pwc-archive/papers-with-abstracts huggingface dataset:
TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC
Authors: Somdip Dey, Amit K. Singh, Dilip K. Prasad, Klaus D. McDonald-Maier
Date: 2019-02-15 00:00:00

Analyzing video for traffic categorization is an important pillar of
Intelligent Transport Systems. However, it is difficult to analyze and predict
traffic based on image frames because the representation of each frame may vary
significantly within a short time period. This also would inaccurately
represent the traffic over a longer period of time such as the case of video.
We propose a novel bio-inspired methodology that integrates analysis of the
previous image frames of the video to represent the analysis of the current
image frame, the same way a human being analyzes the current situation based on
past experience. In our proposed methodology, called IRON-MAN (Integrated
Rational prediction and Motionless ANalysis), we utilize Bayesian update on top
of the individual image frame analysis in the videos and this has resulted in
highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV)
for most of the chosen test cases. The proposed approach could be used for TMAV
using Convolutional Neural Network (CNN) for applications where the number of
objects in an image is the deciding factor for prediction and results also show
that our proposed approach outperforms the state-of-the-art for the chosen test
case. We also introduce a new metric named, Energy Consumption per Training
Image (ECTI). Since, different CNN based models have different training
capability and computing resource utilization, some of the models are more
suitable for embedded device implementation than the others, and ECTI metric is
useful to assess the suitability of using a CNN model in multi-processor
systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in
terms of lifespan of the embedded device using these MPSoCs.
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Authors: Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
Date: 2019-02-15 00:00:00
Tasks: Anatomy, BIG-bench Machine Learning, Deep Learning, Medical Image Analysis

Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future.
KINN: Incorporating Expert Knowledge in Neural Networks
Authors: Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Ludger van Elst, Andreas Dengel, Sheraz Ahmed
Date: 2019-02-15 00:00:00
Tasks: Anomaly Detection, Management, Time Series Analysis

The promise of ANNs to automatically discover and extract useful
features/patterns from data without dwelling on domain expertise although seems
highly promising but comes at the cost of high reliance on large amount of
accurately labeled data, which is often hard to acquire and formulate
especially in time-series domains like anomaly detection, natural disaster
management, predictive maintenance and healthcare. As these networks completely
rely on data and ignore a very important modality i.e. expert, they are unable
to harvest any benefit from the expert knowledge, which in many cases is very
useful. In this paper, we try to bridge the gap between these data driven and
expert knowledge based systems by introducing a novel framework for
incorporating expert knowledge into the network (KINN). Integrating expert
knowledge into the network has three key advantages: (a) Reduction in the
amount of data needed to train the model, (b) provision of a lower bound on the
performance of the resulting classifier by obtaining the best of both worlds,
and (c) improved convergence of model parameters (model converges in smaller
number of epochs). Although experts are extremely good in solving different
tasks, there are some trends and patterns, which are usually hidden only in the
data. Therefore, KINN employs a novel residual knowledge incorporation scheme,
which can automatically determine the quality of the predictions made by the
expert and rectify it accordingly by learning the trends/patterns from data.
Specifically, the method tries to use information contained in one modality to
complement information missed by the other. We evaluated KINN on a real world
traffic flow prediction problem. KINN significantly superseded performance of
both the expert and as well as the base network (LSTM in this case) when
evaluated in isolation, highlighting its superiority for the task.
Asynchronous Coagent Networks
Authors: James E. Kostas, Chris Nota, Philip S. Thomas
Date: 2019-02-15 00:00:00
Proceeding: ICML 2020 1
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal policies. Additionally, we extend prior theory to encompass asynchronous and recurrent coagent networks. These extensions facilitate the straightforward design and analysis of hierarchical reinforcement learning algorithms like the option-critic, and eliminate the need for complex derivations of customized learning rules for these algorithms.
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
Authors: Macheng Shen, Jonathan P. How
Date: 2019-02-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach.
Verifiably Safe Off-Model Reinforcement Learning
Authors: Nathan Fulton, Andre Platzer
Date: 2019-02-14 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The desire to use reinforcement learning in safety-critical settings has
inspired a recent interest in formal methods for learning algorithms. Existing
formal methods for learning and optimization primarily consider the problem of
constrained learning or constrained optimization. Given a single correct model
and associated safety constraint, these approaches guarantee efficient learning
while provably avoiding behaviors outside the safety constraint. Acting well
given an accurate environmental model is an important pre-requisite for safe
learning, but is ultimately insufficient for systems that operate in complex
heterogeneous environments. This paper introduces verification-preserving model
updates, the first approach toward obtaining formal safety guarantees for
reinforcement learning in settings where multiple environmental models must be
taken into account. Through a combination of design-time model updates and
runtime model falsification, we provide a first approach toward obtaining
formal safety proofs for autonomous systems acting in heterogeneous
environments.
Classification with unknown class-conditional label noise on non-compact feature spaces
Authors: Henry W. J. Reeve, Ata Kaban
Date: 2019-02-14 00:00:00
Tasks: Classification, General Classification, regression

