pwc-archive/papers-with-abstracts huggingface dataset:
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
Authors: Rahul Dey, Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
Date: 2018-12-19 00:00:00
Tasks: Face Generation

We present a new stage-wise learning paradigm for training generative
adversarial networks (GANs). The goal of our work is to progressively
strengthen the discriminator and thus, the generators, with each subsequent
stage without changing the network architecture. We call this proposed method
the RankGAN. We first propose a margin-based loss for the GAN discriminator. We
then extend it to a margin-based ranking loss to train the multiple stages of
RankGAN. We focus on face images from the CelebA dataset in our work and show
visual as well as quantitative improvements in face generation and completion
tasks over other GAN approaches, including WGAN and LSGAN.
Bayesian parameter estimation of miss-specified models
Authors: Johannes Oberpriller, T. A. Enßlin
Date: 2018-12-19 00:00:00
Tasks: parameter estimation

Fitting a simplifying model with several parameters to real data of complex
objects is a highly nontrivial task, but enables the possibility to get
insights into the objects physics. Here, we present a method to infer the
parameters of the model, the model error as well as the statistics of the model
error. This method relies on the usage of many data sets in a simultaneous
analysis in order to overcome the problems caused by the degeneracy between
model parameters and model error. Errors in the modeling of the measurement
instrument can be absorbed in the model error allowing for applications with
complex instruments.
Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics
Authors: Supun Abeysinghe, Isura Manchanayake, Chamod Samarajeewa, Prabod Rathnayaka, Malaka J. Walpola, Rashmika Nawaratne, Tharindu Bandaragoda, Damminda Alahakoon
Date: 2018-12-19 00:00:00
Tasks: Decision Making

Social media has gained an immense popularity over the last decade. People
tend to express opinions about their daily encounters on social media freely.
These daily encounters include the places they traveled, hotels or restaurants
they have tried and aspects related to tourism in general. Since people usually
express their true experiences on social media, the expressed opinions contain
valuable information that can be used to generate business value and aid in
decision-making processes. Due to the large volume of data, it is not a
feasible task to manually go through each and every item and extract the
information. Hence, we propose a social media analytics platform which has the
capability to identify discussion pathways and aspects with their corresponding
sentiment and deeper emotions using machine learning techniques and a
visualization tool which shows the extracted insights in a comprehensible and
concise manner. Identified topic pathways and aspects will give a decision
maker some insight into what are the most discussed topics about the entity
whereas associated sentiments and emotions will help to identify the feedback.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
Authors: Ye Wang, Yueru Chen, Jongmoo Choi, C. -C. Jay Kuo
Date: 2018-12-19 00:00:00
Tasks: Data Augmentation

This paper reports a visible and thermal drone monitoring system that
integrates deep-learning-based detection and tracking modules. The biggest
challenge in adopting deep learning methods for drone detection is the paucity
of training drone images especially thermal drone images. To address this
issue, we develop two data augmentation techniques. One is a model-based drone
augmentation technique that automatically generates visible drone images with a
bounding box label on the drone's location. The other is exploiting an
adversarial data augmentation methodology to create thermal drone images. To
track a small flying drone, we utilize the residual information between
consecutive image frames. Finally, we present an integrated detection and
tracking system that outperforms the performance of each individual module
containing detection or tracking only. The experiments show that even being
trained on synthetic data, the proposed system performs well on real-world
drone images with complex background. The USC drone detection and tracking
dataset with user labeled bounding boxes is available to the public.
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization
Authors: Xuefeng Gao, Mert Gurbuzbalaban, Lingjiong Zhu
Date: 2018-12-19 00:00:00

Langevin dynamics (LD) has been proven to be a powerful technique for optimizing a non-convex objective as an efficient algorithm to find local minima while eventually visiting a global minimum on longer time-scales. LD is based on the first-order Langevin diffusion which is reversible in time. We study two variants that are based on non-reversible Langevin diffusions: the underdamped Langevin dynamics (ULD) and the Langevin dynamics with a non-symmetric drift (NLD). Adopting the techniques of Tzen, Liang and Raginsky (2018) for LD to non-reversible diffusions, we show that for a given local minimum that is within an arbitrary distance from the initialization, with high probability, either the ULD trajectory ends up somewhere outside a small neighborhood of this local minimum within a recurrence time which depends on the smallest eigenvalue of the Hessian at the local minimum or they enter this neighborhood by the recurrence time and stay there for a potentially exponentially long escape time. The ULD algorithms improve upon the recurrence time obtained for LD in Tzen, Liang and Raginsky (2018) with respect to the dependency on the smallest eigenvalue of the Hessian at the local minimum. Similar result and improvement are obtained for the NLD algorithm. We also show that non-reversible variants can exit the basin of attraction of a local minimum faster in discrete time when the objective has two local minima separated by a saddle point and quantify the amount of improvement. Our analysis suggests that non-reversible Langevin algorithms are more efficient to locate a local minimum as well as exploring the state space. Our analysis is based on the quadratic approximation of the objective around a local minimum. As a by-product of our analysis, we obtain optimal mixing rates for quadratic objectives in the 2-Wasserstein distance for two non-reversible Langevin algorithms we consider.
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Authors: Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan Oseledets, Luca Daniel
Date: 2018-12-18 00:00:00
Tasks: BIG-bench Machine Learning

