pwc-archive/papers-with-abstracts huggingface dataset:
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations
Authors: Jacob Dumford, Walter Scheirer
Date: 2018-12-07 00:00:00
Tasks: Backdoor Attack

We present a new type of backdoor attack that exploits a vulnerability of
convolutional neural networks (CNNs) that has been previously unstudied. In
particular, we examine the application of facial recognition. Deep learning
techniques are at the top of the game for facial recognition, which means they
have now been implemented in many production-level systems. Alarmingly, unlike
other commercial technologies such as operating systems and network devices,
deep learning-based facial recognition algorithms are not presently designed
with security requirements or audited for security vulnerabilities before
deployment. Given how young the technology is and how abstract many of the
internal workings of these algorithms are, neural network-based facial
recognition systems are prime targets for security breaches. As more and more
of our personal information begins to be guarded by facial recognition (e.g.,
the iPhone X), exploring the security vulnerabilities of these systems from a
penetration testing standpoint is crucial. Along these lines, we describe a
general methodology for backdooring CNNs via targeted weight perturbations.
Using a five-layer CNN and ResNet-50 as case studies, we show that an attacker
is able to significantly increase the chance that inputs they supply will be
falsely accepted by a CNN while simultaneously preserving the error rates for
legitimate enrolled classes.
Taking the Scenic Route: Automatic Exploration for Videogames
Authors: Zeping Zhan, Batu Aytemiz, Adam M. Smith
Date: 2018-12-07 00:00:00
Tasks: Diversity

Machine playtesting tools and game moment search engines require exposure to
the diversity of a game's state space if they are to report on or index the
most interesting moments of possible play. Meanwhile, mobile app distribution
services would like to quickly determine if a freshly-uploaded game is fit to
be published. Having access to a semantic map of reachable states in the game
would enable efficient inference in these applications. However, human gameplay
data is expensive to acquire relative to the coverage of a game that it
provides. We show that off-the-shelf automatic exploration strategies can
explore with an effectiveness comparable to human gameplay on the same
timescale. We contribute generic methods for quantifying exploration quality as
a function of time and demonstrate our metric on several elementary techniques
and human players on a collection of commercial games sampled from multiple
game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of
states reached and the semantic map extracted, this work makes productive
contrast with the focus on finding a behavior policy or optimizing game score
used in most automatic game playing research.
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
Authors: Marouan Belhaj, Pavlos Protopapas, Weiwei Pan
Date: 2018-12-07 00:00:00
Tasks: General Classification, Transfer Learning

In real-world applications, it is often expensive and time-consuming to
obtain labeled examples. In such cases, knowledge transfer from related
domains, where labels are abundant, could greatly reduce the need for extensive
labeling efforts. In this scenario, transfer learning comes in hand. In this
paper, we propose Deep Variational Transfer (DVT), a variational autoencoder
that transfers knowledge across domains using a shared latent Gaussian mixture
model. Thanks to the combination of a semi-supervised ELBO and parameters
sharing across domains, we are able to simultaneously: (i) align all supervised
examples of the same class into the same latent Gaussian Mixture component,
independently from their domain; (ii) predict the class of unsupervised
examples from different domains and use them to better model the occurring
shifts. We perform tests on MNIST and USPS digits datasets, showing DVT's
ability to perform transfer learning across heterogeneous datasets.
Additionally, we present DVT's top classification performances on the MNIST
semi-supervised learning challenge. We further validate DVT on a astronomical
datasets. DVT achieves states-of-the-art classification performances,
transferring knowledge across real stars surveys datasets, EROS, MACHO and
HiTS, . In the worst performance, we double the achieved F1-score for rare
classes. These experiments show DVT's ability to tackle all major challenges
posed by transfer learning: different covariate distributions, different and
highly imbalanced class distributions and different feature spaces.
Kernel Transformer Networks for Compact Spherical Convolution
Authors: Yu-Chuan Su, Kristen Grauman
Date: 2018-12-07 00:00:00
Proceeding: CVPR 2019 6

