pwc-archive/papers-with-abstracts huggingface dataset:
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Authors: Zhuo Xu, Chen Tang, Masayoshi Tomizuka
Date: 2018-12-07 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Reinforcement Learning, Reinforcement Learning

Although deep reinforcement learning (deep RL) methods have lots of strengths
that are favorable if applied to autonomous driving, real deep RL applications
in autonomous driving have been slowed down by the modeling gap between the
source (training) domain and the target (deployment) domain. Unlike current
policy transfer approaches, which generally limit to the usage of
uninterpretable neural network representations as the transferred features, we
propose to transfer concrete kinematic quantities in autonomous driving. The
proposed robust-control-based (RC) generic transfer architecture, which we call
RL-RC, incorporates a transferable hierarchical RL trajectory planner and a
robust tracking controller based on disturbance observer (DOB). The deep RL
policies trained with known nominal dynamics model are transfered directly to
the target domain, DOB-based robust tracking control is applied to tackle the
modeling gap including the vehicle dynamics errors and the external
disturbances such as side forces. We provide simulations validating the
capability of the proposed method to achieve zero-shot transfer across multiple
driving scenarios such as lane keeping, lane changing and obstacle avoidance.
PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding
Authors: Rama Kovvuri, Ram Nevatia
Date: 2018-12-07 00:00:00
Tasks: Phrase Grounding, Sentence, Transfer Learning, Visual Dialog

Phrase Grounding aims to detect and localize objects in images that are
referred to and are queried by natural language phrases. Phrase grounding finds
applications in tasks such as Visual Dialog, Visual Search and Image-text
co-reference resolution. In this paper, we present a framework that leverages
information such as phrase category, relationships among neighboring phrases in
a sentence and context to improve the performance of phrase grounding systems.
We propose three modules: Proposal Indexing Network(PIN); Inter-phrase
Regression Network(IRN) and Proposal Ranking Network(PRN) each of which analyze
the region proposals of an image at increasing levels of detail by
incorporating the above information. Also, in the absence of ground-truth
spatial locations of the phrases(weakly-supervised), we propose knowledge
transfer mechanisms that leverages the framework of PIN module. We demonstrate
the effectiveness of our approach on the Flickr 30k Entities and ReferItGame
datasets, for which we achieve improvements over state-of-the-art approaches in
both supervised and weakly-supervised variants.
Harmonic Networks: Integrating Spectral Information into CNNs
Authors: Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Date: 2018-12-07 00:00:00
Tasks: General Classification

Convolutional neural networks (CNNs) learn filters in order to capture local
correlation patterns in feature space. In contrast, in this paper we propose
harmonic blocks that produce features by learning optimal combinations of
spectral filters defined by the Discrete Cosine Transform. The harmonic blocks
are used to replace conventional convolutional layers to construct partial or
fully harmonic CNNs. We extensively validate our approach and show that the
introduction of harmonic blocks into state-of-the-art CNN baseline
architectures results in comparable or better performance in classification
tasks on small NORB, CIFAR10 and CIFAR100 datasets.
Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
Authors: Xiangtian Zheng, Bin Wang, Le Xie
Date: 2018-12-07 00:00:00
Tasks: Generative Adversarial Network

This paper concerns with the production of synthetic phasor measurement unit
(PMU) data for research and education purposes. Due to the confidentiality of
real PMU data and no public access to the real power systems infrastructure
information, the lack of credible realistic data becomes a growing concern.
Instead of constructing synthetic power grids and then producing synthetic PMU
measurement data by time simulations, we propose a model-free approach to
directly generate synthetic PMU data. we train the generative adversarial
network (GAN) with real PMU data, which can be used to generate synthetic PMU
data capturing the system dynamic behaviors. To validate the sequential
generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of
learning system dynamics. Further by evaluating the synthetic PMU data by a
proposed quantitative method, we verify GAN's potential to synthesize realistic
samples and meanwhile realize that GAN model in this paper still has room to
improve. Moreover it is the first time that such generative model is applied to
synthesize PMU data.
Residual Reinforcement Learning for Robot Control
Authors: Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine
Date: 2018-12-07 00:00:00
Tasks: Friction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conventional feedback control methods can solve various types of robot
control problems very efficiently by capturing the structure with explicit
models, such as rigid body equations of motion. However, many control problems
in modern manufacturing deal with contacts and friction, which are difficult to
capture with first-order physical modeling. Hence, applying control design
methodologies to these kinds of problems often results in brittle and
inaccurate controllers, which have to be manually tuned for deployment.
Reinforcement learning (RL) methods have been demonstrated to be capable of
learning continuous robot controllers from interactions with the environment,
even for problems that include friction and contacts. In this paper, we study
how we can solve difficult control problems in the real world by decomposing
them into a part that is solved efficiently by conventional feedback control
methods, and the residual which is solved with RL. The final control policy is
a superposition of both control signals. We demonstrate our approach by
training an agent to successfully perform a real-world block assembly task
involving contacts and unstable objects.
Deep-RBF Networks Revisited: Robust Classification with Rejection
Authors: Pourya Habib Zadeh, Reshad Hosseini, Suvrit Sra
Date: 2018-12-07 00:00:00
Tasks: Adversarial Attack, Classification, General Classification, Metric Learning, Robust classification