With deep neural networks providing state-of-the-art machine learning models
for numerous machine learning tasks, quantifying the robustness of these models
has become an important area of research. However, most of the research
literature merely focuses on the \textit{worst-case} setting where the input of
the neural network is perturbed with noises that are constrained within an
$\ell_p$ ball; and several algorithms have been proposed to compute certified
lower bounds of minimum adversarial distortion based on such worst-case
analysis. In this paper, we address these limitations and extend the approach
to a \textit{probabilistic} setting where the additive noises can follow a
given distributional characterization. We propose a novel probabilistic
framework PROVEN to PRObabilistically VErify Neural networks with statistical
guarantees -- i.e., PROVEN certifies the probability that the classifier's
top-1 prediction cannot be altered under any constrained $\ell_p$ norm
perturbation to a given input. Importantly, we show that it is possible to
derive closed-form probabilistic certificates based on current state-of-the-art
neural network robustness verification frameworks. Hence, the probabilistic
certificates provided by PROVEN come naturally and with almost no overhead when
obtaining the worst-case certified lower bounds from existing methods such as
Fast-Lin, CROWN and CNN-Cert. Experiments on small and large MNIST and CIFAR
neural network models demonstrate our probabilistic approach can achieve up to
around $75\%$ improvement in the robustness certification with at least a
$99.99\%$ confidence compared with the worst-case robustness certificate
delivered by CROWN.
Quantum computing and the brain: quantum nets, dessins d'enfants and neural networks
Authors: Torsten Asselmeyer-Maluga
Date: 2018-12-18 00:00:00
Tasks: Tensor Networks

In this paper, we will discuss a formal link between neural networks and
quantum computing. For that purpose we will present a simple model for the
description of the neural network by forming sub-graphs of the whole network
with the same or a similar state. We will describe the interaction between
these areas by closed loops, the feedback loops. The change of the graph is
given by the deformations of the loops. This fact can be mathematically
formalized by the fundamental group of the graph. Furthermore the neuron has
two basic states $|0\rangle$ (ground state) and $|1\rangle$ (excited state).
The whole state of an area of neurons is the linear combination of the two
basic state with complex coefficients representing the signals (with 3
Parameters: amplitude, frequency and phase) along the neurons. Then it can be
shown that the set of all signals forms a manifold (character variety) and all
properties of the network must be encoded in this manifold. In the paper, we
will discuss how to interpret learning and intuition in this model. Using the
Morgan-Shalen compactification, the limit for signals with large amplitude can
be analyzed by using quasi-Fuchsian groups as represented by dessins d'enfants
(graphs to analyze Riemannian surfaces). As shown by Planat and collaborators,
these dessins d'enfants are a direct bridge to (topological) quantum computing
with permutation groups. The normalization of the signal reduces to the group
$SU(2)$ and the whole model to a quantum network. Then we have a direct
connection to quantum circuits. This network can be transformed into operations
on tensor networks. Formally we will obtain a link between machine learning and
Quantum computing.
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
Authors: Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
Date: 2018-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Imitation Learning, Management, Transfer Learning

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, results in challenging mixed-integer nonlinear programming (MINLP) problems in most cases. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). Instead of the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning. To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations will demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while achieving significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Authors: Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi
Date: 2018-12-18 00:00:00
Tasks: Adversarial Attack, Self-Driving Cars, Survey

In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017.
The Recognition Of Persian Phonemes Using PPNet
Authors: Saber Malekzadeh, Mohammad Hossein Gholizadeh, Hossein Ghayoumi zadeh, Seyed Naser Razavi
Date: 2018-12-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, Document Classification, General Classification, image-classification, Image Classification, speech-recognition, Speech Recognition

In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian Consonant-Vowel Combination (PCVC) speech dataset. Nowadays, deep neural networks play a crucial role in classification tasks. However, the best results in speech recognition are not yet as perfect as human recognition rate. Deep learning techniques show outstanding performance over many other classification tasks like image classification, document classification, etc. Furthermore, the performance is sometimes better than a human. The reason why automatic speech recognition (ASR) systems are not as qualified as the human speech recognition system, mostly depends on features of data which is fed to deep neural networks. Methods: In this research, firstly, the sound samples are cut for the exact extraction of phoneme sounds in 50ms samples. Then, phonemes are divided into 30 groups, containing 23 consonants, 6 vowels, and a silence phoneme. Results: The short-time Fourier transform (STFT) is conducted on them, and the results are given to PPNet (A new deep convolutional neural network architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on separated Persian phonemes (Like in PCVC speech dataset). Conclusion: This method can be used not only for recognizing mono-phonemes but also it can be adopted as an input to the selection of the best words in speech transcription.
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
Authors: Hichem Sahbi
Date: 2018-12-21 00:00:00
Tasks: Action Recognition, Change Detection, Image to text, Temporal Action Localization

