pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Robustness Toolbox v1.0.0
Authors: Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, Ben Edwards
Date: 2018-07-03 00:00:00
Tasks: Adversarial Robustness, BIG-bench Machine Learning, Gaussian Processes, Time Series Analysis

Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, Gaussian Processes, Decision Trees, Scikit-learn Pipelines, etc.) against adversarial threats and helps making AI systems more secure and trustworthy. Machine Learning models are vulnerable to adversarial examples, which are inputs (images, texts, tabular data, etc.) deliberately modified to produce a desired response by the Machine Learning model. ART provides the tools to build and deploy defences and test them with adversarial attacks. Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary. The attacks implemented in ART allow creating adversarial attacks against Machine Learning models which is required to test defenses with state-of-the-art threat models. Supported Machine Learning Libraries include TensorFlow (v1 and v2), Keras, PyTorch, MXNet, Scikit-learn, XGBoost, LightGBM, CatBoost, and GPy. The source code of ART is released with MIT license at https://github.com/IBM/adversarial-robustness-toolbox. The release includes code examples, notebooks with tutorials and documentation (http://adversarial-robustness-toolbox.readthedocs.io).
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
Authors: Marco Reisert, Volker A. Coenen, Christoph Kaller, Karl Egger, Henrik Skibbe
Date: 2018-07-03 00:00:00
Tasks: Data Augmentation, Diffusion  MRI, tensor algebra, Translation

In this work we propose HAMLET, a novel tract learning algorithm, which,
after training, maps raw diffusion weighted MRI directly onto an image which
simultaneously indicates tract direction and tract presence. The automatic
learning of fiber tracts based on diffusion MRI data is a rather new idea,
which tries to overcome limitations of atlas-based techniques. HAMLET takes a
such an approach. Unlike the current trend in machine learning, HAMLET has only
a small number of free parameters HAMLET is based on spherical tensor algebra
which allows a translation and rotation covariant treatment of the problem.
HAMLET is based on a repeated application of convolutions and non-linearities,
which all respect the rotation covariance. The intrinsic treatment of such
basic image transformations in HAMLET allows the training and generalization of
the algorithm without any additional data augmentation. We demonstrate the
performance of our approach for twelve prominent bundles, and show that the
obtained tract estimates are robust and reliable. It is also shown that the
learned models are portable from one sequence to another.
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
Authors: Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill
Date: 2018-07-03 00:00:00

In this work, we consider the problem of estimating a behaviour policy for
use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is
unknown. Via a series of empirical studies, we demonstrate how accurate OPE is
strongly dependent on the calibration of estimated behaviour policy models: how
precisely the behaviour policy is estimated from data. We show how powerful
parametric models such as neural networks can result in highly uncalibrated
behaviour policy models on a real-world medical dataset, and illustrate how a
simple, non-parametric, k-nearest neighbours model produces better calibrated
behaviour policy estimates and can be used to obtain superior importance
sampling-based OPE estimates.
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Authors: Haitao Liu, Yew-Soon Ong, Xiaobo Shen, Jianfei Cai
Date: 2018-07-03 00:00:00

The vast quantity of information brought by big data as well as the evolving
computer hardware encourages success stories in the machine learning community.
In the meanwhile, it poses challenges for the Gaussian process (GP) regression,
a well-known non-parametric and interpretable Bayesian model, which suffers
from cubic complexity to data size. To improve the scalability while retaining
desirable prediction quality, a variety of scalable GPs have been presented.
But they have not yet been comprehensively reviewed and analyzed in order to be
well understood by both academia and industry. The review of scalable GPs in
the GP community is timely and important due to the explosion of data size. To
this end, this paper is devoted to the review on state-of-the-art scalable GPs
involving two main categories: global approximations which distillate the
entire data and local approximations which divide the data for subspace
learning. Particularly, for global approximations, we mainly focus on sparse
approximations comprising prior approximations which modify the prior but
perform exact inference, posterior approximations which retain exact prior but
perform approximate inference, and structured sparse approximations which
exploit specific structures in kernel matrix; for local approximations, we
highlight the mixture/product of experts that conducts model averaging from
multiple local experts to boost predictions. To present a complete review,
recent advances for improving the scalability and capability of scalable GPs
are reviewed. Finally, the extensions and open issues regarding the
implementation of scalable GPs in various scenarios are reviewed and discussed
to inspire novel ideas for future research avenues.
MediaEval 2018: Predicting Media Memorability Task
Authors: Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do, France Rennes
Date: 2018-07-03 00:00:00
Tasks: Benchmarking, Memorization

