pwc-archive/papers-with-abstracts huggingface dataset:
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Authors: Manu Goyal, Neil D. Reeves, Adrian K. Davison, Satyan Rajbhandari, Jennifer Spragg, Moi Hoon Yap
Date: 2017-11-28 00:00:00
Tasks: Classification, General Classification

Globally, in 2016, one out of eleven adults suffered from Diabetes Mellitus.
Diabetic Foot Ulcers (DFU) are a major complication of this disease, which if
not managed properly can lead to amputation. Current clinical approaches to DFU
treatment rely on patient and clinician vigilance, which has significant
limitations such as the high cost involved in the diagnosis, treatment and
lengthy care of the DFU. We collected an extensive dataset of foot images,
which contain DFU from different patients. In this paper, we have proposed the
use of traditional computer vision features for detecting foot ulcers among
diabetic patients, which represent a cost-effective, remote and convenient
healthcare solution. Furthermore, we used Convolutional Neural Networks (CNNs)
for the first time in DFU classification. We have proposed a novel
convolutional neural network architecture, DFUNet, with better feature
extraction to identify the feature differences between healthy skin and the
DFU. Using 10-fold cross-validation, DFUNet achieved an AUC score of 0.962.
This outperformed both the machine learning and deep learning classifiers we
have tested. Here we present the development of a novel and highly sensitive
DFUNet for objectively detecting the presence of DFUs. This novel approach has
the potential to deliver a paradigm shift in diabetic foot care.
Inducing Interpretability in Knowledge Graph Embeddings
Authors: Chandrahas, Tathagata Sengupta, Cibi Pragadeesh, Partha Pratim Talukdar
Date: 2017-12-10 00:00:00
Proceeding: ICON 2020 12
Tasks: Knowledge Graph Embeddings

We study the problem of inducing interpretability in KG embeddings.
Specifically, we explore the Universal Schema (Riedel et al., 2013) and propose
a method to induce interpretability. There have been many vector space models
proposed for the problem, however, most of these methods don't address the
interpretability (semantics) of individual dimensions. In this work, we study
this problem and propose a method for inducing interpretability in KG
embeddings using entity co-occurrence statistics. The proposed method
significantly improves the interpretability, while maintaining comparable
performance in other KG tasks.
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification
Authors: Abien Fred Agarap
Date: 2017-12-10 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional neural networks (CNNs) are similar to "ordinary" neural
networks in the sense that they are made up of hidden layers consisting of
neurons with "learnable" parameters. These neurons receive inputs, performs a
dot product, and then follows it with a non-linearity. The whole network
expresses the mapping between raw image pixels and their class scores.
Conventionally, the Softmax function is the classifier used at the last layer
of this network. However, there have been studies (Alalshekmubarak and Smith,
2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited
studies introduce the usage of linear support vector machine (SVM) in an
artificial neural network architecture. This project is yet another take on the
subject, and is inspired by (Tang, 2013). Empirical data has shown that the
CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST
dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax
was able to achieve a test accuracy of ~99.23% using the same dataset. Both
models were also tested on the recently-published Fashion-MNIST dataset (Xiao,
Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image
classification dataset than MNIST (Zalandoresearch, 2017). This proved to be
the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax
reached a test accuracy of ~91.86%. The said results may be improved if data
preprocessing techniques were employed on the datasets, and if the base CNN
model was a relatively more sophisticated than the one used in this study.
Multi-Task Learning for Mental Health using Social Media Text
Authors: Adrian Benton, Margaret Mitchell, Dirk Hovy
Date: 2017-12-10 00:00:00
Tasks: Gender Prediction, Multi-Task Learning

We introduce initial groundwork for estimating suicide risk and mental health
in a deep learning framework. By modeling multiple conditions, the system
learns to make predictions about suicide risk and mental health at a low false
positive rate. Conditions are modeled as tasks in a multi-task learning (MTL)
framework, with gender prediction as an additional auxiliary task. We
demonstrate the effectiveness of multi-task learning by comparison to a
well-tuned single-task baseline with the same number of parameters. Our best
MTL model predicts potential suicide attempt, as well as the presence of
atypical mental health, with AUC > 0.8. We also find additional large
improvements using multi-task learning on mental health tasks with limited
training data.
Dynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image
Authors: Wissam J. Baddar, Geonmo Gu, Sangmin Lee, Yong Man Ro
Date: 2017-12-10 00:00:00

