pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Noisy Evolutionary Optimization When Sampling Fails
Authors: Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao
Date: 2018-10-11 00:00:00

In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{sample size}) independently, and its true fitness is then approximated by the average of these evaluations. Most previous studies on sampling are empirical, and the few theoretical studies mainly showed the effectiveness of sampling with a sufficiently large sample size. In this paper, we theoretically examine what strategies can work when sampling with any fixed sample size fails. By constructing a family of artificial noisy examples, we prove that sampling is always ineffective, while using parent or offspring populations can be helpful on some examples. We also construct an artificial noisy example to show that when using neither sampling nor populations is effective, a tailored adaptive sampling (i.e., sampling with an adaptive sample size) strategy can work. These findings may enhance our understanding of sampling to some extent, but future work is required to validate them in natural situations.
Semantic Structural Evaluation for Text Simplification
Authors: Elior Sulem, Omri Abend, Ari Rappoport
Date: 2018-10-11 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Parsing, Sentence, Text Simplification, valid

Current measures for evaluating text simplification systems focus on
evaluating lexical text aspects, neglecting its structural aspects. In this
paper we propose the first measure to address structural aspects of text
simplification, called SAMSA. It leverages recent advances in semantic parsing
to assess simplification quality by decomposing the input based on its semantic
structure and comparing it to the output. SAMSA provides a reference-less
automatic evaluation procedure, avoiding the problems that reference-based
methods face due to the vast space of valid simplifications for a given
sentence. Our human evaluation experiments show both SAMSA's substantial
correlation with human judgments, as well as the deficiency of existing
reference-based measures in evaluating structural simplification.
Multi-Strategy Coevolving Aging Particle Optimization
Authors: Giovanni Iacca, Fabio Caraffini, Ferrante Neri
Date: 2018-10-11 00:00:00

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel
population-based algorithm for black-box optimization. In a memetic fashion,
MS-CAP combines two components with complementary algorithm logics. In the
first stage, each particle is perturbed independently along each dimension with
a progressively shrinking (decaying) radius, and attracted towards the current
best solution with an increasing force. In the second phase, the particles are
mutated and recombined according to a multi-strategy approach in the fashion of
the ensemble of mutation strategies in Differential Evolution. The proposed
algorithm is tested, at different dimensionalities, on two complete black-box
optimization benchmarks proposed at the Congress on Evolutionary Computation
2010 and 2013. To demonstrate the applicability of the approach, we also test
MS-CAP to train a Feedforward Neural Network modelling the kinematics of an
8-link robot manipulator. The numerical results show that MS-CAP, for the
setting considered in this study, tends to outperform the state-of-the-art
optimization algorithms on a large set of problems, thus resulting in a robust
and versatile optimizer.
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Authors: Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas
Date: 2018-10-11 00:00:00
Proceeding: ICLR 2019 5

Humans are experts at high-fidelity imitation -- closely mimicking a
demonstration, often in one attempt. Humans use this ability to quickly solve a
task instance, and to bootstrap learning of new tasks. Achieving these
abilities in autonomous agents is an open problem. In this paper, we introduce
an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn
both (i) policies for high-fidelity one-shot imitation of diverse novel skills,
and (ii) policies that enable the agent to solve tasks more efficiently than
the demonstrators. MetaMimic relies on the principle of storing all experiences
in a memory and replaying these to learn massive deep neural network policies
by off-policy RL. This paper introduces, to the best of our knowledge, the
largest existing neural networks for deep RL and shows that larger networks
with normalization are needed to achieve one-shot high-fidelity imitation on a
challenging manipulation task. The results also show that both types of policy
can be learned from vision, in spite of the task rewards being sparse, and
without access to demonstrator actions.
ISA$^2$: Intelligent Speed Adaptation from Appearance
Authors: Carlos Herranz-Perdiguero, Roberto J. López-Sastre
Date: 2018-10-11 00:00:00
Tasks: regression