In this paper, we present the Predicting Media Memorability task, which is
proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia
Evaluation. Participants are expected to design systems that automatically
predict memorability scores for videos, which reflect the probability of a
video being remembered. In contrast to previous work in image memorability
prediction, where memorability was measured a few minutes after memorization,
the proposed dataset comes with short-term and long-term memorability
annotations. All task characteristics are described, namely: the task's
challenges and breakthrough, the released data set and ground truth, the
required participant runs and the evaluation metrics.
Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
Authors: Manfred Eppe, Matthias Kerzel, Erik Strahl, Stefan Wermter
Date: 2018-07-03 00:00:00
Tasks: General Classification, Material Classification

We present a novel approach for interactive auditory object analysis with a
humanoid robot. The robot elicits sensory information by physically shaking
visually indistinguishable plastic capsules. It gathers the resulting audio
signals from microphones that are embedded into the robotic ears. A neural
network architecture learns from these signals to analyze properties of the
contents of the containers. Specifically, we evaluate the material
classification and weight prediction accuracy and demonstrate that the
framework is fairly robust to acoustic real-world noise.
Kitting in the Wild through Online Domain Adaptation
Authors: Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo
Date: 2018-07-03 00:00:00
Tasks: Domain Adaptation, Object Recognition, Online Domain Adaptation

Technological developments call for increasing perception and action
capabilities of robots. Among other skills, vision systems that can adapt to
any possible change in the working conditions are needed. Since these
conditions are unpredictable, we need benchmarks which allow to assess the
generalization and robustness capabilities of our visual recognition
algorithms. In this work we focus on robotic kitting in unconstrained
scenarios. As a first contribution, we present a new visual dataset for the
kitting task. Differently from standard object recognition datasets, we provide
images of the same objects acquired under various conditions where camera,
illumination and background are changed. This novel dataset allows for testing
the robustness of robot visual recognition algorithms to a series of different
domain shifts both in isolation and unified. Our second contribution is a novel
online adaptation algorithm for deep models, based on batch-normalization
layers, which allows to continuously adapt a model to the current working
conditions. Differently from standard domain adaptation algorithms, it does not
require any image from the target domain at training time. We benchmark the
performance of the algorithm on the proposed dataset, showing its capability to
fill the gap between the performances of a standard architecture and its
counterpart adapted offline to the given target domain.
Deep Architectures and Ensembles for Semantic Video Classification
Authors: Eng-Jon Ong, Sameed Husain, Mikel Bober-Irizar, Miroslaw Bober
Date: 2018-07-03 00:00:00
Tasks: Classification, Diversity, General Classification, Video Classification

This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features.
Coopetitive Soft Gating Ensemble
Authors: Stephan Deist, Maarten Bieshaar, Jens Schreiber, Andre Gensler, Bernhard Sick
Date: 2018-07-03 00:00:00
Tasks: BIG-bench Machine Learning

In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for
general machine learning tasks and interwoven systems. The goal of machine
learning is to create models that generalize well for unknown datasets. Often,
however, the problems are too complex to be solved with a single model, so
several models are combined. Similar, Autonomic Computing requires the
integration of different systems. Here, especially, the local, temporal online
evaluation and the resulting (re-)weighting scheme of the CSGE makes the
approach highly applicable for self-improving system integrations. To achieve
the best potential performance the CSGE can be optimized according to arbitrary
loss functions making it accessible for a broader range of problems. We
introduce a novel training procedure including a hyper-parameter initialisation
at its heart. We show that the CSGE approach reaches state-of-the-art
performance for both classification and regression tasks. Further on, the CSGE
provides a human-readable quantification on the influence of all base
estimators employing the three weighting aspects. Moreover, we provide a
scikit-learn compatible implementation.
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming
Authors: Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein
Date: 2018-07-03 00:00:00
Tasks: Symbolic Regression

