pwc-archive/papers-with-abstracts huggingface dataset:
Continuous-variable quantum neural networks
Authors: Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, Seth Lloyd
Date: 2018-06-18 00:00:00
Tasks: Fraud Detection

We introduce a general method for building neural networks on quantum
computers. The quantum neural network is a variational quantum circuit built in
the continuous-variable (CV) architecture, which encodes quantum information in
continuous degrees of freedom such as the amplitudes of the electromagnetic
field. This circuit contains a layered structure of continuously parameterized
gates which is universal for CV quantum computation. Affine transformations and
nonlinear activation functions, two key elements in neural networks, are
enacted in the quantum network using Gaussian and non-Gaussian gates,
respectively. The non-Gaussian gates provide both the nonlinearity and the
universality of the model. Due to the structure of the CV model, the CV quantum
neural network can encode highly nonlinear transformations while remaining
completely unitary. We show how a classical network can be embedded into the
quantum formalism and propose quantum versions of various specialized model
such as convolutional, recurrent, and residual networks. Finally, we present
numerous modeling experiments built with the Strawberry Fields software
library. These experiments, including a classifier for fraud detection, a
network which generates Tetris images, and a hybrid classical-quantum
autoencoder, demonstrate the capability and adaptability of CV quantum neural
networks.
Beyond Local Nash Equilibria for Adversarial Networks
Authors: Frans A. Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol, Roderich Groß
Date: 2018-06-18 00:00:00

Save for some special cases, current training methods for Generative
Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash
equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual
Nash equilibrium (NE), which implies that there are no guarantees on the
quality of the found generator or classifier. This paper proposes to model GANs
explicitly as finite games in mixed strategies, thereby ensuring that every LNE
is an NE. With this formulation, we propose a solution method that is proven to
monotonically converge to a resource-bounded Nash equilibrium (RB-NE): by
increasing computational resources we can find better solutions. We empirically
demonstrate that our method is less prone to typical GAN problems such as mode
collapse, and produces solutions that are less exploitable than those produced
by GANs and MGANs, and closely resemble theoretical predictions about NEs.
Coupled Fluid Density and Motion from Single Views
Authors: Marie-Lena Eckert, Wolfgang Heidrich, Nils Thuerey
Date: 2018-06-18 00:00:00

We present a novel method to reconstruct a fluid's 3D density and motion
based on just a single sequence of images. This is rendered possible by using
powerful physical priors for this strongly under-determined problem. More
specifically, we propose a novel strategy to infer density updates strongly
coupled to previous and current estimates of the flow motion. Additionally, we
employ an accurate discretization and depth-based regularizers to compute
stable solutions. Using only one view for the reconstruction reduces the
complexity of the capturing setup drastically and could even allow for online
video databases or smart-phone videos as inputs. The reconstructed 3D velocity
can then be flexibly utilized, e.g., for re-simulation, domain modification or
guiding purposes. We will demonstrate the capacity of our method with a series
of synthetic test cases and the reconstruction of real smoke plumes captured
with a Raspberry Pi camera.
Tensor-Tensor Product Toolbox
Authors: Canyi Lu
Date: 2018-06-17 00:00:00

The tensor-tensor product (t-product) [M. E. Kilmer and C. D. Martin, 2011]
is a natural generalization of matrix multiplication. Based on t-product, many
operations on matrix can be extended to tensor cases, including tensor SVD,
tensor spectral norm, tensor nuclear norm [C. Lu, et al., 2018] and many
others. The linear algebraic structure of tensors are similar to the matrix
cases. We develop a Matlab toolbox to implement several basic operations on
tensors based on t-product. The toolbox is available at
https://github.com/canyilu/tproduct.
In situ TensorView: In situ Visualization of Convolutional Neural Networks
Authors: Xinyu Chen, Qiang Guan, Li-Ta Lo, Simon Su, James Ahrens, Trilce Estrada
Date: 2018-06-16 00:00:00