Ideally, 360{\deg} imagery could inherit the deep convolutional neural
networks (CNNs) already trained with great success on perspective projection
images. However, existing methods to transfer CNNs from perspective to
spherical images introduce significant computational costs and/or degradations
in accuracy. In this work, we present the Kernel Transformer Network (KTN).
KTNs efficiently transfer convolution kernels from perspective images to the
equirectangular projection of 360{\deg} images. Given a source CNN for
perspective images as input, the KTN produces a function parameterized by a
polar angle and kernel as output. Given a novel 360{\deg} image, that function
in turn can compute convolutions for arbitrary layers and kernels as would the
source CNN on the corresponding tangent plane projections. Distinct from all
existing methods, KTNs allow model transfer: the same model can be applied to
different source CNNs with the same base architecture. This enables application
to multiple recognition tasks without re-training the KTN. Validating our
approach with multiple source CNNs and datasets, we show that KTNs improve the
state of the art for spherical convolution. KTNs successfully preserve the
source CNN's accuracy, while offering transferability, scalability to typical
image resolutions, and, in many cases, a substantially lower memory footprint.
Change Point Estimation in a Dynamic Stochastic Block Model
Authors: Monika Bhattacharjee, Moulinath Banerjee, George Michailidis
Date: 2018-12-07 00:00:00
Tasks: Clustering, Stochastic Block Model

We consider the problem of estimating the location of a single change point in a dynamic stochastic block model. We propose two methods of estimating the change point, together with the model parameters. The first employs a least squares criterion function and takes into consideration the full structure of the stochastic block model and is evaluated at each point in time. Hence, as an intermediate step, it requires estimating the community structure based on a clustering algorithm at every time point. The second method comprises of the following two steps: in the first one, a least squares function is used and evaluated at each time point, but ignores the community structures and just considers a random graph generating mechanism exhibiting a change point. Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters. A comparison between these two methods is illustrated. Further, for both methods under their respective identifiability and certain additional regularity conditions, we establish rates of convergence and derive the asymptotic distributions of the change point estimators. The results are illustrated on synthetic data.
Quantum algorithms for feedforward neural networks
Authors: Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, Shengyu Zhang
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, Retrieval

Quantum machine learning has the potential for broad industrial applications, and the development of quantum algorithms for improving the performance of neural networks is of particular interest given the central role they play in machine learning today. In this paper we present quantum algorithms for training and evaluating feedforward neural networks based on the canonical classical feedforward and backpropagation algorithms. Our algorithms rely on an efficient quantum subroutine for approximating the inner products between vectors in a robust way, and on implicitly storing large intermediate values in quantum random access memory for fast retrieval at later stages. The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case. This makes our algorithms suited for large-scale, highly-connected networks where the number of edges in the network dominates the classical algorithmic running time. Furthermore, networks trained by our quantum algorithm may have an intrinsic resilience to overfitting, as the algorithm naturally mimics the effects of classical techniques such as drop-out used to regularize networks. Our algorithms can also be used as the basis for new quantum-inspired classical algorithms which have the same dependence on the network dimensions as their quantum counterparts, but with quadratic overhead in other parameters that makes them relatively impractical.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Authors: Rajeev Sahay, Rehana Mahfuz, Aly El Gamal
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Denoising, Dimensionality Reduction

Machine Learning models are vulnerable to adversarial attacks that rely on
perturbing the input data. This work proposes a novel strategy using
Autoencoder Deep Neural Networks to defend a machine learning model against two
gradient-based attacks: The Fast Gradient Sign attack and Fast Gradient attack.
First we use an autoencoder to denoise the test data, which is trained with
both clean and corrupted data. Then, we reduce the dimension of the denoised
data using the hidden layer representation of another autoencoder. We perform
this experiment for multiple values of the bound of adversarial perturbations,
and consider different numbers of reduced dimensions. When the test data is
preprocessed using this cascaded pipeline, the tested deep neural network
classifier yields a much higher accuracy, thus mitigating the effect of the
adversarial perturbation.
Color Constancy by GANs: An Experimental Survey
Authors: Partha Das, Anil S. Baslamisli, Yang Liu, Sezer Karaoglu, Theo Gevers
Date: 2018-12-07 00:00:00
Tasks: Color Constancy, Image-to-Image Translation, Survey, Translation

In this paper, we formulate the color constancy task as an image-to-image
translation problem using GANs. By conducting a large set of experiments on
different datasets, an experimental survey is provided on the use of different
types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs).
Based on the experimental review, recommendations are given for the design of
CC-GAN architectures based on different criteria, circumstances and datasets.
Catalog of quasars from the Kilo-Degree Survey Data Release 3
Authors: S. Nakoneczny, M. Bilicki, A. Solarz, A. Pollo, N. Maddox, C. Spiniello, M. Brescia, N. R. Napolitano
Date: 2018-12-07 00:00:00
Tasks: Feature Importance