One of the main drawbacks of deep neural networks, like many other
classifiers, is their vulnerability to adversarial attacks. An important reason
for their vulnerability is assigning high confidence to regions with few or
even no feature points. By feature points, we mean a nonlinear transformation
of the input space extracting a meaningful representation of the input data. On
the other hand, deep-RBF networks assign high confidence only to the regions
containing enough feature points, but they have been discounted due to the
widely-held belief that they have the vanishing gradient problem. In this
paper, we revisit the deep-RBF networks by first giving a general formulation
for them, and then proposing a family of cost functions thereof inspired by
metric learning. In the proposed deep-RBF learning algorithm, the vanishing
gradient problem does not occur. We make these networks robust to adversarial
attack by adding the reject option to their output layer. Through several
experiments on the MNIST dataset, we demonstrate that our proposed method not
only achieves significant classification accuracy but is also very resistant to
various adversarial attacks.
METCC: METric learning for Confounder Control Making distance matter in high dimensional biological analysis
Authors: Kabir Manghnani, Adam Drake, Nathan Wan, Imran Haque
Date: 2018-12-07 00:00:00
Tasks: Metric Learning, Triplet

High-dimensional data acquired from biological experiments such as next
generation sequencing are subject to a number of confounding effects. These
effects include both technical effects, such as variation across batches from
instrument noise or sample processing, or institution-specific differences in
sample acquisition and physical handling, as well as biological effects arising
from true but irrelevant differences in the biology of each sample, such as age
biases in diseases. Prior work has used linear methods to adjust for such batch
effects. Here, we apply contrastive metric learning by a non-linear triplet
network to optimize the ability to distinguish biologically distinct sample
classes in the presence of irrelevant technical and biological variation. Using
whole-genome cell-free DNA data from 817 patients, we demonstrate that our
approach, METric learning for Confounder Control (METCC), is able to match or
exceed the classification performance achieved using a best-in-class linear
method (HCP) or no normalization. Critically, results from METCC appear less
confounded by irrelevant technical variables like institution and batch than
those from other methods even without access to high quality metadata
information required by many existing techniques; offering hope for improved
generalization.
Removal of Parameter Adjustment of Frangi Filters in Case of Coronary Angiograms
Authors: Dhruv Gosain, Rishabh Joshi
Date: 2018-12-07 00:00:00

Frangi Filters are one of the widely used filters for enhancing vessels in
medical images. Since they were first proposed, the threshold of the vesselness
function of Frangi Filters is to be arranged for each individual application.
These thresholds are changed manually for individual fluoroscope, for enhancing
coronary angiogram images. Hence it is felt, there is a need of mitigating the
tuning procedure of threshold values for every fluoroscope. The current papers
approach has been devised in order to treat the coronary angiogram images
uniformly, irrespective of the fluoroscopes through which they were obtained
and the patient demographics for further stenosis detection. This problem to
the best of our knowledge has not been addressed yet. In the approach, before
feeding the image to Frangi Filters, non uniform illumination of the input
image is removed using homomorphic filters and the image is enhanced using Non
Subsampled Contourlet Transform (NSCT). The experiment was conducted on the
data that has been accumulated from various hospitals in India and the results
obtained verifies dependency removal of parameters without compromising the
results obtained by Frangi filters.
A hybrid machine-learning algorithm for designing quantum experiments
Authors: L. O'Driscoll, R. Nichols, P. A. Knott
Date: 2018-12-07 00:00:00
Tasks: All, BIG-bench Machine Learning, Hybrid Machine Learning

We introduce a hybrid machine-learning algorithm for designing quantum optics
experiments that produce specific quantum states. Our algorithm successfully
found experimental schemes to produce all 5 states we asked it to, including
Schr\"odinger cat states and cubic phase states, all to a fidelity of over
$96\%$. Here we specifically focus on designing realistic experiments, and
hence all of the algorithm's designs only contain experimental elements that
are available with current technology. The core of our algorithm is a genetic
algorithm that searches for optimal arrangements of the experimental elements,
but to speed up the initial search we incorporate a neural network that
classifies quantum states. The latter is of independent interest, as it quickly
learned to accurately classify quantum states given their photon-number
distributions.
Toward the Engineering of Virtuous Machines
Authors: Naveen Sundar Govindarajulu, Selmer Bringsjord, Rikhiya Ghosh
Date: 2018-12-07 00:00:00
Tasks: Ethics, Formal Logic