In this paper, we propose Dynamics Transfer GAN; a new method for generating
video sequences based on generative adversarial learning. The spatial
constructs of a generated video sequence are acquired from the target image.
The dynamics of the generated video sequence are imported from a source video
sequence, with arbitrary motion, and imposed onto the target image. To preserve
the spatial construct of the target image, the appearance of the source video
sequence is suppressed and only the dynamics are obtained before being imposed
onto the target image. That is achieved using the proposed appearance
suppressed dynamics feature. Moreover, the spatial and temporal consistencies
of the generated video sequence are verified via two discriminator networks.
One discriminator validates the fidelity of the generated frames appearance,
while the other validates the dynamic consistency of the generated video
sequence. Experiments have been conducted to verify the quality of the video
sequences generated by the proposed method. The results verified that Dynamics
Transfer GAN successfully transferred arbitrary dynamics of the source video
sequence onto a target image when generating the output video sequence. The
experimental results also showed that Dynamics Transfer GAN maintained the
spatial constructs (appearance) of the target image while generating spatially
and temporally consistent video sequences.
A General Memory-Bounded Learning Algorithm
Authors: Michal Moshkovitz, Naftali Tishby
Date: 2017-12-10 00:00:00

Designing bounded-memory algorithms is becoming increasingly important nowadays. Previous works studying bounded-memory algorithms focused on proving impossibility results, while the design of bounded-memory algorithms was left relatively unexplored. To remedy this situation, in this work we design a general bounded-memory learning algorithm, when the underlying distribution is known. The core idea of the algorithm is not to save the exact example received, but only a few important bits that give sufficient information. This algorithm applies to any hypothesis class that has an "anti-mixing" property. This paper complements previous works on unlearnability with bounded memory and provides a step towards a full characterization of bounded-memory learning.
Comparative analysis of criteria for filtering time series of word usage frequencies
Authors: Inna A. Belashova, Vladimir V. Bochkarev
Date: 2017-12-10 00:00:00
Tasks: Stochastic Optimization, Time Series, Time Series Analysis

This paper describes a method of nonlinear wavelet thresholding of time
series. The Ramachandran-Ranganathan runs test is used to assess the quality of
approximation. To minimize the objective function, it is proposed to use
genetic algorithms - one of the stochastic optimization methods. The suggested
method is tested both on the model series and on the word frequency series
using the Google Books Ngram data. It is shown that method of filtering which
uses the runs criterion shows significantly better results compared with the
standard wavelet thresholding. The method can be used when quality of filtering
is of primary importance but not the speed of calculations.
3D Facial Expression Reconstruction using Cascaded Regression
Authors: Fanzi Wu, Songnan Li, Tianhao Zhao, King Ngi Ngan, Lv Sheng
Date: 2017-12-10 00:00:00
Tasks: regression

This paper proposes a novel model fitting algorithm for 3D facial expression
reconstruction from a single image. Face expression reconstruction from a
single image is a challenging task in computer vision. Most state-of-the-art
methods fit the input image to a 3D Morphable Model (3DMM). These methods need
to solve a stochastic problem and cannot deal with expression and pose
variations. To solve this problem, we adopt a 3D face expression model and use
a combined feature which is robust to scale, rotation and different lighting
conditions. The proposed method applies a cascaded regression framework to
estimate parameters for the 3DMM. 2D landmarks are detected and used to
initialize the 3D shape and mapping matrices. In each iteration, residues
between the current 3DMM parameters and the ground truth are estimated and then
used to update the 3D shapes. The mapping matrices are also calculated based on
the updated shapes and 2D landmarks. HOG features of the local patches and
displacements between 3D landmark projections and 2D landmarks are exploited.
Compared with existing methods, the proposed method is robust to expression and
pose changes and can reconstruct higher fidelity 3D face shape.
Analyzing Approximate Value Iteration Algorithms
Authors: Arunselvan Ramaswamy, Shalabh Bhatnagar
Date: 2017-09-14 00:00:00

In this paper, we consider the stochastic iterative counterpart of the value iteration scheme wherein only noisy and possibly biased approximations of the Bellman operator are available. We call this counterpart as the approximate value iteration (AVI) scheme. Neural networks are often used as function approximators, in order to counter Bellman's curse of dimensionality. In this paper, they are used to approximate the Bellman operator. Since neural networks are typically trained using sample data, errors and biases may be introduced. The design of AVI accounts for implementations with biased approximations of the Bellman operator and sampling errors. We present verifiable sufficient conditions under which AVI is stable (almost surely bounded) and converges to a fixed point of the approximate Bellman operator. To ensure the stability of AVI, we present three different yet related sets of sufficient conditions that are based on the existence of an appropriate Lyapunov function. These Lyapunov function based conditions are easily verifiable and new to the literature. The verifiability is enhanced by the fact that a recipe for the construction of the necessary Lyapunov function is also provided. We also show that the stability analysis of AVI can be readily extended to the general case of set-valued stochastic approximations. Finally, we show that AVI can also be used in more general circumstances, i.e., for finding fixed points of contractive set-valued maps.
Improving Malware Detection Accuracy by Extracting Icon Information
Authors: Pedro Silva, Sepehr Akhavan-Masouleh, Li Li
Date: 2017-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Malware Detection

