pwc-archive/papers-with-abstracts huggingface dataset:
Method for Searching of an Optimal Scenario of Impact in Cognitive Maps during Information Operations Recognition
Authors: Oleh Dmytrenko, Dmitry Lande, Oleh Andriichuk
Date: 2019-04-25 00:00:00

In this paper, we consider the problem of choosing the optimal scenario of
the impact between nodes based on of the introduced criteria for the optimality
of the impact. Two criteria for the optimality of the impact, which are called
the force of impact and the speed of implementation of the scenario, are
considered. To obtain a unique solution of the problem, a multi-criterial
assessment of the received scenarios using the Pareto principle was applied.
Based on the criteria of a force of impact and the speed of implementation of
the scenario, the choice of the optimal scenario of impact was justified. The
results and advantages of the proposed approach in comparison with the Kosko
model are presented.
Survey of Dropout Methods for Deep Neural Networks
Authors: Alex Labach, Hojjat Salehinejad, Shahrokh Valaee
Date: 2019-04-25 00:00:00
Tasks: Model Compression, Survey

Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice. They have been successfully applied in neural network regularization, model compression, and in measuring the uncertainty of neural network outputs. While original formulated for dense neural network layers, recent advances have made dropout methods also applicable to convolutional and recurrent neural network layers. This paper summarizes the history of dropout methods, their various applications, and current areas of research interest. Important proposed methods are described in additional detail.
Topic Classification Method for Analyzing Effect of eWOM on Consumer Game Sales
Authors: Yoshiki Horii, Hirofumi Nonaka, Elisa Claire Alemán Carreón, Hiroki Horino, Toru Hiraoka
Date: 2019-04-23 00:00:00
Tasks: feature selection, General Classification, Marketing, Topic Classification

Electronic word-of-mouth (eWOM) has become an important resource for the
analysis of marketing research. In this study, in order to analyze user needs
for consumer game software, we focus on tweet data. And we proposed topic
extraction method using entropy-based feature selection based feature
expansion. We also applied it to the classification of the data extracted from
tweet data by using SVM. As a result, we achieved a 0.63 F-measure.
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site
Authors: Elisa Claire Alemán Carreón, Hirofumi Nonaka, Toru Hiraoka
Date: 2019-04-24 00:00:00
Tasks: General Classification, Sentiment Analysis

With an increasingly large number of Chinese tourists in Japan, the hotel
industry is in need of an affordable market research tool that does not rely on
expensive and time-consuming surveys or interviews. Because this problem is
real and relevant to the hotel industry in Japan, and otherwise completely
unexplored in other studies, we have extracted a list of potential keywords
from Chinese reviews of Japanese hotels in the hotel portal site Ctrip1 using a
mathematical model to then use them in a sentiment analysis with a machine
learning classifier. While most studies that use information collected from the
internet use pre-existing data analysis tools, in our study, we designed the
mathematical model to have the highest possible performing results in
classification, while also exploring on the potential business implications
these may have.
Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks
Authors: Kensei Nakai, Hirofumi Nonaka, Asahi Hentona, Yuki Kanai, Takeshi Sakumoto, Shotaro Kataoka, Elisa Claire Alemán Carreón, Toru Hiraoka
Date: 2019-04-23 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering, Management, Stochastic Block Model

Scoring patent documents is very useful for technology management. However,
conventional methods are based on static models and, thus, do not reflect the
growth potential of the technology cluster of the patent. Because even if the
cluster of a patent has no hope of growing, we recognize the patent is
important if PageRank or other ranking score is high. Therefore, there arises a
necessity of developing citation network clustering and prediction of future
citations. In our research, clustering of patent citation networks by
Stochastic Block Model was done with the aim of enabling corporate managers and
investors to evaluate the scale and life cycle of technology. As a result, we
confirmed nested SBM is appropriate for graph clustering of patent citation
networks. Also, a high MAPE value was obtained and the direction accuracy
achieved a value greater than 50% when predicting growth potential for each
cluster by using LSTM.
A new algorithm for shape matching and pattern recognition using dynamic programming
Authors: Noreddine Gherabi, Bahaj Mohamed
Date: 2019-04-04 00:00:00
Tasks: Retrieval