In this work we introduce a new problem named Intelligent Speed Adaptation
from Appearance (ISA$^2$). Technically, the goal of an ISA$^2$ model is to
predict for a given image of a driving scenario the proper speed of the
vehicle. Note this problem is different from predicting the actual speed of the
vehicle. It defines a novel regression problem where the appearance information
has to be directly mapped to get a prediction for the speed at which the
vehicle should go, taking into account the traffic situation. First, we release
a novel dataset for the new problem, where multiple driving video sequences,
with the annotated adequate speed per frame, are provided. We then introduce
two deep learning based ISA$^2$ models, which are trained to perform the final
regression of the proper speed given a test image. We end with a thorough
experimental validation where the results show the level of difficulty of the
proposed task. The dataset and the proposed models will all be made publicly
available to encourage much needed further research on this problem.
A Simple Way to Deal with Cherry-picking
Authors: Junpei Komiyama, Takanori Maehara
Date: 2018-10-11 00:00:00
Tasks: Selection bias, Two-sample testing

Statistical hypothesis testing serves as statistical evidence for scientific
innovation. However, if the reported results are intentionally biased,
hypothesis testing no longer controls the rate of false discovery. In
particular, we study such selection bias in machine learning models where the
reporter is motivated to promote an algorithmic innovation. When the number of
possible configurations (e.g., datasets) is large, we show that the reporter
can falsely report an innovation even if there is no improvement at all. We
propose a `post-reporting' solution to this issue where the bias of the
reported results is verified by another set of results. The theoretical
findings are supported by experimental results with synthetic and real-world
datasets.
SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
Authors: Xiaoming Yu, Xing Cai, Zhenqiang Ying, Thomas Li, Ge Li
Date: 2018-10-11 00:00:00
Tasks: Image-to-Image Translation, Translation

Image translation is a burgeoning field in computer vision where the goal is to learn the mapping between an input image and an output image. However, most recent methods require multiple generators for modeling different domain mappings, which are inefficient and ineffective on some multi-domain image translation tasks. In this paper, we propose a novel method, SingleGAN, to perform multi-domain image-to-image translations with a single generator. We introduce the domain code to explicitly control the different generative tasks and integrate multiple optimization goals to ensure the translation. Experimental results on several unpaired datasets show superior performance of our model in translation between two domains. Besides, we explore variants of SingleGAN for different tasks, including one-to-many domain translation, many-to-many domain translation and one-to-one domain translation with multimodality. The extended experiments show the universality and extensibility of our model.
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
Authors: Letizia Marchegiani, Paul Newman
Date: 2018-10-11 00:00:00
Tasks: Denoising, Event Detection, General Classification, Multi-Task Learning, Semantic Segmentation

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.
The persistence landscape and some of its properties
Authors: Peter Bubenik
Date: 2018-10-11 00:00:00

Persistence landscapes map persistence diagrams into a function space, which
may often be taken to be a Banach space or even a Hilbert space. In the latter
case, it is a feature map and there is an associated kernel. The main advantage
of this summary is that it allows one to apply tools from statistics and
machine learning. Furthermore, the mapping from persistence diagrams to
persistence landscapes is stable and invertible. We introduce a weighted
version of the persistence landscape and define a one-parameter family of
Poisson-weighted persistence landscape kernels that may be useful for learning.
We also demonstrate some additional properties of the persistence landscape.
First, the persistence landscape may be viewed as a tropical rational function.
Second, in many cases it is possible to exactly reconstruct all of the
component persistence diagrams from an average persistence landscape. It
follows that the persistence landscape kernel is characteristic for certain
generic empirical measures. Finally, the persistence landscape distance may be
arbitrarily small compared to the interleaving distance.
Globally Continuous and Non-Markovian Activity Analysis from Videos
Authors: He Wang, Carol O'Sullivan
Date: 2018-10-11 00:00:00

Automatically recognizing activities in video is a classic problem in vision
and helps to understand behaviors, describe scenes and detect anomalies. We
propose an unsupervised method for such purposes. Given video data, we discover
recurring activity patterns that appear, peak, wane and disappear over time. By
using non-parametric Bayesian methods, we learn coupled spatial and temporal
patterns with minimum prior knowledge. To model the temporal changes of
patterns, previous works compute Markovian progressions or locally continuous
motifs whereas we model time in a globally continuous and non-Markovian way.
Visually, the patterns depict flows of major activities. Temporally, each
pattern has its own unique appearance-disappearance cycles. To compute compact
pattern representations, we also propose a hybrid sampling method. By combining
these patterns with detailed environment information, we interpret the
semantics of activities and report anomalies. Also, our method fits data better
and detects anomalies that were difficult to detect previously.
Interactive Cognitive Assessment Tools: A Case Study on Digital Pens for the Clinical Assessment of Dementia
Authors: Daniel Sonntag
Date: 2018-10-11 00:00:00

