pwc-archive/papers-with-abstracts huggingface dataset:
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
Authors: Meixin Zhu, Xuesong Wang, Yinhai Wang
Date: 2019-01-03 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This study proposes a framework for human-like autonomous car-following
planning based on deep reinforcement learning (deep RL). Historical driving
data are fed into a simulation environment where an RL agent learns from trial
and error interactions based on a reward function that signals how much the
agent deviates from the empirical data. Through these interactions, an optimal
policy, or car-following model that maps in a human-like way from speed,
relative speed between a lead and following vehicle, and inter-vehicle spacing
to acceleration of a following vehicle is finally obtained. The model can be
continuously updated when more data are fed in. Two thousand car-following
periods extracted from the 2015 Shanghai Naturalistic Driving Study were used
to train the model and compare its performance with that of traditional and
recent data-driven car-following models. As shown by this study results, a deep
deterministic policy gradient car-following model that uses disparity between
simulated and observed speed as the reward function and considers a reaction
delay of 1s, denoted as DDPGvRT, can reproduce human-like car-following
behavior with higher accuracy than traditional and recent data-driven
car-following models. Specifically, the DDPGvRT model has a spacing validation
error of 18% and speed validation error of 5%, which are less than those of
other models, including the intelligent driver model, models based on locally
weighted regression, and conventional neural network-based models. Moreover,
the DDPGvRT demonstrates good capability of generalization to various driving
situations and can adapt to different drivers by continuously learning. This
study demonstrates that reinforcement learning methodology can offer insight
into driver behavior and can contribute to the development of human-like
autonomous driving algorithms and traffic-flow models.
Adaptive Locality Preserving Regression
Authors: Jie Wen, Zuofeng Zhong, Zheng Zhang, Lunke Fei, Zhihui Lai, Runze Chen
Date: 2019-01-03 00:00:00
Tasks: feature selection, regression

This paper proposes a novel discriminative regression method, called adaptive
locality preserving regression (ALPR) for classification. In particular, ALPR
aims to learn a more flexible and discriminative projection that not only
preserves the intrinsic structure of data, but also possesses the properties of
feature selection and interpretability. To this end, we introduce a target
learning technique to adaptively learn a more discriminative and flexible
target matrix rather than the pre-defined strict zero-one label matrix for
regression. Then a locality preserving constraint regularized by the adaptive
learned weights is further introduced to guide the projection learning, which
is beneficial to learn a more discriminative projection and avoid overfitting.
Moreover, we replace the conventional `Frobenius norm' with the special l21
norm to constrain the projection, which enables the method to adaptively select
the most important features from the original high-dimensional data for feature
extraction. In this way, the negative influence of the redundant features and
noises residing in the original data can be greatly eliminated. Besides, the
proposed method has good interpretability for features owing to the
row-sparsity property of the l21 norm. Extensive experiments conducted on the
synthetic database with manifold structure and many real-world databases prove
the effectiveness of the proposed method.
Instance-Based Classification through Hypothesis Testing
Authors: Zengyou He, Chaohua Sheng, Yan Liu, Quan Zou
Date: 2019-01-03 00:00:00
Tasks: Binary Classification, Classification, General Classification, Two-sample testing

Classification is a fundamental problem in machine learning and data mining.
During the past decades, numerous classification methods have been presented
based on different principles. However, most existing classifiers cast the
classification problem as an optimization problem and do not address the issue
of statistical significance. In this paper, we formulate the binary
classification problem as a two-sample testing problem. More precisely, our
classification model is a generic framework that is composed of two steps. In
the first step, the distance between the test instance and each training
instance is calculated to derive two distance sets. In the second step, the
two-sample test is performed under the null hypothesis that the two sets of
distances are drawn from the same cumulative distribution. After these two
steps, we have two p-values for each test instance and the test instance is
assigned to the class associated with the smaller p-value. Essentially, the
presented classification method can be regarded as an instance-based classifier
based on hypothesis testing. The experimental results on 40 real data sets show
that our method is able to achieve the same level performance as the
state-of-the-art classifiers and has significantly better performance than
existing testing-based classifiers. Furthermore, we can handle outlying
instances and control the false discovery rate of test instances assigned to
each class under the same framework.
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
Authors: Jonathan Scarlett, Volkan Cevher
Date: 2019-01-02 00:00:00
Tasks: Density Estimation, Model Selection, regression