Detecting PE malware files is now commonly approached using statistical and
machine learning models. While these models commonly use features extracted
from the structure of PE files, we propose that icons from these files can also
help better predict malware. We propose an innovative machine learning approach
to extract information from icons. Our proposed approach consists of two steps:
1) extracting icon features using summary statics, histogram of gradients
(HOG), and a convolutional autoencoder, 2) clustering icons based on the
extracted icon features. Using publicly available data and by using machine
learning experiments, we show our proposed icon clusters significantly boost
the efficacy of malware prediction models. In particular, our experiments show
an average accuracy increase of 10% when icon clusters are used in the
prediction model.
Capsule Network Performance on Complex Data
Authors: Edgar Xi, Selina Bing, Yang Jin
Date: 2017-12-10 00:00:00
Tasks: Data Augmentation

In recent years, convolutional neural networks (CNN) have played an important
role in the field of deep learning. Variants of CNN's have proven to be very
successful in classification tasks across different domains. However, there are
two big drawbacks to CNN's: their failure to take into account of important
spatial hierarchies between features, and their lack of rotational invariance.
As long as certain key features of an object are present in the test data,
CNN's classify the test data as the object, disregarding features' relative
spatial orientation to each other. This causes false positives. The lack of
rotational invariance in CNN's would cause the network to incorrectly assign
the object another label, causing false negatives. To address this concern,
Hinton et al. propose a novel type of neural network using the concept of
capsules in a recent paper. With the use of dynamic routing and reconstruction
regularization, the capsule network model would be both rotation invariant and
spatially aware. The capsule network has shown its potential by achieving a
state-of-the-art result of 0.25% test error on MNIST without data augmentation
such as rotation and scaling, better than the previous baseline of 0.39%. To
further test out the application of capsule networks on data with higher
dimensionality, we attempt to find the best set of configurations that yield
the optimal test error on CIFAR10 dataset.
Geometry Guided Adversarial Facial Expression Synthesis
Authors: Lingxiao Song, Zhihe Lu, Ran He, Zhenan Sun, Tieniu Tan
Date: 2017-12-10 00:00:00
Tasks: Face Recognition, Face Transfer, Generative Adversarial Network, Texture Synthesis

Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic
and identity-preserving facial expression synthesis. We employ facial geometry
(fiducial points) as a controllable condition to guide facial texture synthesis
with specific expression. A pair of generative adversarial subnetworks are
jointly trained towards opposite tasks: expression removal and expression
synthesis. The paired networks form a mapping cycle between neutral expression
and arbitrary expressions, which also facilitate other applications such as
face transfer and expression invariant face recognition. Experimental results
show that our method can generate compelling perceptual results on various
facial expression synthesis databases. An expression invariant face recognition
experiment is also performed to further show the advantages of our proposed
method.
SPP-Net: Deep Absolute Pose Regression with Synthetic Views
Authors: Pulak Purkait, Cheng Zhao, Christopher Zach
Date: 2017-12-09 00:00:00
Tasks: Image-Based Localization, Pose Estimation, regression

Image based localization is one of the important problems in computer vision
due to its wide applicability in robotics, augmented reality, and autonomous
systems. There is a rich set of methods described in the literature how to
geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based
on deep (and convolutional) feedforward networks (CNNs) became popular for pose
regression. However, these CNN-based methods are still less accurate than
geometry based methods despite being fast and memory efficient. In this work we
design a deep neural network architecture based on sparse feature descriptors
to estimate the absolute pose of an image. Our choice of using sparse feature
descriptors has two major advantages: first, our network is significantly
smaller than the CNNs proposed in the literature for this task---thereby making
our approach more efficient and scalable. Second---and more importantly---,
usage of sparse features allows to augment the training data with synthetic
viewpoints, which leads to substantial improvements in the generalization
performance to unseen poses. Thus, our proposed method aims to combine the best
of the two worlds---feature-based localization and CNN-based pose
regression--to achieve state-of-the-art performance in the absolute pose
estimation. A detailed analysis of the proposed architecture and a rigorous
evaluation on the existing datasets are provided to support our method.
Modulating and attending the source image during encoding improves Multimodal Translation
Authors: Jean-Benoit Delbrouck, Stéphane Dupont
Date: 2017-12-09 00:00:00
Tasks: Translation