Interactive cognitive assessment tools may be valuable for doctors and
therapists to reduce costs and improve quality in healthcare systems. Use cases
and scenarios include the assessment of dementia. In this paper, we present our
approach to the semi-automatic assessment of dementia. We describe a case study
with digital pens for the patients including background, problem description
and possible solutions. We conclude with lessons learned when implementing
digital tests, and a generalisation for use outside the cognitive impairments
field.
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Authors: Hafez Farazi, Sven Behnke
Date: 2018-10-11 00:00:00

Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.
Location Dependency in Video Prediction
Authors: Niloofar Azizi, Hafez Farazi, Sven Behnke
Date: 2018-10-11 00:00:00
Tasks: Prediction, Video Prediction

Deep convolutional neural networks are used to address many computer vision
problems, including video prediction. The task of video prediction requires
analyzing the video frames, temporally and spatially, and constructing a model
of how the environment evolves. Convolutional neural networks are spatially
invariant, though, which prevents them from modeling location-dependent
patterns. In this work, the authors propose location-biased convolutional
layers to overcome this limitation. The effectiveness of location bias is
evaluated on two architectures: Video Ladder Network (VLN) and Convolutional
redictive Gating Pyramid (Conv-PGP). The results indicate that encoding
location-dependent features is crucial for the task of video prediction. Our
proposed methods significantly outperform spatially invariant models.
VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video
Authors: Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen
Date: 2018-10-11 00:00:00
Tasks: Representation Learning

Heart rate (HR) is an important physiological signal that reflects the
physical and emotional activities of humans. Traditional HR measurements are
mainly based on contact monitors, which are inconvenient and may cause
discomfort for the subjects. Recently, methods have been proposed for remote HR
estimation from face videos. However, most of the existing methods focus on
well-controlled scenarios, their generalization ability into less-constrained
scenarios are not known. At the same time, lacking large-scale databases has
limited the use of deep representation learning methods in remote HR
estimation. In this paper, we introduce a large-scale multi-modal HR database
(named as VIPL-HR), which contains 2,378 visible light videos (VIS) and 752
near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database also contains
various variations such as head movements, illumination variations, and
acquisition device changes. We also learn a deep HR estimator (named as
RhythmNet) with the proposed spatial-temporal representation, which achieves
promising results on both the public-domain and our VIPL-HR HR estimation
databases. We would like to put the VIPL-HR database into the public domain.
Pairwise Augmented GANs with Adversarial Reconstruction Loss
Authors: Aibek Alanov, Max Kochurov, Daniil Yashkov, Dmitry Vetrov
Date: 2018-10-11 00:00:00
Proceeding: ICLR 2019 5

We propose a novel autoencoding model called Pairwise Augmented GANs. We
train a generator and an encoder jointly and in an adversarial manner. The
generator network learns to sample realistic objects. In turn, the encoder
network at the same time is trained to map the true data distribution to the
prior in latent space. To ensure good reconstructions, we introduce an
augmented adversarial reconstruction loss. Here we train a discriminator to
distinguish two types of pairs: an object with its augmentation and the one
with its reconstruction. We show that such adversarial loss compares objects
based on the content rather than on the exact match. We experimentally
demonstrate that our model generates samples and reconstructions of quality
competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and
achieves good quantitative results on CIFAR10.
MOANOFS: Multi-Objective Automated Negotiation based Online Feature Selection System for Big Data Classification
Authors: Fatma BenSaid, Adel M. Alimi
Date: 2018-10-11 00:00:00
Tasks: Binary Classification, Decision Making, feature selection, General Classification

Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when data arrive sequentially. In this paper, we propose an online feature selection system which resolves this problem. More specifically, we treat the problem of online supervised feature selection for binary classification as a decision-making problem. A philosophical vision to this problem leads to a hybridization between two important domains: feature selection using online learning technique (OFS) and automated negotiation (AN). The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation based Online Feature Selection) uses two levels of decision. In the first level, from n learners (or OFS methods), we decide which are the k trustful ones (with high confidence or trust value). These elected k learners will participate in the second level. In this level, we integrate our proposed Multilateral Automated Negotiation based OFS (MANOFS) method to decide finally which is the best solution or which are relevant features. We show that MOANOFS system is applicable to different domains successfully and achieves high accuracy with several real-world applications. Index Terms: Feature selection, online learning, multi-objective automated negotiation, trust, classification, big data.
Perfusion parameter estimation using neural networks and data augmentation
Authors: David Robben, Paul Suetens
Date: 2018-10-11 00:00:00
Tasks: Data Augmentation, Decision Making, parameter estimation

Perfusion imaging plays a crucial role in acute stroke diagnosis and
treatment decision making. Current perfusion analysis relies on deconvolution
of the measured signals, an operation that is mathematically ill-conditioned
and requires strong regularization. We propose a neural network and a data
augmentation approach to predict perfusion parameters directly from the native
measurements. A comparison on simulated CT Perfusion data shows that the neural
network provides better estimations for both CBF and Tmax than a state of the
art deconvolution method, and this over a wide range of noise levels. The
proposed data augmentation enables to achieve these results with less than 100
datasets.
Automata for Infinite Argumentation Structures
Authors: Pietro Baroni, Federico Cerutti, Paul E. Dunne, Massimiliano Giacomin
Date: 2018-10-11 00:00:00
Tasks: Abstract Argumentation

The theory of abstract argumentation frameworks (afs) has, in the main,
focused on finite structures, though there are many significant contexts where
argumentation can be regarded as a process involving infinite objects. To
address this limitation, in this paper we propose a novel approach for
describing infinite afs using tools from formal language theory. In particular,
the possibly infinite set of arguments is specified through the language
recognized by a deterministic finite automaton while a suitable formalism,
called attack expression, is introduced to describe the relation of attack
between arguments. The proposed approach is shown to satisfy some desirable
properties which can not be achieved through other "naive" uses of formal
languages. In particular, the approach is shown to be expressive enough to
capture (besides any arbitrary finite structure) a large variety of infinite
afs including two major examples from previous literature and two sample cases
from the domains of multi-agent negotiation and ambient intelligence. On the
computational side, we show that several decision and construction problems
which are known to be polynomial time solvable in finite afs are decidable in
the context of the proposed formalism and we provide the relevant algorithms.
Moreover we obtain additional results concerning the case of finitary afs.
Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations
Authors: Lan Hu, Yuchen Cao, Peng Wu, Laurent Kneip
Date: 2018-10-11 00:00:00
Tasks: Object Reconstruction, Simultaneous Localization and Mapping

Most problems involving simultaneous localization and mapping can nowadays be
solved using one of two fundamentally different approaches. The traditional
approach is given by a least-squares objective, which minimizes many local
photometric or geometric residuals over explicitly parametrized structure and
camera parameters. Unmodeled effects violating the lambertian surface
assumption or geometric invariances of individual residuals are encountered
through statistical averaging or the addition of robust kernels and smoothness
terms. Aiming at more accurate measurement models and the inclusion of
higher-order shape priors, the community more recently shifted its attention to
deep end-to-end models for solving geometric localization and mapping problems.
However, at test-time, these feed-forward models ignore the more traditional
geometric or photometric consistency terms, thus leading to a low ability to
recover fine details and potentially complete failure in corner case scenarios.
With an application to dense object modeling from RGBD images, our work aims at
taking the best of both worlds by embedding modern higher-order object shape
priors into classical iterative residual minimization objectives. We
demonstrate a general ability to improve mapping accuracy with respect to each
modality alone, and present a successful application to real data.
AFRA: Argumentation framework with recursive attacks
Authors: Pietro Baroni, Federico Cerutti, Massimiliano Giacomin, Giovanni Guida
Date: 2018-10-11 00:00:00

The issue of representing attacks to attacks in argumentation is receiving an
increasing attention as a useful conceptual modelling tool in several contexts.
In this paper we present AFRA, a formalism encompassing unlimited recursive
attacks within argumentation frameworks. AFRA satisfies the basic requirements
of definition simplicity and rigorous compatibility with Dung's theory of
argumentation. This paper provides a complete development of the AFRA formalism
complemented by illustrative examples and a detailed comparison with other
recursive attack formalizations.