We present a catalog of quasars selected from broad-band photometric ugri
data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are
identified by the random forest (RF) supervised machine learning model, trained
on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from
entries with excessively noisy, missing or otherwise problematic measurements.
Applying a feature importance analysis, we then tune the algorithm and identify
in the KiDS multiband catalog the 17 most useful features for the
classification, namely magnitudes, colors, magnitude ratios, and the stellarity
index. We used the t-SNE algorithm to map the multi-dimensional photometric
data onto 2D planes and compare the coverage of the training and inference
sets. We limited the inference set to r<22 to avoid extrapolation beyond the
feature space covered by training, as the SDSS spectroscopic sample is
considerably shallower than KiDS. This gives 3.4 million objects in the final
inference sample, from which the random forest identified 190,000 quasar
candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived
from a test set extracted from SDSS and not used in the training, are confirmed
by comparison with external spectroscopic and photometric QSO catalogs
overlapping with the KiDS footprint. The robustness of our results is
strengthened by number counts of the quasar candidates in the r band, as well
as by their mid-infrared colors available from WISE. An analysis of parallaxes
and proper motions of our QSO candidates found also in Gaia DR2 suggests that a
probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is
preferable for better completeness. Our study presents the first comprehensive
quasar selection from deep high-quality KiDS data and will serve as the basis
for versatile studies of the QSO population detected by this survey.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Authors: Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
Date: 2018-12-07 00:00:00
Tasks: Autonomous Driving, Imitation Learning

Our goal is to train a policy for autonomous driving via imitation learning
that is robust enough to drive a real vehicle. We find that standard behavior
cloning is insufficient for handling complex driving scenarios, even when we
leverage a perception system for preprocessing the input and a controller for
executing the output on the car: 30 million examples are still not enough. We
propose exposing the learner to synthesized data in the form of perturbations
to the expert's driving, which creates interesting situations such as
collisions and/or going off the road. Rather than purely imitating all data, we
augment the imitation loss with additional losses that penalize undesirable
events and encourage progress -- the perturbations then provide an important
signal for these losses and lead to robustness of the learned model. We show
that the ChauffeurNet model can handle complex situations in simulation, and
present ablation experiments that emphasize the importance of each of our
proposed changes and show that the model is responding to the appropriate
causal factors. Finally, we demonstrate the model driving a car in the real
world.
Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems
Authors: Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning

Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems using machine-learning and deep-learning models, such
as automated-driving vehicles. Quality assurance frameworks are required for
such machine learning systems, but there are no widely accepted and established
quality-assurance concepts and techniques. At the same time, open problems and
the relevant technical fields are not organized. To establish standard quality
assurance frameworks, it is necessary to visualize and organize these open
problems in an interdisciplinary way, so that the experts from many different
technical fields may discuss these problems in depth and develop solutions. In
the present study, we identify, classify, and explore the open problems in
quality assurance of safety-critical machine-learning systems, and their
relevant corresponding industry and technological trends, using
automated-driving vehicles as an example. Our results show that addressing
these open problems requires incorporating knowledge from several different
technological and industrial fields, including the automobile industry,
statistics, software engineering, and machine learning.
Graph Cut Segmentation Methods Revisited with a Quantum Algorithm
Authors: Lisa Tse, Peter Mountney, Paul Klein, Simone Severini
Date: 2018-12-07 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The design and performance of computer vision algorithms are greatly
influenced by the hardware on which they are implemented. CPUs, multi-core
CPUs, FPGAs and GPUs have inspired new algorithms and enabled existing ideas to
be realized. This is notably the case with GPUs, which has significantly
changed the landscape of computer vision research through deep learning. As the
end of Moores law approaches, researchers and hardware manufacturers are
exploring alternative hardware computing paradigms. Quantum computers are a
very promising alternative and offer polynomial or even exponential speed-ups
over conventional computing for some problems. This paper presents a novel
approach to image segmentation that uses new quantum computing hardware.
Segmentation is formulated as a graph cut problem that can be mapped to the
quantum approximate optimization algorithm (QAOA). This algorithm can be
implemented on current and near-term quantum computers. Encouraging results are
presented on artificial and medical imaging data. This represents an important,
practical step towards leveraging quantum computers for computer vision.
On Batch Orthogonalization Layers
Authors: Blanchette, Laganière
Date: 2018-12-07 00:00:00

