches, thus reducing the demand for training data and training time. Since we rely on end-to-end training, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. The main difference of the framework from existing end-to-end training strategies is the sharing of the network weights across iterations and channels. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks such as conjugate gradients algorithm within the network; this approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, especially when the available GPU memory restricts the number of iterations.
NE-Table: A Neural key-value table for Named Entities
Authors: Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, Lazaros Polymenakos
Date: 2018-04-22 00:00:00
Proceeding: RANLP 2019 9
Tasks: Goal-Oriented Dialog, Question Answering, Reading Comprehension, Word Embeddings

Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set available at - https://github.com/IBM/ne-table-datasets.
Explanations of model predictions with live and breakDown packages
Authors: Mateusz Staniak, Przemyslaw Biecek
Date: 2018-04-05 00:00:00
Tasks: Attribute

Complex models are commonly used in predictive modeling. In this paper we
present R packages that can be used to explain predictions from complex black
box models and attribute parts of these predictions to input features. We
introduce two new approaches and corresponding packages for such attribution,
namely live and breakDown. We also compare their results with existing
implementations of state of the art solutions, namely lime that implements
Locally Interpretable Model-agnostic Explanations and ShapleyR that implements
Shapley values.
I Know How You Feel: Emotion Recognition with Facial Landmarks
Authors: Ivona Tautkute, Tomasz Trzcinski, Adam Bielski
Date: 2018-04-22 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition, General Classification, Landmark Recognition

Classification of human emotions remains an important and challenging task
for many computer vision algorithms, especially in the era of humanoid robots
which coexist with humans in their everyday life. Currently proposed methods
for emotion recognition solve this task using multi-layered convolutional
networks that do not explicitly infer any facial features in the classification
phase. In this work, we postulate a fundamentally different approach to solve
emotion recognition task that relies on incorporating facial landmarks as a
part of the classification loss function. To that end, we extend a recently
proposed Deep Alignment Network (DAN), that achieves state-of-the-art results
in the recent facial landmark recognition challenge, with a term related to
facial features. Thanks to this simple modification, our model called
EmotionalDAN is able to outperform state-of-the-art emotion classification
methods on two challenging benchmark dataset by up to 5%.
Local White Matter Architecture Defines Functional Brain Dynamics
Authors: Yo Joong Choe, Sivaraman Balakrishnan, Aarti Singh, Jean M. Vettel, Timothy Verstynen
Date: 2018-04-22 00:00:00
Tasks: Functional Connectivity, Variable Selection

Large bundles of myelinated axons, called white matter, anatomically connect
disparate brain regions together and compose the structural core of the human
connectome. We recently proposed a method of measuring the local integrity
along the length of each white matter fascicle, termed the local connectome. If
communication efficiency is fundamentally constrained by the integrity along
the entire length of a white matter bundle, then variability in the functional
dynamics of brain networks should be associated with variability in the local
connectome. We test this prediction using two statistical approaches that are
capable of handling the high dimensionality of data. First, by performing
statistical inference on distance-based correlations, we show that similarity
in the local connectome between individuals is significantly correlated with
similarity in their patterns of functional connectivity. Second, by employing
variable selection using sparse canonical correlation analysis and
cross-validation, we show that segments of the local connectome are predictive
of certain patterns of functional brain dynamics. These results are consistent
with the hypothesis that structural variability along axon bundles constrains
communication between disparate brain regions.
The geometry of learning
Authors: Gianluca Calcagni
Date: 2016-05-02 00:00:00

We establish a correspondence between Pavlovian conditioning processes and
fractals. The association strength at a training trial corresponds to a point
in a disconnected set at a given iteration level. In this way, one can
represent a training process as a hopping on a fractal set, instead of the
traditional learning curve as a function of the trial. The main advantage of
this novel perspective is to provide an elegant classification of associative
theories in terms of the geometric features of fractal sets. In particular, the
dimension of fractals can measure the efficiency of conditioning models. We
illustrate the correspondence with the examples of the Hull, Rescorla-Wagner,
and Mackintosh models and show that they are equivalent to a Cantor set. More
generally, conditioning programs are described by the geometry of their
associated fractal, which gives much more information than just its dimension.
We show this in several examples of random fractals and also comment on a
possible relation between our formalism and other "fractal" findings in the
cognitive literature.
Discovering and Deciphering Relationships Across Disparate Data Modalities
Authors: Joshua T. Vogelstein, Eric Bridgeford, Qing Wang, Carey E. Priebe, Mauro Maggioni, Cencheng Shen
Date: 2016-09-16 00:00:00
Tasks: Computational Efficiency

