pwc-archive/papers-with-abstracts huggingface dataset:
Anomaly Detection using Deep Learning based Image Completion
Authors: Matthias Haselmann, Dieter P. Gruber, Paul Tabatabai
Date: 2018-11-16 00:00:00
Tasks: Anomaly Detection, Deep Learning

Automated surface inspection is an important task in many manufacturing
industries and often requires machine learning driven solutions. Supervised
approaches, however, can be challenging, since it is often difficult to obtain
large amounts of labeled training data. In this work, we instead perform
one-class unsupervised learning on fault-free samples by training a deep
convolutional neural network to complete images whose center regions are cut
out. Since the network is trained exclusively on fault-free data, it completes
the image patches with a fault-free version of the missing image region. The
pixel-wise reconstruction error within the cut out region is an anomaly image
which can be used for anomaly detection. Results on surface images of decorated
plastic parts demonstrate that this approach is suitable for detection of
visible anomalies and moreover surpasses all other tested methods.
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control
Authors: Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal
Date: 2018-11-16 00:00:00
Tasks: Autonomous Driving, Self-Driving Cars, Uncertainty Quantification

A rise in popularity of Deep Neural Networks (DNNs), attributed to more
powerful GPUs and widely available datasets, has seen them being increasingly
used within safety-critical domains. One such domain, self-driving, has
benefited from significant performance improvements, with millions of miles
having been driven with no human intervention. Despite this, crashes and
erroneous behaviours still occur, in part due to the complexity of verifying
the correctness of DNNs and a lack of safety guarantees.
  In this paper, we demonstrate how quantitative measures of uncertainty can be
extracted in real-time, and their quality evaluated in end-to-end controllers
for self-driving cars. To this end we utilise a recent method for gathering
approximate uncertainty information from DNNs without changing the network's
architecture. We propose evaluation techniques for the uncertainty on two
separate architectures which use the uncertainty to predict crashes up to five
seconds in advance. We find that mutual information, a measure of uncertainty
in classification networks, is a promising indicator of forthcoming crashes.
Fixation properties of multiple cooperator configurations on regular graphs
Authors: Hendrik Richter
Date: 2018-11-16 00:00:00

Whether or not cooperation is favored in evolutionary games on graphs depends
on the population structure and spatial properties of the interaction network.
Population structures can be expressed as configurations. Such configurations
extend scenarios with a single cooperator among defectors to any number of
cooperators and any arrangement of cooperators and defectors. Thus, as a single
cooperator can be interpreted as a lone mutant, the discussion about fixation
properties based on configurations also applies to multiple mutants. For
interaction networks modeled as regular graphs and for weak selection, the
emergence of cooperation can be assessed by structure coefficients, which are
specific for a configuration and a graph. We analyze these structure
coefficients and particularly show that under certain conditions the
coefficients strongly correlate to the average shortest path length between
cooperators on the evolutionary graph. Thus,for multiple cooperators fixation
properties on regular evolutionary graphs can be linked to cooperator path
length.
Using recurrences in time and frequency within U-net architecture for speech enhancement
Authors: Tomasz Grzywalski, Szymon Drgas
Date: 2018-11-16 00:00:00
Tasks: Speech Enhancement

When designing fully-convolutional neural network, there is a trade-off
between receptive field size, number of parameters and spatial resolution of
features in deeper layers of the network. In this work we present a novel
network design based on combination of many convolutional and recurrent layers
that solves these dilemmas. We compare our solution with U-nets based models
known from the literature and other baseline models on speech enhancement task.
We test our solution on TIMIT speech utterances combined with noise segments
extracted from NOISEX-92 database and show clear advantage of proposed solution
in terms of SDR (signal-to-distortion ratio), SIR (signal-to-interference
ratio) and STOI (spectro-temporal objective intelligibility) metrics compared
to the current state-of-the-art.
Evolutionary Diversity Optimization Using Multi-Objective Indicators
Authors: Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann
Date: 2018-11-16 00:00:00
Tasks: Diversity

Evolutionary diversity optimization aims to compute a diverse set of
solutions where all solutions meet a given quality criterion. With this paper,
we bridge the areas of evolutionary diversity optimization and evolutionary
multi-objective optimization. We show how popular indicators frequently used in
the area of multi-objective optimization can be used for evolutionary diversity
optimization. Our experimental investigations for evolving diverse sets of TSP
instances and images according to various features show that two of the most
prominent multi-objective indicators, namely the hypervolume indicator and the
inverted generational distance, provide excellent results in terms of
visualization and various diversity indicators.
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Authors: Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez
Date: 2018-11-16 00:00:00
Tasks: Sensitivity

