pwc-archive/papers-with-abstracts huggingface dataset:
Sliced Wasserstein Kernel for Persistence Diagrams
Authors: Mathieu Carrière, Marco Cuturi, Steve Oudot
Date: 2017-06-11 00:00:00
Proceeding: ICML 2017 8
Tasks: Graph Classification, Topological Data Analysis

Persistence diagrams (PDs) play a key role in topological data analysis
(TDA), in which they are routinely used to describe topological properties of
complicated shapes. PDs enjoy strong stability properties and have proven their
utility in various learning contexts. They do not, however, live in a space
naturally endowed with a Hilbert structure and are usually compared with
specific distances, such as the bottleneck distance. To incorporate PDs in a
learning pipeline, several kernels have been proposed for PDs with a strong
emphasis on the stability of the RKHS distance w.r.t. perturbations of the PDs.
In this article, we use the Sliced Wasserstein approximation SW of the
Wasserstein distance to define a new kernel for PDs, which is not only provably
stable but also provably discriminative (depending on the number of points in
the PDs) w.r.t. the Wasserstein distance $d_1$ between PDs. We also demonstrate
its practicality, by developing an approximation technique to reduce kernel
computation time, and show that our proposal compares favorably to existing
kernels for PDs on several benchmarks.
Detecting and classifying lesions in mammograms with Deep Learning
Authors: Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, István Csabai
Date: 2017-07-26 00:00:00
Tasks: Breast Cancer Detection, Deep Learning, Medical Image Analysis, object-detection, Object Detection

In the last two decades Computer Aided Diagnostics (CAD) systems were
developed to help radiologists analyze screening mammograms. The benefits of
current CAD technologies appear to be contradictory and they should be improved
to be ultimately considered useful. Since 2012 deep convolutional neural
networks (CNN) have been a tremendous success in image recognition, reaching
human performance. These methods have greatly surpassed the traditional
approaches, which are similar to currently used CAD solutions. Deep CNN-s have
the potential to revolutionize medical image analysis. We propose a CAD system
based on one of the most successful object detection frameworks, Faster R-CNN.
The system detects and classifies malignant or benign lesions on a mammogram
without any human intervention. The proposed method sets the state of the art
classification performance on the public INbreast database, AUC = 0.95 . The
approach described here has achieved the 2nd place in the Digital Mammography
DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches
high sensitivity with very few false positive marks per image on the INbreast
dataset. Source code, the trained model and an OsiriX plugin are availaible
online at https://github.com/riblidezso/frcnn_cad .
Performance Evaluation of Deep Learning Tools in Docker Containers
Authors: Pengfei Xu, Shaohuai Shi, Xiaowen Chu
Date: 2017-11-09 00:00:00
Tasks: CPU, Deep Learning, GPU, Management

With the success of deep learning techniques in a broad range of application
domains, many deep learning software frameworks have been developed and are
being updated frequently to adapt to new hardware features and software
libraries, which bring a big challenge for end users and system administrators.
To address this problem, container techniques are widely used to simplify the
deployment and management of deep learning software. However, it remains
unknown whether container techniques bring any performance penalty to deep
learning applications. The purpose of this work is to systematically evaluate
the impact of docker container on the performance of deep learning
applications. We first benchmark the performance of system components (IO, CPU
and GPU) in a docker container and the host system and compare the results to
see if there's any difference. According to our results, we find that
computational intensive jobs, either running on CPU or GPU, have small overhead
indicating docker containers can be applied to deep learning programs. Then we
evaluate the performance of some popular deep learning tools deployed in a
docker container and the host system. It turns out that the docker container
will not cause noticeable drawbacks while running those deep learning tools. So
encapsulating deep learning tool in a container is a feasible solution.
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Authors: Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han
Date: 2017-10-14 00:00:00
Proceeding: NeurIPS 2017 12