We investigate the problem of classification in the presence of unknown class-conditional label noise in which the labels observed by the learner have been corrupted with some unknown class dependent probability. In order to obtain finite sample rates, previous approaches to classification with unknown class-conditional label noise have required that the regression function is close to its extrema on sets of large measure. We shall consider this problem in the setting of non-compact metric spaces, where the regression function need not attain its extrema. In this setting we determine the minimax optimal learning rates (up to logarithmic factors). The rate displays interesting threshold behaviour: When the regression function approaches its extrema at a sufficient rate, the optimal learning rates are of the same order as those obtained in the label-noise free setting. If the regression function approaches its extrema more gradually then classification performance necessarily degrades. In addition, we present an adaptive algorithm which attains these rates without prior knowledge of either the distributional parameters or the local density. This identifies for the first time a scenario in which finite sample rates are achievable in the label noise setting, but they differ from the optimal rates without label noise.
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection
Authors: Binhang Yuan, Chen Wang, Chen Luo, Fei Jiang, Mingsheng Long, Philip S. Yu, Yu-An Liu
Date: 2019-02-14 00:00:00
Tasks: Anomaly Detection, General Classification, Time Series, Time Series Analysis, Time Series Anomaly Detection, Time Series Classification

Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of wind farms. Unlike traditional methods of installing expensive physical detectors on wind blades, data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this work, we propose a wavelet enhanced autoencoder model (WaveletAE) to identify wind turbine dysfunction by analyzing the multivariate time series monitored by the SCADA system. WaveletAE is enhanced with wavelet detail coefficients to enforce the autoencoder to capture information from multiple scales, and the CNN-LSTM architecture is applied to learn channel-wise and temporal-wise relations. The empirical study shows that the proposed model outperforms other state-of-the-art time series anomaly detection methods for real-world blade icing detection.
Quick and Easy Time Series Generation with Established Image-based GANs
Authors: Eoin Brophy, Zhengwei Wang, Tomas E. Ward
Date: 2019-02-14 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Generation

In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data. In this work we introduce our simple method to exploit the advancements in well established image-based GANs to synthesise single channel time series data. We implement Wasserstein GANs (WGANs) with gradient penalty due to their stability in training to synthesise three different types of data; sinusoidal data, photoplethysmograph (PPG) data and electrocardiograph (ECG) data. The length of the returned time series data is limited only by the image resolution, we use an image size of 64x64 pixels which yields 4096 data points. We present both visual and quantitative evidence that our novel method can successfully generate time series data using image-based GANs.
GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images
Authors: Swetava Ganguli, Pedro Garzon, Noa Glaser
Date: 2019-02-14 00:00:00
Tasks: Generative Adversarial Network, Style Transfer