While various traditions under the 'virtue ethics' umbrella have been studied
extensively and advocated by ethicists, it has not been clear that there exists
a version of virtue ethics rigorous enough to be a target for machine ethics
(which we take to include the engineering of an ethical sensibility in a
machine or robot itself, not only the study of ethics in the humans who might
create artificial agents). We begin to address this by presenting an embryonic
formalization of a key part of any virtue-ethics theory: namely, the learning
of virtue by a focus on exemplars of moral virtue. Our work is based in part on
a computational formal logic previously used to formally model other ethical
theories and principles therein, and to implement these models in artificial
agents.
Representing pictures with emotions
Authors: António Filipe Fonseca
Date: 2018-12-06 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Modern research in content-based image retrieval systems (CIBR) has become
progressively more focused on the richness of human semantics. Several
approaches may be used to reduced the 'semantic gap' between the high-level
human experience and the low level visual features of pictures. Object
ontology, among others, is one of the methods. In this paper we investigate the
use of a codified emotion ontology over global color features of images to
annotate the images at a high semantic level. In order to speed up the
annotation process the images are sampled so that each digital image is
represented by a random subset of its content. We test within controlled
conditions how this random subset may represent the adequate high level
emotional concept presented in the image. We monitor this information reducing
process with entropy measures, showing that controlled random sampling can
capture with significant relevance high level concepts for picture
representation.
Asynchronous Training of Word Embeddings for Large Text Corpora
Authors: Avishek Anand, Megha Khosla, Jaspreet Singh, Jan-Hendrik Zab, Zijian Zhang
Date: 2018-12-07 00:00:00
Tasks: Information Retrieval, Retrieval, Word Embeddings

Word embeddings are a powerful approach for analyzing language and have been
widely popular in numerous tasks in information retrieval and text mining.
Training embeddings over huge corpora is computationally expensive because the
input is typically sequentially processed and parameters are synchronously
updated. Distributed architectures for asynchronous training that have been
proposed either focus on scaling vocabulary sizes and dimensionality or suffer
from expensive synchronization latencies.
  In this paper, we propose a scalable approach to train word embeddings by
partitioning the input space instead in order to scale to massive text corpora
while not sacrificing the performance of the embeddings. Our training procedure
does not involve any parameter synchronization except a final sub-model merge
phase that typically executes in a few minutes. Our distributed training scales
seamlessly to large corpus sizes and we get comparable and sometimes even up to
45% performance improvement in a variety of NLP benchmarks using models trained
by our distributed procedure which requires $1/10$ of the time taken by the
baseline approach. Finally we also show that we are robust to missing words in
sub-models and are able to effectively reconstruct word representations.
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
Authors: Amir Moradibaad, Ramin Jalilian Mashhoud
Date: 2018-12-07 00:00:00
Tasks: Anomaly Detection, Diagnostic, Dimensionality Reduction, feature selection, General Classification, Intrusion Detection, Network Intrusion Detection

In the world today computer networks have a very important position and most
of the urban and national infrastructure as well as organizations are managed
by computer networks, therefore, the security of these systems against the
planned attacks is of great importance. Therefore, researchers have been trying
to find these vulnerabilities so that after identifying ways to penetrate the
system, they will provide system protection through preventive or
countermeasures. SVM is one of the major algorithms for intrusion detection. In
this research, we studied a variety of malware and methods of intrusion
detection, provide an efficient method for detecting attacks and utilizing
dimension reduction.Thus, we will be able to detect attacks by carefully
combining these two algorithms and pre-processes that are performed before the
two on the input data. The main question raised is how we can identify attacks
on computer networks with the above-mentioned method. In anomalies diagnostic
method, by identifying behavior as a normal behavior for the user, the host, or
the whole system, any deviation from this behavior is considered as an abnormal
behavior, which can be a potential occurrence of an attack. The network
intrusion detection system is used by anomaly detection method that uses the
SVM algorithm for classification and SVD to reduce the size. Steps of the
proposed method include pre-processing of the data set, feature selection,
support vector machine, and evaluation.The NSL-KDD data set has been used to
teach and test the proposed model. In this study, we inferred the intrusion
detection using the SVM algorithm for classification and SVD for diminishing
dimensions with no classification algorithm.Also the KNN algorithm has been
compared in situations with and without diminishing dimensions,the results have
shown that the proposed method has a better performance than comparable
methods.
Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth
Authors: Ankur Singh, Anurag Chanani, Harish Karnick
Date: 2018-12-07 00:00:00
Tasks: Colorization