Convolutional Neural Networks(CNNs) are complex systems. They are trained so
they can adapt their internal connections to recognize images, texts and more.
It is both interesting and helpful to visualize the dynamics within such deep
artificial neural networks so that people can understand how these artificial
networks are learning and making predictions. In the field of scientific
simulations, visualization tools like Paraview have long been utilized to
provide insights and understandings. We present in situ TensorView to visualize
the training and functioning of CNNs as if they are systems of scientific
simulations. In situ TensorView is a loosely coupled in situ visualization open
framework that provides multiple viewers to help users to visualize and
understand their networks. It leverages the capability of co-processing from
Paraview to provide real-time visualization during training and predicting
phases. This avoid heavy I/O overhead for visualizing large dynamic systems.
Only a small number of lines of codes are injected in TensorFlow framework. The
visualization can provide guidance to adjust the architecture of networks, or
compress the pre-trained networks. We showcase visualizing the training of
LeNet-5 and VGG16 using in situ TensorView.
Two Stream Self-Supervised Learning for Action Recognition
Authors: Ahmed Taha, Moustafa Meshry, Xitong Yang, Yi-Ting Chen, Larry Davis
Date: 2018-06-16 00:00:00
Tasks: Action Recognition, Representation Learning, Self-Supervised Learning, Temporal Action Localization, Vocal Bursts Valence Prediction

We present a self-supervised approach using spatio-temporal signals between
video frames for action recognition. A two-stream architecture is leveraged to
tangle spatial and temporal representation learning. Our task is formulated as
both a sequence verification and spatio-temporal alignment tasks. The former
task requires motion temporal structure understanding while the latter couples
the learned motion with the spatial representation. The self-supervised
pre-trained weights effectiveness is validated on the action recognition task.
Quantitative evaluation shows the self-supervised approach competence on three
datasets: HMDB51, UCF101, and Honda driving dataset (HDD). Further
investigations to boost performance and generalize validity are still required.
G2D: from GTA to Data
Authors: Anh-Dzung Doan, Abdul Mohsi Jawaid, Thanh-Toan Do, Tat-Jun Chin
Date: 2018-06-16 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, Visual Localization, Visual Place Recognition

This document describes G2D, a software that enables capturing videos from
Grand Theft Auto V (GTA V), a popular role playing game set in an expansive
virtual city. The target users of our software are computer vision researchers
who wish to collect hyper-realistic computer-generated imagery of a city from
the street level, under controlled 6DOF camera poses and varying environmental
conditions (weather, season, time of day, traffic density, etc.).
  G2D accesses/calls the native functions of the game; hence users can directly
interact with G2D while playing the game. Specifically, G2D enables users to
manipulate conditions of the virtual environment on the fly, while the gameplay
camera is set to automatically retrace a predetermined 6DOF camera pose
trajectory within the game coordinate system. Concurrently, automatic screen
capture is executed while the virtual environment is being explored. G2D and
its source code are publicly available at https://goo.gl/SS7fS6
  In addition, we demonstrate an application of G2D to generate a large-scale
dataset with groundtruth camera poses for testing structure-from-motion (SfM)
algorithms. The dataset and generated 3D point clouds are also made available
at https://goo.gl/DNzxHx
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization
Authors: Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin
Date: 2018-06-15 00:00:00
Tasks: BIG-bench Machine Learning

We propose a scalable computerized approach for large-scale inference of
Liver Imaging Reporting and Data System (LI-RADS) final assessment categories
in narrative ultrasound (US) reports. Although our model was trained on reports
created using a LI-RADS template, it was also able to infer LI-RADS scoring for
unstructured reports that were created before the LI-RADS guidelines were
established. No human-labelled data was required in any step of this study; for
training, LI-RADS scores were automatically extracted from those reports that
contained structured LI-RADS scores, and it translated the derived knowledge to
reasoning on unstructured radiology reports. By providing automated LI-RADS
categorization, our approach may enable standardizing screening recommendations
and treatment planning of patients at risk for hepatocellular carcinoma, and it
may facilitate AI-based healthcare research with US images by offering large
scale text mining and data gathering opportunities from standard hospital
clinical data repositories.
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures
Authors: Alexey Potapov, Innokentii Zhdanov, Oleg Scherbakov, Nikolai Skorobogatko, Hugo Latapie, Enzo Fenoglio
Date: 2018-06-14 00:00:00
Tasks: Deep Learning, Image Retrieval, object-detection, Object Detection, Retrieval, Video Retrieval