Automatically generating maps from satellite images is an important task.
There is a body of literature which tries to address this challenge. We created
a more expansive survey of the task by experimenting with different models and
adding new loss functions to improve results. We created a database of pairs of
satellite images and the corresponding map of the area. Our model translates
the satellite image to the corresponding standard layer map image using three
main model architectures: (i) a conditional Generative Adversarial Network
(GAN) which compresses the images down to a learned embedding, (ii) a generator
which is trained as a normalizing flow (RealNVP) model, and (iii) a conditional
GAN where the generator translates via a series of convolutions to the standard
layer of a map and the discriminator input is the concatenation of the
real/generated map and the satellite image. Model (iii) was by far the most
promising of three models. To improve the results we also added a
reconstruction loss and style transfer loss in addition to the GAN losses. The
third model architecture produced the best quality of sampled images. In
contrast to the other generative model where evaluation of the model is a
challenging problem. since we have access to the real map for a given satellite
image, we are able to assign a quantitative metric to the quality of the
generated images in addition to inspecting them visually. While we are
continuing to work on increasing the accuracy of the model, one challenge has
been the coarse resolution of the data which upper-bounds the quality of the
results of our model. Nevertheless, as will be seen in the results, the
generated map is more accurate in the features it produces since the generator
architecture demands a pixel-wise image translation/pixel-wise coloring. A
video presentation summarizing this paper is available at:
https://youtu.be/Ur0flOX-Ji0
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Authors: Aditya Bhatt, Daniel Palenicek, Boris Belousov, Max Argus, Artemij Amiranashvili, Thomas Brox, Jan Peters
Date: 2019-02-14 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the update-to-data (UTD) ratio to 20 gradient update steps on the critic per environment sample. However, this comes at the expense of a greatly increased computational cost. To reduce this computational burden, we introduce CrossQ: A lightweight algorithm for continuous control tasks that makes careful use of Batch Normalization and removes target networks to surpass the current state-of-the-art in sample efficiency while maintaining a low UTD ratio of 1. Notably, CrossQ does not rely on advanced bias-reduction schemes used in current methods. CrossQ's contributions are threefold: (1) it matches or surpasses current state-of-the-art methods in terms of sample efficiency, (2) it substantially reduces the computational cost compared to REDQ and DroQ, (3) it is easy to implement, requiring just a few lines of code on top of SAC.
Exponentially-Modified Gaussian Mixture Model: Applications in Spectroscopy
Authors: Sebastian Ament, John Gregoire, Carla Gomes
Date: 2019-02-14 00:00:00
Tasks: model, quantile regression, regression

We propose a novel exponentially-modified Gaussian (EMG) mixture residual
model. The EMG mixture is well suited to model residuals that are contaminated
by a distribution with positive support. This is in contrast to commonly used
robust residual models, like the Huber loss or $\ell_1$, which assume a
symmetric contaminating distribution and are otherwise asymptotically biased.
We propose an expectation-maximization algorithm to optimize an arbitrary model
with respect to the EMG mixture. We apply the approach to linear regression and
probabilistic matrix factorization (PMF). We compare against other residual
models, including quantile regression. Our numerical experiments demonstrate
the strengths of the EMG mixture on both tasks. The PMF model arises from
considering spectroscopic data. In particular, we demonstrate the effectiveness
of PMF in conjunction with the EMG mixture model on synthetic data and two
real-world applications: X-ray diffraction and Raman spectroscopy. We show how
our approach is effective in inferring background signals and systematic errors
in data arising from these experimental settings, dramatically outperforming
existing approaches and revealing the data's physically meaningful components.
The Perils of Exploration under Competition: A Computational Modeling Approach
Authors: Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu
Date: 2019-02-14 00:00:00