We propose a new method for shape recognition and retrieval based on dynamic
programming. Our approach uses the dynamic programming algorithm to compute the
optimal score and to find the optimal alignment between two strings. First,
each contour of shape is represented by a set of points. After alignment and
matching between two shapes, the contours are transformed into a string of
symbols and numbers. Finally we find the best alignment of two complete strings
and compute the optimal cost of similarity. In general, dynamic programming has
two phases: the forward phase and the backward phase. In the forward phase, we
compute the optimal cost for each subproblem. In the backward phase, we
reconstruct the solution that gives the optimal cost. Our algorithm is tested
in a database that contains various shapes such as MPEG-7.
Signal2Image Modules in Deep Neural Networks for EEG Classification
Authors: Paschalis Bizopoulos, George I Lambrou, Dimitrios Koutsouris
Date: 2019-04-18 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.
Convolutional Neural Networks Applied to House Numbers Digit Classification
Authors: Pierre Sermanet, Soumith Chintala, Yann Lecun
Date: 2012-04-18 00:00:00
Tasks: Classification, General Classification

We classify digits of real-world house numbers using convolutional neural
networks (ConvNets). ConvNets are hierarchical feature learning neural networks
whose structure is biologically inspired. Unlike many popular vision approaches
that are hand-designed, ConvNets can automatically learn a unique set of
features optimized for a given task. We augmented the traditional ConvNet
architecture by learning multi-stage features and by using Lp pooling and
establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2%
error improvement). Furthermore, we analyze the benefits of different pooling
methods and multi-stage features in ConvNets. The source code and a tutorial
are available at eblearn.sf.net.
Similarity of Neural Network Representations Revisited
Authors: Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton
Date: 2019-05-01 00:00:00
Proceeding: ICML 2019 2019 5

Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for comparing neural network representations based on canonical correlation analysis (CCA). We show that CCA belongs to a family of statistics for measuring multivariate similarity, but that neither CCA nor any other statistic that is invariant to invertible linear transformation can measure meaningful similarities between representations of higher dimension than the number of data points. We introduce a similarity index that measures the relationship between representational similarity matrices and does not suffer from this limitation. This similarity index is equivalent to centered kernel alignment (CKA) and is also closely connected to CCA. Unlike CCA, CKA can reliably identify correspondences between representations in networks trained from different initializations.
Pushing the Boundaries of View Extrapolation with Multiplane Images
Authors: Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely
Date: 2019-05-01 00:00:00
Proceeding: CVPR 2019 6

We explore the problem of view synthesis from a narrow baseline pair of
images, and focus on generating high-quality view extrapolations with plausible
disocclusions. Our method builds upon prior work in predicting a multiplane
image (MPI), which represents scene content as a set of RGB$\alpha$ planes
within a reference view frustum and renders novel views by projecting this
content into the target viewpoints. We present a theoretical analysis showing
how the range of views that can be rendered from an MPI increases linearly with
the MPI disparity sampling frequency, as well as a novel MPI prediction
procedure that theoretically enables view extrapolations of up to $4\times$ the
lateral viewpoint movement allowed by prior work. Our method ameliorates two
specific issues that limit the range of views renderable by prior methods: 1)
We expand the range of novel views that can be rendered without depth
discretization artifacts by using a 3D convolutional network architecture along
with a randomized-resolution training procedure to allow our model to predict
MPIs with increased disparity sampling frequency. 2) We reduce the repeated
texture artifacts seen in disocclusions by enforcing a constraint that the
appearance of hidden content at any depth must be drawn from visible content at
or behind that depth. Please see our results video at:
https://www.youtube.com/watch?v=aJqAaMNL2m4.
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
Authors: Xi Xiong, Kaan Ozbay, Li Jin, Chen Feng
Date: 2019-05-01 00:00:00
Tasks: Prediction

Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have to be inferred from aggregate traffic flow data on traffic links. Specifically, spatial correlation, congestion and time dependent factors need to be considered in general transportation networks. In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously. The underlying road network topology is converted into a corresponding line graph in the newly designed Fusion Line Graph Convolutional Networks (FL-GCNs), which provide a general framework of predicting spatial-temporal O-D flows from link information. Data from New Jersey Turnpike network are used to evaluate the proposed model. The results show that our proposed approach yields the best performance under various prediction scenarios. In addition, the advantage of combining deep neural networks and Kalman filter is demonstrated.
Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation
Authors: Matan Goldman, Tal Hassner, Shai Avidan
Date: 2019-05-01 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

The field of self-supervised monocular depth estimation has seen huge
advancements in recent years. Most methods assume stereo data is available
during training but usually under-utilize it and only treat it as a reference
signal. We propose a novel self-supervised approach which uses both left and
right images equally during training, but can still be used with a single input
image at test time, for monocular depth estimation. Our Siamese network
architecture consists of two, twin networks, each learns to predict a disparity
map from a single image. At test time, however, only one of these networks is
used in order to infer depth. We show state-of-the-art results on the standard
KITTI Eigen split benchmark as well as being the highest scoring
self-supervised method on the new KITTI single view benchmark. To demonstrate
the ability of our method to generalize to new data sets, we further provide
results on the Make3D benchmark, which was not used during training.
Fast AutoAugment
Authors: Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
Date: 2019-05-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Data Augmentation, GPU, Image Augmentation, Image Classification

Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically search for augmentation policies from a dataset and has significantly enhanced performances on many image recognition tasks. However, its search method requires thousands of GPU hours even for a relatively small dataset. In this paper, we propose an algorithm called Fast AutoAugment that finds effective augmentation policies via a more efficient search strategy based on density matching. In comparison to AutoAugment, the proposed algorithm speeds up the search time by orders of magnitude while achieves comparable performances on image recognition tasks with various models and datasets including CIFAR-10, CIFAR-100, SVHN, and ImageNet.
Sex-Prediction from Periocular Images across Multiple Sensors and Spectra
Authors: Juan Tapia, Christian Rathgeb, Christoph Busch
Date: 2019-05-01 00:00:00
Tasks: Classification, Gender Classification, General Classification, Prediction

In this paper, we provide a comprehensive analysis of periocular-based
sex-prediction (commonly referred to as gender classification) using
state-of-the-art machine learning techniques. In order to reflect a more
challenging scenario where periocular images are likely to be obtained from an
unknown source, i.e. sensor, convolutional neural networks are trained on fused
sets composed of several near-infrared (NIR) and visible wavelength (VW) image
databases. In a cross-sensor scenario within each spectrum an average
classification accuracy of approximately 85\% is achieved. When sex-prediction
is performed across spectra an average classification accuracy of about 82\% is
obtained. Finally, a multi-spectral sex-prediction yields a classification
accuracy of 83\% on average. Compared to proposed works, obtained results
provide a more realistic estimation of the feasibility to predict a subject's
sex from the periocular region.
Estimation of Tissue Oxygen Saturation from RGB images and Sparse Hyperspectral Signals based on Conditional Generative Adversarial Network
Authors: Qing-Biao Li, Jianyu Lin, Neil T. Clancy, Daniel S. Elson
Date: 2019-05-01 00:00:00
Tasks: Generative Adversarial Network