Canonical correlation analysis (CCA) is a statistical learning method that
seeks to build view-independent latent representations from multi-view data.
This method has been successfully applied to several pattern analysis tasks
such as image-to-text mapping and view-invariant object/action recognition.
However, this success is highly dependent on the quality of data pairing (i.e.,
alignments) and mispairing adversely affects the generalization ability of the
learned CCA representations. In this paper, we address the issue of alignment
errors using a new variant of canonical correlation analysis referred to as
alignment-agnostic (AA) CCA. Starting from erroneously paired data taken from
different views, this CCA finds transformation matrices by optimizing a
constrained maximization problem that mixes a data correlation term with
context regularization; the particular design of these two terms mitigates the
effect of alignment errors when learning the CCA transformations. Experiments
conducted on multi-view tasks, including multi-temporal satellite image change
detection, show that our AA CCA method is highly effective and resilient to
mispairing errors.
Multimodal Sensor Fusion In Single Thermal image Super-Resolution
Authors: Feras Almasri, Olivier Debeir
Date: 2018-12-21 00:00:00
Tasks: Image Super-Resolution, Sensor Fusion, Super-Resolution

With the fast growth in the visual surveillance and security sectors, thermal
infrared images have become increasingly necessary ina large variety of
industrial applications. This is true even though IR sensors are still more
expensive than their RGB counterpart having the same resolution. In this paper,
we propose a deep learning solution to enhance the thermal image resolution.
The following results are given:(I) Introduction of a multimodal,
visual-thermal fusion model that ad-dresses thermal image super-resolution, via
integrating high-frequency information from the visual image. (II)
Investigation of different net-work architecture schemes in the literature,
their up-sampling methods,learning procedures, and their optimization functions
by showing their beneficial contribution to the super-resolution problem. (III)
A bench-mark ULB17-VT dataset that contains thermal images and their visual
images counterpart is presented. (IV) Presentation of a qualitative evaluation
of a large test set with 58 samples and 22 raters which shows that our proposed
model performs better against state-of-the-arts.
Polygonal approximation of digital planar curve using novel significant measure
Authors: Mangayarkarasi Ramaiah, Dilip K. Prasad
Date: 2018-12-21 00:00:00
Tasks: Segmentation

This paper presents an iterative smoothing technique for polygonal
approximation of digital image boundary. The technique starts with finest
initial segmentation points of a curve. The contribution of initially segmented
points towards preserving the original shape of the image boundary is
determined by computing the significant measure of every initial segmentation
points which is sensitive to sharp turns, which may be missed easily when
conventional significant measures are used for detecting dominant points. The
proposed method differentiates between the situations when a point on the curve
between two points on a curve projects directly upon the line segment or beyond
this line segment. It not only identifies these situations, but also computes
its significant contribution for these situations differently. This
situation-specific treatment allows preservation of points with high curvature
even as revised set of dominant points are derived. The experimental results
show that the proposed technique competes well with the state of the art
techniques.
Persistence Bag-of-Words for Topological Data Analysis
Authors: Bartosz Zieliński, Michał Lipiński, Mateusz Juda, Matthias Zeppelzauer, Paweł Dłotko
Date: 2018-12-21 00:00:00
Tasks: BIG-bench Machine Learning, Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.
Symbolic inductive bias for visually grounded learning of spoken language
Authors: Grzegorz Chrupała
Date: 2018-12-21 00:00:00
Tasks: Image Retrieval, Inductive Bias, Retrieval

A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.
Learning from Web Data: the Benefit of Unsupervised Object Localization
Authors: Xiaoxiao Sun, Liang Zheng, Yu-Kun Lai, Jufeng Yang
Date: 2018-12-21 00:00:00
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object Localization, TAG, Unsupervised Object Localization

Annotating a large number of training images is very time-consuming. In this
background, this paper focuses on learning from easy-to-acquire web data and
utilizes the learned model for fine-grained image classification in labeled
datasets. Currently, the performance gain from training with web data is
incremental, like a common saying "better than nothing, but not by much".
Conventionally, the community looks to correcting the noisy web labels to
select informative samples. In this work, we first systematically study the
built-in gap between the web and standard datasets, i.e. different data
distributions between the two kinds of data. Then, in addition to using web
labels, we present an unsupervised object localization method, which provides
critical insights into the object density and scale in web images.
Specifically, we design two constraints on web data to substantially reduce the
difference of data distributions for the web and standard datasets. First, we
present a method to control the scale, localization and number of objects in
the detected region. Second, we propose to select the regions containing
objects that are consistent with the web tag. Based on the two constraints, we
are able to process web images to reduce the gap, and the processed web data is
used to better assist the standard dataset to train CNNs. Experiments on
several fine-grained image classification datasets confirm that our method
performs favorably against the state-of-the-art methods.