Surrogate models are a well established approach to reduce the number of
expensive function evaluations in continuous optimization. In the context of
genetic programming, surrogate modeling still poses a challenge, due to the
complex genotype-phenotype relationships. We investigate how different
genotypic and phenotypic distance measures can be used to learn Kriging models
as surrogates. We compare the measures and suggest to use their linear
combination in a kernel.
  We test the resulting model in an optimization framework, using symbolic
regression problem instances as a benchmark. Our experiments show that the
model provides valuable information. Firstly, the model enables an improved
optimization performance compared to a model-free algorithm. Furthermore, the
model provides information on the contribution of different distance measures.
The data indicates that a phenotypic distance measure is important during the
early stages of an optimization run when less data is available. In contrast,
genotypic measures, such as the tree edit distance, contribute more during the
later stages.
Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain
Authors: Laxmi R. Iyer, Yansong Chua, Haizhou Li
Date: 2018-07-03 00:00:00

The advantage of spiking neural networks (SNNs) over their predecessors is
their ability to spike, enabling them to use spike timing for coding and
efficient computing. A neuromorphic dataset should allow a neuromorphic
algorithm to clearly show that a SNN is able to perform better on the dataset
than an ANN. We have analyzed both N-MNIST and N-Caltech101 along these lines,
but focus our study on N-MNIST. First we evaluate if additional information is
encoded in the time domain in a neuromoprhic dataset. We show that an ANN
trained with backpropagation on frame based versions of N-MNIST and
N-Caltech101 images achieve 99.23% and 78.01% accuracy. These are the best
classification accuracies obtained on these datasets to date. Second we present
the first unsupervised SNN to be trained on N-MNIST and demonstrate results of
91.78%. We also use this SNN for further experiments on N-MNIST to show that
rate based SNNs perform better, and precise spike timings are not important in
N-MNIST. N-MNIST does not, therefore, highlight the unique ability of SNNs. The
conclusion of this study opens an important question in neuromorphic
engineering - what, then, constitutes a good neuromorphic dataset?
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Authors: Martin Zaefferer, Daniel Horn
Date: 2018-07-03 00:00:00
Tasks: Evolutionary Algorithms

Many real-world optimization problems require significant resources for
objective function evaluations. This is a challenge to evolutionary algorithms,
as it limits the number of available evaluations. One solution are surrogate
models, which replace the expensive objective. A particular issue in this
context are hierarchical variables. Hierarchical variables only influence the
objective function if other variables satisfy some condition. We study how this
kind of hierarchical structure can be integrated into the model based
optimization framework. We discuss an existing kernel and propose alternatives.
An artificial test function is used to investigate how different kernels and
assumptions affect model quality and search performance.
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs
Authors: Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixé
Date: 2018-07-03 00:00:00
Tasks: Generative Adversarial Network, Self-Driving Cars

Even though end-to-end supervised learning has shown promising results for
sensorimotor control of self-driving cars, its performance is greatly affected
by the weather conditions under which it was trained, showing poor
generalization to unseen conditions. In this paper, we show how knowledge can
be transferred using semantic maps to new weather conditions without the need
to obtain new ground truth data. To this end, we propose to divide the task of
vehicle control into two independent modules: a control module which is only
trained on one weather condition for which labeled steering data is available,
and a perception module which is used as an interface between new weather
conditions and the fixed control module. To generate the semantic data needed
to train the perception module, we propose to use a generative adversarial
network (GAN)-based model to retrieve the semantic information for the new
conditions in an unsupervised manner. We introduce a master-servant
architecture, where the master model (semantic labels available) trains the
servant model (semantic labels not available). We show that our proposed method
trained with ground truth data for a single weather condition is capable of
achieving similar results on the task of steering angle prediction as an
end-to-end model trained with ground truth data of 15 different weather
conditions.
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimation
Authors: Bin Wang, Zhijian Ou
Date: 2018-07-03 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