We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate three key pillars of drug sensitivity, namely, the molecular structure of compounds, transcriptomic profiles of cancer cells as well as prior knowledge about interactions among proteins within cells. Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value. Gene expression profiles are encoded using an attention-based encoding mechanism that assigns high weights to the most informative genes. We present and study three encoders for SMILES string of compounds: 1) bidirectional recurrent 2) convolutional 3) attention-based encoders. We compare our devised models against a baseline model that ingests engineered fingerprints to represent the molecular structure. We demonstrate that using our attention-based encoders, we can surpass the baseline model. The use of attention-based encoders enhance interpretability and enable us to identify genes, bonds and atoms that were used by the network to make a prediction.
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks
Authors: Hengyue Pan, Hui Jiang, Xin Niu, Yong Dou
Date: 2018-11-16 00:00:00
Tasks: image-classification, Image Classification

The past few years have witnessed the fast development of different
regularization methods for deep learning models such as fully-connected deep
neural networks (DNNs) and Convolutional Neural Networks (CNNs). Most of
previous methods mainly consider to drop features from input data and hidden
layers, such as Dropout, Cutout and DropBlocks. DropConnect select to drop
connections between fully-connected layers. By randomly discard some features
or connections, the above mentioned methods control the overfitting problem and
improve the performance of neural networks. In this paper, we proposed two
novel regularization methods, namely DropFilter and DropFilter-PLUS, for the
learning of CNNs. Different from the previous methods, DropFilter and
DropFilter-PLUS selects to modify the convolution filters. For DropFilter-PLUS,
we find a suitable way to accelerate the learning process based on theoretical
analysis. Experimental results on MNIST show that using DropFilter and
DropFilter-PLUS may improve performance on image classification tasks.
A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding
Authors: Ashkan Esmaeili, Farokh Marvasti
Date: 2018-11-16 00:00:00

Sparse Inverse Covariance Estimation (SICE) is useful in many practical data
analyses. Recovering the connectivity, non-connectivity graph of covariates is
classified amongst the most important data mining and learning problems. In
this paper, we introduce a novel SICE approach using adaptive thresholding. Our
method is based on updates in a transformed domain of the desired matrix and
exponentially decaying adaptive thresholding in the main domain (Inverse
Covariance matrix domain). In addition to the proposed algorithm, the
convergence analysis is also provided. In the Numerical Experiments Section, we
show that the proposed method outperforms state-of-the-art methods in terms of
accuracy.
Entropy-regularized Optimal Transport Generative Models
Authors: Dong Liu, Minh Thành Vu, Saikat Chatterjee, Lars K. Rasmussen
Date: 2018-11-16 00:00:00
Tasks: Image Generation

We investigate the use of entropy-regularized optimal transport (EOT) cost in
developing generative models to learn implicit distributions. Two generative
models are proposed. One uses EOT cost directly in an one-shot optimization
problem and the other uses EOT cost iteratively in an adversarial game. The
proposed generative models show improved performance over contemporary models
for image generation on MNSIT.
Stochastic Adaptive Neural Architecture Search for Keyword Spotting
Authors: Tom Véniat, Olivier Schwander, Ludovic Denoyer
Date: 2018-11-16 00:00:00
Tasks: Keyword Spotting, Neural Architecture Search

The problem of keyword spotting i.e. identifying keywords in a real-time
audio stream is mainly solved by applying a neural network over successive
sliding windows. Due to the difficulty of the task, baseline models are usually
large, resulting in a high computational cost and energy consumption level. We
propose a new method called SANAS (Stochastic Adaptive Neural Architecture
Search) which is able to adapt the architecture of the neural network
on-the-fly at inference time such that small architectures will be used when
the stream is easy to process (silence, low noise, ...) and bigger networks
will be used when the task becomes more difficult. We show that this adaptive
model can be learned end-to-end by optimizing a trade-off between the
prediction performance and the average computational cost per unit of time.
Experiments on the Speech Commands dataset show that this approach leads to a
high recognition level while being much faster (and/or energy saving) than
classical approaches where the network architecture is static.
Machine Decisions and Human Consequences
Authors: Teresa Scantamburlo, Andrew Charlesworth, Nello Cristianini
Date: 2018-11-16 00:00:00
Tasks: Decision Making, Fairness