We propose a new and fully end-to-end approach for multimodal translation
where the source text encoder modulates the entire visual input processing
using conditional batch normalization, in order to compute the most informative
image features for our task. Additionally, we propose a new attention mechanism
derived from this original idea, where the attention model for the visual input
is conditioned on the source text encoder representations. In the paper, we
detail our models as well as the image analysis pipeline. Finally, we report
experimental results. They are, as far as we know, the new state of the art on
three different test sets.
Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews
Authors: Sharmistha Dey
Date: 2017-12-09 00:00:00
Tasks: Aspect Extraction, General Classification, Sentiment Analysis, Sentiment Classification

Understanding of customer sentiment can be useful for product development. On
top of that if the priorities for the development order can be known, then
development procedure become simpler. This work has tried to address this issue
in the mobile app domain. Along with aspect and opinion extraction this work
has also categorized the extracted aspects ac-cording to their importance. This
can help developers to focus their time and energy at the right place.
Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent
Authors: Matteo Pirotta, Marcello Restelli
Date: 2017-12-09 00:00:00
Tasks: General Classification

In this paper, we propose a novel approach to automatically determine the
batch size in stochastic gradient descent methods. The choice of the batch size
induces a trade-off between the accuracy of the gradient estimate and the cost
in terms of samples of each update. We propose to determine the batch size by
optimizing the ratio between a lower bound to a linear or quadratic Taylor
approximation of the expected improvement and the number of samples used to
estimate the gradient. The performance of the proposed approach is empirically
compared with related methods on popular classification tasks.
  The work was presented at the NIPS workshop on Optimizing the Optimizers.
Barcelona, Spain, 2016.
BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
Authors: Shanxin Yuan, Qi Ye, Bjorn Stenger, Siddhant Jain, Tae-Kyun Kim
Date: 2017-04-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Art Analysis, Hand Pose Estimation, Pose Estimation

In this paper we introduce a large-scale hand pose dataset, collected using a
novel capture method. Existing datasets are either generated synthetically or
captured using depth sensors: synthetic datasets exhibit a certain level of
appearance difference from real depth images, and real datasets are limited in
quantity and coverage, mainly due to the difficulty to annotate them. We
propose a tracking system with six 6D magnetic sensors and inverse kinematics
to automatically obtain 21-joints hand pose annotations of depth maps captured
with minimal restriction on the range of motion. The capture protocol aims to
fully cover the natural hand pose space. As shown in embedding plots, the new
dataset exhibits a significantly wider and denser range of hand poses compared
to existing benchmarks. Current state-of-the-art methods are evaluated on the
dataset, and we demonstrate significant improvements in cross-benchmark
performance. We also show significant improvements in egocentric hand pose
estimation with a CNN trained on the new dataset.
EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
Authors: Maxim Grechkin, Hoifung Poon, Bill Howe
Date: 2017-09-25 00:00:00

Many real-world applications require automated data annotation, such as
identifying tissue origins based on gene expressions and classifying images
into semantic categories. Annotation classes are often numerous and subject to
changes over time, and annotating examples has become the major bottleneck for
supervised learning methods. In science and other high-value domains, large
repositories of data samples are often available, together with two sources of
organic supervision: a lexicon for the annotation classes, and text
descriptions that accompany some data samples. Distant supervision has emerged
as a promising paradigm for exploiting such indirect supervision by
automatically annotating examples where the text description contains a class
mention in the lexicon. However, due to linguistic variations and ambiguities,
such training data is inherently noisy, which limits the accuracy of this
approach. In this paper, we introduce an auxiliary natural language processing
system for the text modality, and incorporate co-training to reduce noise and
augment signal in distant supervision. Without using any manually labeled data,
our EZLearn system learned to accurately annotate data samples in functional
genomics and scientific figure comprehension, substantially outperforming
state-of-the-art supervised methods trained on tens of thousands of annotated
examples.
Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
Authors: Federico Baldassarre, Diego González Morín, Lucas Rodés-Guirao
Date: 2017-12-09 00:00:00
Tasks: Colorization, Decoder, Image Colorization

We review some of the most recent approaches to colorize gray-scale images
using deep learning methods. Inspired by these, we propose a model which
combines a deep Convolutional Neural Network trained from scratch with
high-level features extracted from the Inception-ResNet-v2 pre-trained model.
Thanks to its fully convolutional architecture, our encoder-decoder model can
process images of any size and aspect ratio. Other than presenting the training
results, we assess the "public acceptance" of the generated images by means of
a user study. Finally, we present a carousel of applications on different types
of images, such as historical photographs.