A new whole-sentence language model - neural trans-dimensional random field
language model (neural TRF LM), where sentences are modeled as a collection of
random fields, and the potential function is defined by a neural network, has
been introduced and successfully trained by noise-contrastive estimation (NCE).
In this paper, we extend NCE and propose dynamic noise-contrastive estimation
(DNCE) to solve the two problems observed in NCE training. First, a dynamic
noise distribution is introduced and trained simultaneously to converge to the
data distribution. This helps to significantly cut down the noise sample number
used in NCE and reduce the training cost. Second, DNCE discriminates between
sentences generated from the noise distribution and sentences generated from
the interpolation of the data distribution and the noise distribution. This
alleviates the overfitting problem caused by the sparseness of the training
set. With DNCE, we can successfully and efficiently train neural TRF LMs on
large corpus (about 0.8 billion words) with large vocabulary (about 568 K
words). Neural TRF LMs perform as good as LSTM LMs with less parameters and
being 5x~114x faster in rescoring sentences. Interpolating neural TRF LMs with
LSTM LMs and n-gram LMs can further reduce the error rates.
Long Activity Video Understanding using Functional Object-Oriented Network
Authors: Ahmad Babaeian Jelodar, David Paulius, Yu Sun
Date: 2018-07-03 00:00:00
Tasks: Object, Video Understanding

Video understanding is one of the most challenging topics in computer vision.
In this paper, a four-stage video understanding pipeline is presented to
simultaneously recognize all atomic actions and the single on-going activity in
a video. This pipeline uses objects and motions from the video and a
graph-based knowledge representation network as prior reference. Two deep
networks are trained to identify objects and motions in each video sequence
associated with an action. Low Level image features are then used to identify
objects of interest in that video sequence. Confidence scores are assigned to
objects of interest based on their involvement in the action and to motion
classes based on results from a deep neural network that classifies the
on-going action in video into motion classes. Confidence scores are computed
for each candidate functional unit associated with an action using a knowledge
representation network, object confidences, and motion confidences. Each action
is therefore associated with a functional unit and the sequence of actions is
further evaluated to identify the single on-going activity in the video. The
knowledge representation used in the pipeline is called the functional
object-oriented network which is a graph-based network useful for encoding
knowledge about manipulation tasks. Experiments are performed on a dataset of
cooking videos to test the proposed algorithm with action inference and
activity classification. Experiments show that using functional object oriented
network improves video understanding significantly.
Learning concise representations for regression by evolving networks of trees
Authors: William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore
Date: 2018-07-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Disentanglement, regression, Stochastic Optimization

We propose and study a method for learning interpretable representations for
the task of regression. Features are represented as networks of multi-type
expression trees comprised of activation functions common in neural networks in
addition to other elementary functions. Differentiable features are trained via
gradient descent, and the performance of features in a linear model is used to
weight the rate of change among subcomponents of each representation. The
search process maintains an archive of representations with accuracy-complexity
trade-offs to assist in generalization and interpretation. We compare several
stochastic optimization approaches within this framework. We benchmark these
variants on 100 open-source regression problems in comparison to
state-of-the-art machine learning approaches. Our main finding is that this
approach produces the highest average test scores across problems while
producing representations that are orders of magnitude smaller than the next
best performing method (gradient boosting). We also report a negative result in
which attempts to directly optimize the disentanglement of the representation
result in more highly correlated features.
MetaAnchor: Learning to Detect Objects with Customized Anchors
Authors: Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun
Date: 2018-07-03 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object, object-detection, Object Detection

We propose a novel and flexible anchor mechanism named MetaAnchor for object
detection frameworks. Unlike many previous detectors model anchors via a
predefined manner, in MetaAnchor anchor functions could be dynamically
generated from the arbitrary customized prior boxes. Taking advantage of weight
prediction, MetaAnchor is able to work with most of the anchor-based object
detection systems such as RetinaNet. Compared with the predefined anchor
scheme, we empirically find that MetaAnchor is more robust to anchor settings
and bounding box distributions; in addition, it also shows the potential on
transfer tasks. Our experiment on COCO detection task shows that MetaAnchor
consistently outperforms the counterparts in various scenarios.