Purpose: Intra-operative measurement of tissue oxygen saturation (StO2) is important in the detection of ischemia, monitoring perfusion and identifying disease. Hyperspectral imaging (HSI) measures the optical reflectance spectrum of the tissue and uses this information to quantify its composition, including StO2. However, real-time monitoring is difficult due to the capture rate and data processing time. Methods: An endoscopic system based on a multi-fiber probe was previously developed to sparsely capture HSI data (sHSI). These were combined with RGB images, via a deep neural network, to generate high-resolution hypercubes and calculate StO2. To improve accuracy and processing speed, we propose a dual-input conditional generative adversarial network (cGAN), Dual2StO2, to directly estimate StO2 by fusing features from both RGB and sHSI. Results: Validation experiments were carried out on in vivo porcine bowel data, where the ground truth StO2 was generated from the HSI camera. The performance was also compared to our previous super-spectral-resolution network, SSRNet in terms of mean StO2 prediction accuracy and structural similarity metrics. Dual2StO2 was also tested using simulated probe data with varying fiber number. Conclusions: StO2 estimation by Dual2StO2 is visually closer to ground truth in general structure, achieves higher prediction accuracy and faster processing speed than SSRNet. Simulations showed that results improved when a greater number of fibers are used in the probe. Future work will include refinement of the network architecture, hardware optimization based on simulation results, and evaluation of the technique in clinical applications beyond StO2 estimation.
Gradient-free activation maximization for identifying effective stimuli
Authors: Will Xiao, Gabriel Kreiman
Date: 2019-05-01 00:00:00

A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
  We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.
Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
Authors: Juan Tapia, Claudia Arellano
Date: 2019-05-01 00:00:00
Tasks: Classification, Gender Classification, General Classification, Iris Recognition, Iris Segmentation

Soft biometric information such as gender can contribute to many applications
like as identification and security. This paper explores the use of a Binary
Statistical Features (BSIF) algorithm for classifying gender from iris texture
images captured with NIR sensors. It uses the same pipeline for iris
recognition systems consisting of iris segmentation, normalisation and then
classification. Experiments show that applying BSIF is not straightforward
since it can create artificial textures causing misclassification. In order to
overcome this limitation, a new set of filters was trained from eye images and
different sized filters with padding bands were tested on a subject-disjoint
database. A Modified-BSIF (MBSIF) method was implemented. The latter achieved
better gender classification results (94.6\% and 91.33\% for the left and right
eye respectively). These results are competitive with the state of the art in
gender classification. In an additional contribution, a novel gender labelled
database was created and it will be available upon request.
Quantum Generalized Linear Models
Authors: Colleen M. Farrelly, Srikanth Namuduri, Uchenna Chukwu
Date: 2019-05-01 00:00:00

Generalized linear models (GLM) are link function based statistical models.
Many supervised learning algorithms are extensions of GLMs and have link
functions built into the algorithm to model different outcome distributions.
There are two major drawbacks when using this approach in applications using
real world datasets. One is that none of the link functions available in the
popular packages is a good fit for the data. Second, it is computationally
inefficient and impractical to test all the possible distributions to find the
optimum one. In addition, many GLMs and their machine learning extensions
struggle on problems of overdispersion in Tweedie distributions. In this paper
we propose a quantum extension to GLM that overcomes these drawbacks. A quantum
gate with non-Gaussian transformation can be used to continuously deform the
outcome distribution from known results. In doing so, we eliminate the need for
a link function. Further, by using an algorithm that superposes all possible
distributions to collapse to fit a dataset, we optimize the model in a
computationally efficient way. We provide an initial proof-of-concept by
testing this approach on both a simulation of overdispersed data and then on a
benchmark dataset, which is quite overdispersed, and achieved state of the art
results. This is a game changer in several applied fields, such as part failure
modeling, medical research, actuarial science, finance and many other fields
where Tweedie regression and overdispersion are ubiquitous.
Information-Theoretic Considerations in Batch Reinforcement Learning
Authors: Jinglin Chen, Nan Jiang
Date: 2019-05-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Value-function approximation methods that operate in batch mode have
foundational importance to reinforcement learning (RL). Finite sample
guarantees for these methods often crucially rely on two types of assumptions:
(1) mild distribution shift, and (2) representation conditions that are
stronger than realizability. However, the necessity ("why do we need them?")
and the naturalness ("when do they hold?") of such assumptions have largely
eluded the literature. In this paper, we revisit these assumptions and provide
theoretical results towards answering the above questions, and make steps
towards a deeper understanding of value-function approximation.