As we increasingly delegate decision-making to algorithms, whether directly
or indirectly, important questions emerge in circumstances where those
decisions have direct consequences for individual rights and personal
opportunities, as well as for the collective good. A key problem for
policymakers is that the social implications of these new methods can only be
grasped if there is an adequate comprehension of their general technical
underpinnings. The discussion here focuses primarily on the case of enforcement
decisions in the criminal justice system, but draws on similar situations
emerging from other algorithms utilised in controlling access to opportunities,
to explain how machine learning works and, as a result, how decisions are made
by modern intelligent algorithms or 'classifiers'. It examines the key aspects
of the performance of classifiers, including how classifiers learn, the fact
that they operate on the basis of correlation rather than causation, and that
the term 'bias' in machine learning has a different meaning to common usage. An
example of a real world 'classifier', the Harm Assessment Risk Tool (HART), is
examined, through identification of its technical features: the classification
method, the training data and the test data, the features and the labels,
validation and performance measures. Four normative benchmarks are then
considered by reference to HART: (a) prediction accuracy (b) fairness and
equality before the law (c) transparency and accountability (d) informational
privacy and freedom of expression, in order to demonstrate how its technical
features have important normative dimensions that bear directly on the extent
to which the system can be regarded as a viable and legitimate support for, or
even alternative to, existing human decision-makers.
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Authors: Chih-Hong Cheng, Chung-Hao Huang, Georg Nührenberg
Date: 2018-11-16 00:00:00
Tasks: Autonomous Driving

Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolbox to support safety engineering of neural networks for autonomous driving systems. The rationale behind nn-dependability-kit is to consider a structured approach (via Goal Structuring Notation) to argue the quality of neural networks. In particular, the tool realizes recent scientific results including (a) novel dependability metrics for indicating sufficient elimination of uncertainties in the product life cycle, (b) formal reasoning engine for ensuring that the generalization does not lead to undesired behaviors, and (c) runtime monitoring for reasoning whether a decision of a neural network in operation is supported by prior similarities in the training data. A proprietary version of nn-dependability-kit has been used to improve the quality of a level-3 autonomous driving component developed by Audi for highway maneuvers.
Learning Approximately Optimal Contracts
Authors: Alon Cohen, Moran Koren, Argyrios Deligkas
Date: 2018-11-16 00:00:00

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space: she sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal to obtain a contract that is within $\eps$ of her optimal net profit for every $\eps>0$. Our results are robust even when considering risk-averse agents. Furthermore, we show that when there are only two possible outcomes or the agent is risk-neutral, the algorithm's outcome approximates the optimal contract described in the classical theory.
Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization
Authors: Simon Leglaive, Laurent Girin, Radu Horaud
Date: 2018-11-16 00:00:00
Tasks: Speech Enhancement

In this paper we address speaker-independent multichannel speech enhancement
in unknown noisy environments. Our work is based on a well-established
multichannel local Gaussian modeling framework. We propose to use a neural
network for modeling the speech spectro-temporal content. The parameters of
this supervised model are learned using the framework of variational
autoencoders. The noisy recording environment is supposed to be unknown, so the
noise spectro-temporal modeling remains unsupervised and is based on
non-negative matrix factorization (NMF). We develop a Monte Carlo
expectation-maximization algorithm and we experimentally show that the proposed
approach outperforms its NMF-based counterpart, where speech is modeled using
supervised NMF.
An Algorithmic Perspective on Imitation Learning
Authors: Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters
Date: 2018-11-16 00:00:00
Tasks: Imitation Learning, Learning Theory, Reinforcement Learning

As robots and other intelligent agents move from simple environments and
problems to more complex, unstructured settings, manually programming their
behavior has become increasingly challenging and expensive. Often, it is easier
for a teacher to demonstrate a desired behavior rather than attempt to manually
engineer it. This process of learning from demonstrations, and the study of
algorithms to do so, is called imitation learning. This work provides an
introduction to imitation learning. It covers the underlying assumptions,
approaches, and how they relate; the rich set of algorithms developed to tackle
the problem; and advice on effective tools and implementation.
  We intend this paper to serve two audiences. First, we want to familiarize
machine learning experts with the challenges of imitation learning,
particularly those arising in robotics, and the interesting theoretical and
practical distinctions between it and more familiar frameworks like statistical
supervised learning theory and reinforcement learning. Second, we want to give
roboticists and experts in applied artificial intelligence a broader
appreciation for the frameworks and tools available for imitation learning.
DeRPN: Taking a further step toward more general object detection
Authors: Lele Xie, Yuliang Liu, Lianwen Jin, Zecheng Xie
Date: 2018-11-16 00:00:00
Tasks: Object, object-detection, Object Detection, Region Proposal, Scene Text Detection, Text Detection