We empirically study the interplay between exploration and competition.
Systems that learn from interactions with users often engage in exploration:
making potentially suboptimal decisions in order to acquire new information for
future decisions. However, when multiple systems are competing for the same
market of users, exploration may hurt a system's reputation in the near term,
with adverse competitive effects. In particular, a system may enter a "death
spiral", when the short-term reputation cost decreases the number of users for
the system to learn from, which degrades its performance relative to
competition and further decreases its market share.
  We ask whether better exploration algorithms are incentivized under
competition. We run extensive numerical experiments in a stylized duopoly model
in which two firms deploy multi-armed bandit algorithms and compete for myopic
users. We find that duopoly and monopoly tend to favor a primitive "greedy
algorithm" that does not explore and leads to low consumer welfare, whereas a
temporary monopoly (a duopoly with an early entrant) may incentivize better
bandit algorithms and lead to higher consumer welfare. Our findings shed light
on the first-mover advantage in the digital economy by exploring the role that
data can play as a barrier to entry in online markets.
Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?
Authors: Cody Burkard, Brent Lagesse
Date: 2019-02-14 00:00:00

Convolutional Neural Networks and Deep Learning classification systems in
general have been shown to be vulnerable to attack by specially crafted data
samples that appear to belong to one class but are instead classified as
another, commonly known as adversarial examples. A variety of attack strategies
have been proposed to craft these samples; however, there is no standard model
that is used to compare the success of each type of attack. Furthermore, there
is no literature currently available that evaluates how common hyperparameters
and optimization strategies may impact a model's ability to resist these
samples. This research bridges that lack of awareness and provides a means for
the selection of training and model parameters in future research on evasion
attacks against convolutional neural networks. The findings of this work
indicate that the selection of model hyperparameters does impact the ability of
a model to resist attack, although they alone cannot prevent the existence of
adversarial examples.
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Authors: Hongxu Yang, Caifeng Shan, Alexander F. Kolen, Peter H. N. de With
Date: 2019-02-14 00:00:00

Fast and accurate catheter detection in cardiac catheterization using
harmless 3D ultrasound (US) can improve the efficiency and outcome of the
intervention. However, the low image quality of US requires extra training for
sonographers to localize the catheter. In this paper, we propose a catheter
detection method based on a pre-trained VGG network, which exploits 3D
information through re-organized cross-sections to segment the catheter by a
shared fully convolutional network (FCN), which is called a Direction-Fused FCN
(DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized
by model fitting. Our experiments show that the proposed method can
successfully detect an ablation catheter in a challenging ex-vivo 3D US
dataset, which was collected on the porcine heart. Extensive analysis shows
that the proposed method achieves a Dice score of 57.7%, which offers at least
an 11.8 % improvement when compared to state-of-the-art instrument detection
methods. Due to the improved segmentation performance by the DF-FCN, the
catheter can be localized with an error of only 1.4 mm.
Adversarially Approximated Autoencoder for Image Generation and Manipulation
Authors: Wenju Xu, Shawn Keshmiri, Guanghui Wang
Date: 2019-02-14 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Regularized autoencoders learn the latent codes, a structure with the
regularization under the distribution, which enables them the capability to
infer the latent codes given observations and generate new samples given the
codes. However, they are sometimes ambiguous as they tend to produce
reconstructions that are not necessarily faithful reproduction of the inputs.
The main reason is to enforce the learned latent code distribution to match a
prior distribution while the true distribution remains unknown. To improve the
reconstruction quality and learn the latent space a manifold structure, this
work present a novel approach using the adversarially approximated autoencoder
(AAAE) to investigate the latent codes with adversarial approximation. Instead
of regularizing the latent codes by penalizing on the distance between the
distributions of the model and the target, AAAE learns the autoencoder flexibly
and approximates the latent space with a simpler generator. The ratio is
estimated using generative adversarial network (GAN) to enforce the similarity
of the distributions. Additionally, the image space is regularized with an
additional adversarial regularizer. The proposed approach unifies two deep
generative models for both latent space inference and diverse generation. The
learning scheme is realized without regularization on the latent codes, which
also encourages faithful reconstruction. Extensive validation experiments on
four real-world datasets demonstrate the superior performance of AAAE. In
comparison to the state-of-the-art approaches, AAAE generates samples with
better quality and shares the properties of regularized autoencoder with a nice
latent manifold structure.