Overfitting is one of the most critical challenges in deep neural networks,
and there are various types of regularization methods to improve generalization
performance. Injecting noises to hidden units during training, e.g., dropout,
is known as a successful regularizer, but it is still not clear enough why such
training techniques work well in practice and how we can maximize their benefit
in the presence of two conflicting objectives---optimizing to true data
distribution and preventing overfitting by regularization. This paper addresses
the above issues by 1) interpreting that the conventional training methods with
regularization by noise injection optimize the lower bound of the true
objective and 2) proposing a technique to achieve a tighter lower bound using
multiple noise samples per training example in a stochastic gradient descent
iteration. We demonstrate the effectiveness of our idea in several computer
vision applications.
One-pass Person Re-identification by Sketch Online Discriminant Analysis
Authors: Wei-Hong Li, Zhuowei Zhong, Wei-Shi Zheng
Date: 2017-11-09 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-id) is to match people across disjoint camera
views in a multi-camera system, and re-id has been an important technology
applied in smart city in recent years. However, the majority of existing person
re-id methods are not designed for processing sequential data in an online way.
This ignores the real-world scenario that person images detected from
multi-cameras system are coming sequentially. While there is a few work on
discussing online re-id, most of them require considerable storage of all
passed data samples that have been ever observed, and this could be unrealistic
for processing data from a large camera network. In this work, we present an
onepass person re-id model that adapts the re-id model based on each newly
observed data and no passed data are directly used for each update. More
specifically, we develop an Sketch online Discriminant Analysis (SoDA) by
embedding sketch processing into Fisher discriminant analysis (FDA). SoDA can
efficiently keep the main data variations of all passed samples in a low rank
matrix when processing sequential data samples, and estimate the approximate
within-class variance (i.e. within-class covariance matrix) from the sketch
data information. We provide theoretical analysis on the effect of the
estimated approximate within-class covariance matrix. In particular, we derive
upper and lower bounds on the Fisher discriminant score (i.e. the quotient
between between-class variation and within-class variation after feature
transformation) in order to investigate how the optimal feature transformation
learned by SoDA sequentially approximates the offline FDA that is learned on
all observed data. Extensive experimental results have shown the effectiveness
of our SoDA and empirically support our theoretical analysis.
Multi-Relevance Transfer Learning
Authors: Tianchun Wang
Date: 2017-11-09 00:00:00
Tasks: Transfer Learning

Transfer learning aims to faciliate learning tasks in a label-scarce target
domain by leveraging knowledge from a related source domain with plenty of
labeled data. Often times we may have multiple domains with little or no
labeled data as targets waiting to be solved. Most existing efforts tackle
target domains separately by modeling the `source-target' pairs without
exploring the relatedness between them, which would cause loss of crucial
information, thus failing to achieve optimal capability of knowledge transfer.
In this paper, we propose a novel and effective approach called Multi-Relevance
Transfer Learning (MRTL) for this purpose, which can simultaneously transfer
different knowledge from the source and exploits the shared common latent
factors between target domains. Specifically, we formulate the problem as an
optimization task based on a collective nonnegative matrix tri-factorization
framework. The proposed approach achieves both source-target transfer and
target-target leveraging by sharing multiple decomposed latent subspaces.
Further, an alternative minimization learning algorithm is developed with
convergence guarantee. Empirical study validates the performance and
effectiveness of MRTL compared to the state-of-the-art methods.
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM
Authors: Elizabeth P. Chou, Tzu-Wei Ko
Date: 2017-11-09 00:00:00
Tasks: Dimensionality Reduction, Prediction, Vocal Bursts Intensity Prediction

The current study proposes a dimension reduction method, stepwise support
vector machine (SVM), to reduce the dimensions of large p small n datasets. The
proposed method is compared with other dimension reduction methods, namely, the
Pearson product difference correlation coefficient (PCCs), recursive feature
elimination based on random forest (RF-RFE), and principal component analysis
(PCA), by using five gene expression datasets. Additionally, the prediction
performance of the variables selected by our method is evaluated. The study
found that stepwise SVM can effectively select the important variables and
achieve good prediction performance. Moreover, the predictions of stepwise SVM
for reduced datasets was better than those for the unreduced datasets. The
performance of stepwise SVM was more stable than that of PCA and RF-RFE, but
the performance difference with respect to PCCs was minimal. It is necessary to
reduce the dimensions of large p small n datasets. We believe that stepwise SVM
can effectively eliminate noise in data and improve the prediction accuracy in
any large p small n dataset.
Frangi-Net: A Neural Network Approach to Vessel Segmentation
Authors: Weilin Fu, Katharina Breininger, Tobias Würfl, Nishant Ravikumar, Roman Schaffert, Andreas Maier
Date: 2017-11-09 00:00:00

In this paper, we reformulate the conventional 2-D Frangi vesselness measure
into a pre-weighted neural network ("Frangi-Net"), and illustrate that the
Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show
that, as a neural network, Frangi-Net is trainable. We evaluate the proposed
method on a set of 45 high resolution fundus images. After fine-tuning, we
observe both qualitative and quantitative improvements in the segmentation
quality compared to the original Frangi measure, with an increase up to $17\%$
in F1 score.
Analysis of Dropout in Online Learning
Authors: Kazuyuki Hara
Date: 2017-11-09 00:00:00
Tasks: Object Recognition, speech-recognition, Speech Recognition

Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of layers
and a huge number of units and connections. Therefore, overfitting is a serious
problem with it, and the dropout which is a kind of regularization tool is
used. However, in online learning, the effect of dropout is not well known.
This paper presents our investigation on the effect of dropout in online
learning. We analyzed the effect of dropout on convergence speed near the
singular point. Our results indicated that dropout is effective in online
learning. Dropout tends to avoid the singular point for convergence speed near
that point.
A Separation Principle for Control in the Age of Deep Learning
Authors: Alessandro Achille, Stefano Soatto
Date: 2017-11-09 00:00:00

We review the problem of defining and inferring a "state" for a control
system based on complex, high-dimensional, highly uncertain measurement streams
such as videos. Such a state, or representation, should contain all and only
the information needed for control, and discount nuisance variability in the
data. It should also have finite complexity, ideally modulated depending on
available resources. This representation is what we want to store in memory in
lieu of the data, as it "separates" the control task from the measurement
process. For the trivial case with no dynamics, a representation can be
inferred by minimizing the Information Bottleneck Lagrangian in a function
class realized by deep neural networks. The resulting representation has much
higher dimension than the data, already in the millions, but it is smaller in
the sense of information content, retaining only what is needed for the task.
This process also yields representations that are invariant to nuisance factors
and having maximally independent components. We extend these ideas to the
dynamic case, where the representation is the posterior density of the task
variable given the measurements up to the current time, which is in general
much simpler than the prediction density maintained by the classical Bayesian
filter. Again this can be finitely-parametrized using a deep neural network,
and already some applications are beginning to emerge. No explicit assumption
of Markovianity is needed; instead, complexity trades off approximation of an
optimal representation, including the degree of Markovianity.
Joint Screening Tests for LASSO
Authors: C. Herzet, A. Drémeau
Date: 2017-10-26 00:00:00

This paper focusses on "safe" screening techniques for the LASSO problem.
Motivated by the need for low-complexity algorithms, we propose a new approach,
dubbed "joint" screening test, allowing to screen a set of atoms by carrying
out one single test. The approach is particularized to two different sets of
atoms, respectively expressed as sphere and dome regions. After presenting the
mathematical derivations of the tests, we elaborate on their relative
effectiveness and discuss the practical use of such procedures.
DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
Authors: Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, Senthil Mani
Date: 2017-11-09 00:00:00
Tasks: Deep Learning, valid

With an abundance of research papers in deep learning, reproducibility or
adoption of the existing works becomes a challenge. This is due to the lack of
open source implementations provided by the authors. Further, re-implementing
research papers in a different library is a daunting task. To address these
challenges, we propose a novel extensible approach, DLPaper2Code, to extract
and understand deep learning design flow diagrams and tables available in a
research paper and convert them to an abstract computational graph. The
extracted computational graph is then converted into execution ready source
code in both Keras and Caffe, in real-time. An arXiv-like website is created
where the automatically generated designs is made publicly available for 5,000
research papers. The generated designs could be rated and edited using an
intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our
approach, we create a simulated dataset with over 216,000 valid design
visualizations using a manually defined grammar. Experiments on the simulated
dataset show that the proposed framework provide more than $93\%$ accuracy in
flow diagram content extraction.
Egocentric Video Description based on Temporally-Linked Sequences
Authors: Marc Bolaños, Álvaro Peris, Francisco Casacuberta, Sergi Soler, Petia Radeva
Date: 2017-04-07 00:00:00
Tasks: Decoder, Video Description

Egocentric vision consists in acquiring images along the day from a first
person point-of-view using wearable cameras. The automatic analysis of this
information allows to discover daily patterns for improving the quality of life
of the user. A natural topic that arises in egocentric vision is storytelling,
that is, how to understand and tell the story relying behind the pictures. In
this paper, we tackle storytelling as an egocentric sequences description
problem. We propose a novel methodology that exploits information from
temporally neighboring events, matching precisely the nature of egocentric
sequences. Furthermore, we present a new method for multimodal data fusion
consisting on a multi-input attention recurrent network. We also publish the
first dataset for egocentric image sequences description, consisting of 1,339
events with 3,991 descriptions, from 55 days acquired by 11 people.
Furthermore, we prove that our proposal outperforms classical attentional
encoder-decoder methods for video description.
Fast camera focus estimation for gaze-based focus control
Authors: Wolfgang Fuhl, Thiago Santini, Enkelejda Kasneci
Date: 2017-11-09 00:00:00
Tasks: Depth Estimation