Most current detection methods have adopted anchor boxes as regression
references. However, the detection performance is sensitive to the setting of
the anchor boxes. A proper setting of anchor boxes may vary significantly
across different datasets, which severely limits the universality of the
detectors. To improve the adaptivity of the detectors, in this paper, we
present a novel dimension-decomposition region proposal network (DeRPN) that
can perfectly displace the traditional Region Proposal Network (RPN). DeRPN
utilizes an anchor string mechanism to independently match object widths and
heights, which is conducive to treating variant object shapes. In addition, a
novel scale-sensitive loss is designed to address the imbalanced loss
computations of different scaled objects, which can avoid the small objects
being overwhelmed by larger ones. Comprehensive experiments conducted on both
general object detection datasets (Pascal VOC 2007, 2012 and MS COCO) and scene
text detection datasets (ICDAR 2013 and COCO-Text) all prove that our DeRPN can
significantly outperform RPN. It is worth mentioning that the proposed DeRPN
can be employed directly on different models, tasks, and datasets without any
modifications of hyperparameters or specialized optimization, which further
demonstrates its adaptivity. The code will be released at
https://github.com/HCIILAB/DeRPN.
Subtask Gated Networks for Non-Intrusive Load Monitoring
Authors: Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, Wonjong Rhee
Date: 2018-11-16 00:00:00
Tasks: blind source separation, General Classification, Non-Intrusive Load Monitoring, regression

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is
a blind source separation problem where a household's aggregate electricity
consumption is broken down into electricity usages of individual appliances. In
this way, the cost and trouble of installing many measurement devices over
numerous household appliances can be avoided, and only one device needs to be
installed. The problem has been well-known since Hart's seminal paper in 1992,
and recently significant performance improvements have been achieved by
adopting deep networks. In this work, we focus on the idea that appliances have
on/off states, and develop a deep network for further performance improvements.
Specifically, we propose a subtask gated network that combines the main
regression network with an on/off classification subtask network. Unlike
typical multitask learning algorithms where multiple tasks simply share the
network parameters to take advantage of the relevance among tasks, the subtask
gated network multiply the main network's regression output with the subtask's
classification probability. When standby-power is additionally learned, the
proposed solution surpasses the state-of-the-art performance for most of the
benchmark cases. The subtask gated network can be very effective for any
problem that inherently has on/off states.
Deep Knockoffs
Authors: Yaniv Romano, Matteo Sesia, Emmanuel J. Candès
Date: 2018-11-16 00:00:00
Tasks: Variable Selection

This paper introduces a machine for sampling approximate model-X knockoffs
for arbitrary and unspecified data distributions using deep generative models.
The main idea is to iteratively refine a knockoff sampling mechanism until a
criterion measuring the validity of the produced knockoffs is optimized; this
criterion is inspired by the popular maximum mean discrepancy in machine
learning and can be thought of as measuring the distance to pairwise
exchangeability between original and knockoff features. By building upon the
existing model-X framework, we thus obtain a flexible and model-free
statistical tool to perform controlled variable selection. Extensive numerical
experiments and quantitative tests confirm the generality, effectiveness, and
power of our deep knockoff machines. Finally, we apply this new method to a
real study of mutations linked to changes in drug resistance in the human
immunodeficiency virus.
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images
Authors: Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Nam Ling
Date: 2018-11-16 00:00:00
Tasks: Co-Salient Object Detection, Saliency Detection

Co-saliency detection aims to discover common and salient objects in an image
group containing more than two relevant images. Moreover, depth information has
been demonstrated to be effective for many computer vision tasks. In this
paper, we propose a novel co-saliency detection method for RGBD images based on
hierarchical sparsity reconstruction and energy function refinement. With the
assistance of the intra saliency map, the inter-image correspondence is
formulated as a hierarchical sparsity reconstruction framework. The global
sparsity reconstruction model with a ranking scheme focuses on capturing the
global characteristics among the whole image group through a common foreground
dictionary. The pairwise sparsity reconstruction model aims to explore the
corresponding relationship between pairwise images through a set of pairwise
dictionaries. In order to improve the intra-image smoothness and inter-image
consistency, an energy function refinement model is proposed, which includes
the unary data term, spatial smooth term, and holistic consistency term.
Experiments on two RGBD co-saliency detection benchmarks demonstrate that the
proposed method outperforms the state-of-the-art algorithms both qualitatively
and quantitatively.