Understanding the relationships between different properties of data, such as
whether a connectome or genome has information about disease status, is
becoming increasingly important in modern biological datasets. While existing
approaches can test whether two properties are related, they often require
unfeasibly large sample sizes in real data scenarios, and do not provide any
insight into how or why the procedure reached its decision. Our approach,
"Multiscale Graph Correlation" (MGC), is a dependence test that juxtaposes
previously disparate data science techniques, including k-nearest neighbors,
kernel methods (such as support vector machines), and multiscale analysis (such
as wavelets). Other methods typically require double or triple the number
samples to achieve the same statistical power as MGC in a benchmark suite
including high-dimensional and nonlinear relationships - spanning polynomial
(linear, quadratic, cubic), trigonometric (sinusoidal, circular, ellipsoidal,
spiral), geometric (square, diamond, W-shape), and other functions, with
dimensionality ranging from 1 to 1000. Moreover, MGC uniquely provides a simple
and elegant characterization of the potentially complex latent geometry
underlying the relationship, providing insight while maintaining computational
efficiency. In several real data applications, including brain imaging and
cancer genetics, MGC is the only method that can both detect the presence of a
dependency and provide specific guidance for the next experiment and/or
analysis to conduct.
Micro-Net: A unified model for segmentation of various objects in microscopy images
Authors: Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban, Simon Graham, David Epstein, Stella Pelengaris, Michael Khan, Nasir M. Rajpoot
Date: 2018-04-22 00:00:00
Tasks: Deep Learning, Multi-tissue Nucleus Segmentation, Segmentation, Semantic Segmentation

Object segmentation and structure localization are important steps in
automated image analysis pipelines for microscopy images. We present a
convolution neural network (CNN) based deep learning architecture for
segmentation of objects in microscopy images. The proposed network can be used
to segment cells, nuclei and glands in fluorescence microscopy and histology
images after slight tuning of input parameters. The network trains at multiple
resolutions of the input image, connects the intermediate layers for better
localization and context and generates the output using multi-resolution
deconvolution filters. The extra convolutional layers which bypass the
max-pooling operation allow the network to train for variable input intensities
and object size and make it robust to noisy data. We compare our results on
publicly available data sets and show that the proposed network outperforms
recent deep learning algorithms.
Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks
Authors: Renjie Zheng, Junkun Chen, Xipeng Qiu
Date: 2018-04-22 00:00:00
Tasks: General Classification, Multi-Task Learning, Representation Learning, Sentence, text-classification, Text Classification

Distributed representation plays an important role in deep learning based
natural language processing. However, the representation of a sentence often
varies in different tasks, which is usually learned from scratch and suffers
from the limited amounts of training data. In this paper, we claim that a good
sentence representation should be invariant and can benefit the various
subsequent tasks. To achieve this purpose, we propose a new scheme of
information sharing for multi-task learning. More specifically, all tasks share
the same sentence representation and each task can select the task-specific
information from the shared sentence representation with attention mechanism.
The query vector of each task's attention could be either static parameters or
generated dynamically. We conduct extensive experiments on 16 different text
classification tasks, which demonstrate the benefits of our architecture.
Complex Network Analysis of Men Single ATP Tennis Matches
Authors: Umberto Michieli
Date: 2018-04-22 00:00:00

Who are the most significant players in the history of men tennis? Is the
official ATP ranking system fair in evaluating players scores? Which players
deserved the most contemplation looking at their match records? Which players
have never faced yet and are likely to play against in the future? Those are
just some of the questions developed in this paper supported by data updated at
April 2018. In order to give an answer to the aforementioned questions, complex
network science techniques have been applied to some representations of the
network of men singles tennis matches. Additionally, a new predictive algorithm
is proposed in order to forecast the winner of a match.