Many cameras implement auto-focus functionality. However, they typically
require the user to manually identify the location to be focused on. While such
an approach works for temporally-sparse autofocusing functionality (e.g., photo
shooting), it presents extreme usability problems when the focus must be
quickly switched between multiple areas (and depths) of interest - e.g., in a
gaze-based autofocus approach. This work introduces a novel, real-time
auto-focus approach based on eye-tracking, which enables the user to shift the
camera focus plane swiftly based solely on the gaze information. Moreover, the
proposed approach builds a graph representation of the image to estimate depth
plane surfaces and runs in real time (requiring ~20ms on a single i5 core),
thus allowing for the depth map estimation to be performed dynamically. We
evaluated our algorithm for gaze-based depth estimation against
state-of-the-art approaches based on eight new data sets with flat, skewed, and
round surfaces, as well as publicly available datasets.
End-to-End Learning of Semantic Grasping
Authors: Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor, Julian Ibarz, Sergey Levine
Date: 2017-07-06 00:00:00
Tasks: Object, object-detection, Object Detection, Robotic Grasping, Visual Reasoning

We consider the task of semantic robotic grasping, in which a robot picks up
an object of a user-specified class using only monocular images. Inspired by
the two-stream hypothesis of visual reasoning, we present a semantic grasping
framework that learns object detection, classification, and grasp planning in
an end-to-end fashion. A "ventral stream" recognizes object class while a
"dorsal stream" simultaneously interprets the geometric relationships necessary
to execute successful grasps. We leverage the autonomous data collection
capabilities of robots to obtain a large self-supervised dataset for training
the dorsal stream, and use semi-supervised label propagation to train the
ventral stream with only a modest amount of human supervision. We
experimentally show that our approach improves upon grasping systems whose
components are not learned end-to-end, including a baseline method that uses
bounding box detection. Furthermore, we show that jointly training our model
with auxiliary data consisting of non-semantic grasping data, as well as
semantically labeled images without grasp actions, has the potential to
substantially improve semantic grasping performance.
Keypoint-based object tracking and localization using networks of low-power embedded smart cameras
Authors: Ibrahim Abdelkader, Yasser El-Sonbaty, Mohamed El-Habrouk
Date: 2017-11-09 00:00:00
Tasks: Object, Object Tracking

Object tracking and localization is a complex task that typically requires
processing power beyond the capabilities of low-power embedded cameras. This
paper presents a new approach to real-time object tracking and localization
using multi-view binary keypoints descriptor. The proposed approach offers a
compromise between processing power, accuracy and networking bandwidth and has
been tested using multiple distributed low-power smart cameras. Additionally,
multiple optimization techniques are presented to improve the performance of
the keypoints descriptor for low-power embedded systems.
Crafting Adversarial Examples For Speech Paralinguistics Applications
Authors: Yuan Gong, Christian Poellabauer
Date: 2017-11-09 00:00:00
Tasks: Medical Diagnosis, Speaker Verification

Computational paralinguistic analysis is increasingly being used in a wide
range of cyber applications, including security-sensitive applications such as
speaker verification, deceptive speech detection, and medical diagnostics.
While state-of-the-art machine learning techniques, such as deep neural
networks, can provide robust and accurate speech analysis, they are susceptible
to adversarial attacks. In this work, we propose an end-to-end scheme to
generate adversarial examples for computational paralinguistic applications by
perturbing directly the raw waveform of an audio recording rather than specific
acoustic features. Our experiments show that the proposed adversarial
perturbation can lead to a significant performance drop of state-of-the-art
deep neural networks, while only minimally impairing the audio quality.
Feed Forward and Backward Run in Deep Convolution Neural Network
Authors: Pushparaja Murugan
Date: 2017-11-09 00:00:00
Tasks: Deep Learning, General Classification, speech-recognition, Speech Recognition

Convolution Neural Networks (CNN), known as ConvNets are widely used in many
visual imagery application, object classification, speech recognition. After
the implementation and demonstration of the deep convolution neural network in
Imagenet classification in 2012 by krizhevsky, the architecture of deep
Convolution Neural Network is attracted many researchers. This has led to the
major development in Deep learning frameworks such as Tensorflow, caffe, keras,
theno. Though the implementation of deep learning is quite possible by
employing deep learning frameworks, mathematical theory and concepts are harder
to understand for new learners and practitioners. This article is intended to
provide an overview of ConvNets architecture and to explain the mathematical
theory behind it including activation function, loss function, feedforward and
backward propagation. In this article, grey scale image is taken as input
information image, ReLU and Sigmoid activation function are considered for
developing the architecture and cross-entropy loss function are used for
computing the difference between predicted value and actual value. The
architecture is developed in such a way that it can contain one convolution
layer, one pooling layer, and multiple dense layers