Image and video retrieval by their semantic content has been an important and
challenging task for years, because it ultimately requires bridging the
symbolic/subsymbolic gap. Recent successes in deep learning enabled detection
of objects belonging to many classes greatly outperforming traditional computer
vision techniques. However, deep learning solutions capable of executing
retrieval queries are still not available. We propose a hybrid solution
consisting of a deep neural network for object detection and a cognitive
architecture for query execution. Specifically, we use YOLOv2 and OpenCog.
Queries allowing the retrieval of video frames containing objects of specified
classes and specified spatial arrangement are implemented.
Implicit Quantile Networks for Distributional Reinforcement Learning
Authors: Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
Date: 2018-06-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Atari Games, Distributional Reinforcement Learning, quantile regression, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we build on recent advances in distributional reinforcement
learning to give a generally applicable, flexible, and state-of-the-art
distributional variant of DQN. We achieve this by using quantile regression to
approximate the full quantile function for the state-action return
distribution. By reparameterizing a distribution over the sample space, this
yields an implicitly defined return distribution and gives rise to a large
class of risk-sensitive policies. We demonstrate improved performance on the 57
Atari 2600 games in the ALE, and use our algorithm's implicitly defined
distributions to study the effects of risk-sensitive policies in Atari games.
Maximum a Posteriori Policy Optimisation
Authors: Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller
Date: 2018-06-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a new algorithm for reinforcement learning called Maximum
aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative
entropy objective. We show that several existing methods can directly be
related to our derivation. We develop two off-policy algorithms and demonstrate
that they are competitive with the state-of-the-art in deep reinforcement
learning. In particular, for continuous control, our method outperforms
existing methods with respect to sample efficiency, premature convergence and
robustness to hyperparameter settings while achieving similar or better final
performance.
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
Authors: Wenjia Meng, Qian Zheng, Long Yang, Pengfei Li, Gang Pan
Date: 2018-06-14 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample trajectories. In this paper, we propose a general framework to combine DQN and most of the return-based reinforcement learning algorithms, named R-DQN. We show the performance of traditional DQN can be improved effectively by introducing return-based reinforcement learning. In order to further improve the R-DQN, we design a strategy with two measurements which can qualitatively measure the policy discrepancy. Moreover, we give the two measurements' bounds in the proposed R-DQN framework. We show that algorithms with our strategy can accurately express the trace coefficient and achieve a better approximation to return. The experiments, conducted on several representative tasks from the OpenAI Gym library, validate the effectiveness of the proposed measurements. The results also show that the algorithms with our strategy outperform the state-of-the-art methods.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Authors: Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu
Date: 2018-06-13 00:00:00
Tasks: Decoder, Prediction, Traffic Prediction

Predicting traffic conditions from online route queries is a challenging task
as there are many complicated interactions over the roads and crowds involved.
In this paper, we intend to improve traffic prediction by appropriate
integration of three kinds of implicit but essential factors encoded in
auxiliary information. We do this within an encoder-decoder sequence learning
framework that integrates the following data: 1) offline geographical and
social attributes. For example, the geographical structure of roads or public
social events such as national celebrations; 2) road intersection information.
In general, traffic congestion occurs at major junctions; 3) online crowd
queries. For example, when many online queries issued for the same destination
due to a public performance, the traffic around the destination will
potentially become heavier at this location after a while. Qualitative and
quantitative experiments on a real-world dataset from Baidu have demonstrated
the effectiveness of our framework.
Deep Learning based Estimation of Weaving Target Maneuvers
Authors: Vitaly Shalumov, Itzik Klein
Date: 2018-06-13 00:00:00
Tasks: Deep Learning

In target tracking, the estimation of an unknown weaving target frequency is
crucial for improving the miss distance. The estimation process is commonly
carried out in a Kalman framework. The objective of this paper is to examine
the potential of using neural networks in target tracking applications. To that
end, we propose estimating the weaving frequency using deep neural networks,
instead of classical Kalman framework based estimation. Particularly, we focus
on the case where a set of possible constant target frequencies is known.
Several neural network architectures, requiring low computational resources
were designed to estimate the unknown frequency out of the known set of
frequencies. The proposed approach performance is compared with the multiple
model adaptive estimation algorithm. Simulation results show that in the
examined scenarios, deep neural network outperforms multiple model adaptive
estimation in terms of accuracy and the amount of required measurements to
convergence.
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Authors: Yangchen Pan, Amir-Massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski
Date: 2018-06-13 00:00:00
Proceeding: ICML 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has shown that reinforcement learning (RL) is a promising
approach to control dynamical systems described by partial differential
equations (PDE). This paper shows how to use RL to tackle more general PDE
control problems that have continuous high-dimensional action spaces with
spatial relationship among action dimensions. In particular, we propose the
concept of action descriptors, which encode regularities among
spatially-extended action dimensions and enable the agent to control
high-dimensional action PDEs. We provide theoretical evidence suggesting that
this approach can be more sample efficient compared to a conventional approach
that treats each action dimension separately and does not explicitly exploit
the spatial regularity of the action space. The action descriptor approach is
then used within the deep deterministic policy gradient algorithm. Experiments
on two PDE control problems, with up to 256-dimensional continuous actions,
show the advantage of the proposed approach over the conventional one.
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data
Authors: Frank G. Glavin
Date: 2018-06-12 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