In this paper, we tackle the problem of colorization of grayscale videos to
reduce bandwidth usage. For this task, we use some colored keyframes as
reference images from the colored version of the grayscale video. We propose a
model that extracts keyframes from a colored video and trains a Convolutional
Neural Network from scratch on these colored frames. Through the extracted
keyframes we get a good knowledge of the colors that have been used in the
video which helps us in colorizing the grayscale version of the video
efficiently. An application of the technique that we propose in this paper, is
in saving bandwidth while sending raw colored videos that haven't gone through
any compression. A raw colored video takes up around three times more memory
size than its grayscale version. We can exploit this fact and send a grayscale
video along with out trained model instead of a colored video. Later on, in
this paper we show how this technique can help to save bandwidth usage to upto
three times while transmitting raw colored videos.
The particle track reconstruction based on deep learning neural networks
Authors: Dmitriy Baranov, Sergey Mitsyn, Pavel Goncharov, Gennady Ososkov
Date: 2018-12-07 00:00:00
Tasks: Deep Learning

One of the most important problems of data processing in high energy and
nuclear physics is the event reconstruction. Its main part is the track
reconstruction procedure which consists in looking for all tracks that
elementary particles leave when they pass through a detector among a huge
number of points, so-called hits, produced when flying particles fire detector
coordinate planes. Unfortunately, the tracking is seriously impeded by the
famous shortcoming of multiwired, strip in GEM detectors due to the appearance
in them a lot of fake hits caused by extra spurious crossings of fired strips.
Since the number of those fakes is several orders of magnitude greater than for
true hits, one faces with the quite serious difficulty to unravel possible
track-candidates via true hits ignoring fakes. On the basis of our previous
two-stage approach based on hits preprocessing using directed K-d tree search
followed by a deep neural classifier we introduce here two new tracking
algorithms. Both algorithms combine those two stages in one while using
different types of deep neural nets. We show that both proposed deep networks
do not require any special preprocessing stage, are more accurate, faster and
can be easier parallelized. Preliminary results of our new approaches for
simulated events are presented.
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition
Authors: Maziar Sanjabi, Meisam Razaviyayn, Jason D. Lee
Date: 2018-12-07 00:00:00

In this short note, we consider the problem of solving a min-max zero-sum
game. This problem has been extensively studied in the convex-concave regime
where the global solution can be computed efficiently. Recently, there have
also been developments for finding the first order stationary points of the
game when one of the player's objective is concave or (weakly) concave. This
work focuses on the non-convex non-concave regime where the objective of one of
the players satisfies Polyak-{\L}ojasiewicz (PL) Condition. For such a game, we
show that a simple multi-step gradient descent-ascent algorithm finds an
$\varepsilon$--first order stationary point of the problem in
$\widetilde{\mathcal{O}}(\varepsilon^{-2})$ iterations.
The Limitations of Model Uncertainty in Adversarial Settings
Authors: Kathrin Grosse, David Pfaff, Michael Thomas Smith, Michael Backes
Date: 2018-12-06 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, model

Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification. While an obvious security threat, adversarial examples yield as well insights about the applied model itself. We investigate adversarial examples in the context of Bayesian neural network's (BNN's) uncertainty measures. As these measures are highly non-smooth, we use a smooth Gaussian process classifier (GPC) as substitute. We show that both confidence and uncertainty can be unsuspicious even if the output is wrong. Intriguingly, we find subtle differences in the features influencing uncertainty and confidence for most tasks.
Neuromodulated Learning in Deep Neural Networks
Authors: Dennis G Wilson, Sylvain Cussat-Blanc, Hervé Luga, Kyle Harrington
Date: 2018-12-05 00:00:00

In the brain, learning signals change over time and synaptic location, and
are applied based on the learning history at the synapse, in the complex
process of neuromodulation. Learning in artificial neural networks, on the
other hand, is shaped by hyper-parameters set before learning starts, which
remain static throughout learning, and which are uniform for the entire
network. In this work, we propose a method of deep artificial neuromodulation
which applies the concepts of biological neuromodulation to stochastic gradient
descent. Evolved neuromodulatory dynamics modify learning parameters at each
layer in a deep neural network over the course of the network's training. We
show that the same neuromodulatory dynamics can be applied to different models
and can scale to new problems not encountered during evolution. Finally, we
examine the evolved neuromodulation, showing that evolution found dynamic,
location-specific learning strategies.