Batch normalization has become ubiquitous in many state-of-the-art nets. It
accelerates training and yields good performance results. However, there are
various other alternatives to normalization, e.g. orthonormalization. The
objective of this paper is to explore the possible alternatives to channel
normalization with orthonormalization layers. The performance of the algorithms
are compared together with BN with prescribed performance measures.
A High-Order Scheme for Image Segmentation via a modified Level-Set method
Authors: Maurizio Falcone, Giulio Paolucci, Silvia Tozza
Date: 2018-12-07 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper we propose a high-order accurate scheme for image segmentation based on the level-set method. In this approach, the curve evolution is described as the 0-level set of a representation function but we modify the velocity that drives the curve to the boundary of the object in order to obtain a new velocity with additional properties that are extremely useful to develop a more stable high-order approximation with a small additional cost. The approximation scheme proposed here is the first 2D version of an adaptive "filtered" scheme recently introduced and analyzed by the authors in 1D. This approach is interesting since the implementation of the filtered scheme is rather efficient and easy. The scheme combines two building blocks (a monotone scheme and a high-order scheme) via a filter function and smoothness indicators that allow to detect the regularity of the approximate solution adapting the scheme in an automatic way. Some numerical tests on synthetic and real images confirm the accuracy of the proposed method and the advantages given by the new velocity.
Real-time Indoor Scene Reconstruction with RGBD and Inertia Input
Authors: Zunjie Zhu, Feng Xu
Date: 2018-12-07 00:00:00
Tasks: 3D Reconstruction, 3D Scene Reconstruction, Indoor Scene Reconstruction, Motion Estimation, Simultaneous Localization and Mapping

Camera motion estimation is a key technique for 3D scene reconstruction and
Simultaneous localization and mapping (SLAM). To make it be feasibly achieved,
previous works usually assume slow camera motions, which limits its usage in
many real cases. We propose an end-to-end 3D reconstruction system which
combines color, depth and inertial measurements to achieve robust
reconstruction with fast sensor motions. Our framework extends Kalman filter to
fuse the three kinds of information and involve an iterative method to jointly
optimize feature correspondences, camera poses and scene geometry. We also
propose a novel geometry-aware patch deformation technique to adapt the feature
appearance in image domain, leading to a more accurate feature matching under
fast camera motions. Experiments show that our patch deformation method
improves the accuracy of feature tracking, and our 3D reconstruction
outperforms the state-of-the-art solutions under fast camera motions.
The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving
Authors: Zecang Gu, Yin Liang, Zhaoxi Zhang
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, GPU

In this paper we proposed an ultimate theory to solve the multi-target
control problem through its introduction to the machine learning framework in
automatic driving, which explored the implementation of excellent drivers'
knowledge acquisition. Nowadays there exist some core problems that have not
been fully realized by the researchers in automatic driving, such as the
optimal way to control the multi-target objective functions of energy saving,
safe driving, headway distance control and comfort driving, as well as the
resolvability of the networks that automatic driving relied on and the
high-performance chips like GPU on the complex driving environments. According
to these problems, we developed a new theory to map multitarget objective
functions in different spaces into the same one and thus introduced a machine
learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol
based on knowledge acquisition. We will present in this paper the optimal
multi-target control by combining the fuzzy relationship of each multi-target
objective function and the implementation of excellent drivers' knowledge
acquired by machine learning. Theoretically, the impact of this method will
exceed that of the fuzzy control method used in automatic train.
A biconvex analysis for Lasso l1 reweighting
Authors: Sophie M. Fosson
Date: 2018-12-07 00:00:00
Tasks: compressed sensing

l1 reweighting algorithms are very popular in sparse signal recovery and
compressed sensing, since in the practice they have been observed to outperform
classical l1 methods. Nevertheless, the theoretical analysis of their
convergence is a critical point, and generally is limited to the convergence of
the functional to a local minimum or to subsequence convergence. In this
letter, we propose a new convergence analysis of a Lasso l1 reweighting method,
based on the observation that the algorithm is an alternated convex search for
a biconvex problem. Based on that, we are able to prove the numerical
convergence of the sequence of the iterates generated by the algorithm. This is
not yet the convergence of the sequence, but it is close enough for practical
and numerical purposes. Furthermore, we propose an alternative iterative soft
thresholding procedure, which is faster than the main algorithm.
Time Series Featurization via Topological Data Analysis
Authors: Kwangho Kim, Jisu Kim, Alessandro Rinaldo
Date: 2018-12-07 00:00:00
Tasks: Dimensionality Reduction, Feature Engineering, Time Series, Time Series Analysis, Topological Data Analysis

We develop a novel algorithm for feature extraction in time series data by leveraging tools from topological data analysis. Our algorithm provides a simple, efficient way to successfully harness topological features of the attractor of the underlying dynamical system for an observed time series. The proposed methodology relies on the persistent landscapes and silhouette of the Rips complex obtained after a de-noising step based on principal components applied to a time-delayed embedding of a noisy, discrete time series sample. We analyze the stability properties of the proposed approach and show that the resulting TDA-based features are robust to sampling noise. Experiments on synthetic and real-world data demonstrate the effectiveness of our approach. We expect our method to provide new insights on feature extraction from granular, noisy time series data.