This dissertation investigates the use of one-sided classification algorithms
in the application of separating hazardous chlorinated solvents from other
materials, based on their Raman spectra. The experimentation is carried out
using a new one-sided classification toolkit that was designed and developed
from the ground up. In the one-sided classification paradigm, the objective is
to separate elements of the target class from all outliers. These one-sided
classifiers are generally chosen, in practice, when there is a deficiency of
some sort in the training examples. Sometimes outlier examples can be rare,
expensive to label, or even entirely absent. However, this author would like to
note that they can be equally applicable when outlier examples are plentiful
but nonetheless not statistically representative of the complete outlier
concept. It is this scenario that is explicitly dealt with in this research
work. In these circumstances, one-sided classifiers have been found to be more
robust that conventional multi-class classifiers. The term "unexpected"
outliers is introduced to represent outlier examples, encountered in the test
set, that have been taken from a different distribution to the training set
examples. These are examples that are a result of an inadequate representation
of all possible outliers in the training set. It can often be impossible to
fully characterise outlier examples given the fact that they can represent the
immeasurable quantity of "everything else" that is not a target. The findings
from this research have shown the potential drawbacks of using conventional
multi-class classification algorithms when the test data come from a completely
different distribution to that of the training samples.
DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning
Authors: Ramon Gonzalez, Karl Iagnemma
Date: 2018-06-12 00:00:00
Tasks: General Classification

Terramechanics plays a critical role in the areas of ground vehicles and
ground mobile robots since understanding and estimating the variables
influencing the vehicle-terrain interaction may mean the success or the failure
of an entire mission. This research applies state-of-the-art algorithms in deep
learning to two key problems: estimating wheel slip and classifying the terrain
being traversed by a ground robot. Three data sets collected by ground robotic
platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
Fitorobot) are employed in order to compare the performance of traditional
machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
Networks (CNNs). This work also shows the impact that certain tuning parameters
and the network architecture (MLP, DNN and CNN) play on the performance of
those methods. This paper also contributes a deep discussion with the lessons
learned in the implementation of DNNs and CNNs and how these methods can be
extended to solve other problems.
A Graph Transduction Game for Multi-target Tracking
Authors: Tewodros Mulugeta Dagnew, Dalia Coppi, Marcello Pelillo, Rita Cucchiara
Date: 2018-06-12 00:00:00
Tasks: Multiple People Tracking

Semi-supervised learning is a popular class of techniques to learn from
labeled and unlabeled data. The paper proposes an application of a recently
proposed approach of graph transduction that exploits game theoretic notions to
the problem of multiple people tracking. Within the proposed framework, targets
are considered as players of a multi-player non-cooperative game. The
equilibria of the game is considered as a consistent labeling solution and thus
an estimation of the target association in the sequence of frames. Patches of
persons are extracted from the video frames using a HOG based detector and
their similarity is modeled using distances among their covariance matrices.
The solution we propose achieves satisfactory results on video surveillance
datasets. The experiments show the robustness of the method even with a heavy
unbalance between the number of labeled and unlabeled input patches.
Pressure Predictions of Turbine Blades with Deep Learning
Authors: Cheng'an Bai, Chao Zhou
Date: 2018-06-12 00:00:00
Tasks: Deep Learning, Game of Go

Deep learning has been used in many areas, such as feature detections in
images and the game of go. This paper presents a study that attempts to use the
deep learning method to predict turbomachinery performance. Three different
deep neural networks are built and trained to predict the pressure
distributions of turbine airfoils. The performance of a library of turbine
airfoils were firstly predicted using methods based on Euler equations, which
were then used to train and validate the deep learning neural networks. The
results show that network with four layers of convolutional neural network and
two layers of fully connected neural network provides the best predictions. For
the best neural network architecture, the pressure prediction on more than 99%
locations are better than 3% and 90% locations are better than 1%.
Understanding Patch-Based Learning by Explaining Predictions
Authors: Christopher Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Date: 2018-06-11 00:00:00
Tasks: General Classification

Deep networks are able to learn highly predictive models of video data. Due
to video length, a common strategy is to train them on small video snippets. We
apply the deep Taylor / LRP technique to understand the deep network's
classification decisions, and identify a "border effect": a tendency of the
classifier to look mainly at the bordering frames of the input. This effect
relates to the step size used to build the video snippet, which we can then
tune in order to improve the classifier's accuracy without retraining the
model. To our knowledge, this is the the first work to apply the deep Taylor /
LRP technique on any video analyzing neural network.