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic tools have been proposed for this purpose, the oldest one remains arguably the most versatile and widespread: Fano's inequality. In this chapter, we provide a survey of Fano's inequality and its variants in the context of statistical estimation, adopting a versatile framework that covers a wide range of specific problems. We present a variety of key tools and techniques used for establishing impossibility results via this approach, and provide representative examples covering group testing, graphical model selection, sparse linear regression, density estimation, and convex optimization.
An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization
Authors: A. L. Alfeo, F. P. Appio, M. G. C. A. Cimino, A. Lazzeri, A. Martini, G. Vaglini
Date: 2019-01-02 00:00:00

Regional innovation is more and more considered an important enabler of
welfare. It is no coincidence that the European Commission has started looking
at regional peculiarities and dynamics, in order to focus Research and
Innovation Strategies for Smart Specialization towards effective investment
policies. In this context, this work aims to support policy makers in the
analysis of innovation-relevant trends. We exploit a European database of the
regional patent application to determine the dynamics of a set of technological
innovation indicators. For this purpose, we design and develop a software
system for assessing unfolding trends in such indicators. In contrast with
conventional knowledge-based design, our approach is biologically-inspired and
based on self-organization of information. This means that a functional
structure, called track, appears and stays spontaneous at runtime when local
dynamism in data occurs. A further prototyping of tracks allows a better
distinction of the critical phenomena during unfolding events, with a better
assessment of the progressing levels. The proposed mechanism works if
structural parameters are correctly tuned for the given historical context.
Determining such correct parameters is not a simple task since different
indicators may have different dynamics. For this purpose, we adopt an
adaptation mechanism based on differential evolution. The study includes the
problem statement and its characterization in the literature, as well as the
proposed solving approach, experimental setting and results.
Multi-Label Adversarial Perturbations
Authors: Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu
Date: 2019-01-02 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Classification, Multi-Label Learning

Adversarial examples are delicately perturbed inputs, which aim to mislead
machine learning models towards incorrect outputs. While most of the existing
work focuses on generating adversarial perturbations in multi-class
classification problems, many real-world applications fall into the multi-label
setting in which one instance could be associated with more than one label. For
example, a spammer may generate adversarial spams with malicious advertising
while maintaining the other labels such as topic labels unchanged. To analyze
the vulnerability and robustness of multi-label learning models, we investigate
the generation of multi-label adversarial perturbations. This is a challenging
task due to the uncertain number of positive labels associated with one
instance, as well as the fact that multiple labels are usually not mutually
exclusive with each other. To bridge this gap, in this paper, we propose a
general attacking framework targeting on multi-label classification problem and
conduct a premier analysis on the perturbations for deep neural networks.
Leveraging the ranking relationships among labels, we further design a
ranking-based framework to attack multi-label ranking algorithms. We specify
the connection between the two proposed frameworks and separately design two
specific methods grounded on each of them to generate targeted multi-label
perturbations. Experiments on real-world multi-label image classification and
ranking problems demonstrate the effectiveness of our proposed frameworks and
provide insights of the vulnerability of multi-label deep learning models under
diverse targeted attacking strategies. Several interesting findings including
an unpolished defensive strategy, which could potentially enhance the
interpretability and robustness of multi-label deep learning models, are
further presented and discussed at the end.
Multi-class Classification without Multi-class Labels
Authors: Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
Date: 2019-01-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Classification, General Classification, Multi-class Classification

This work presents a new strategy for multi-class classification that
requires no class-specific labels, but instead leverages pairwise similarity
between examples, which is a weaker form of annotation. The proposed method,
meta classification learning, optimizes a binary classifier for pairwise
similarity prediction and through this process learns a multi-class classifier
as a submodule. We formulate this approach, present a probabilistic graphical
model for it, and derive a surprisingly simple loss function that can be used
to learn neural network-based models. We then demonstrate that this same
framework generalizes to the supervised, unsupervised cross-task, and
semi-supervised settings. Our method is evaluated against state of the art in
all three learning paradigms and shows a superior or comparable accuracy,
providing evidence that learning multi-class classification without multi-class
labels is a viable learning option.
Photo-Sketching: Inferring Contour Drawings from Images
Authors: Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, Deva Ramanan
Date: 2019-01-02 00:00:00
Tasks: Boundary Detection, Diversity

Edges, boundaries and contours are important subjects of study in both
computer graphics and computer vision. On one hand, they are the 2D elements
that convey 3D shapes, on the other hand, they are indicative of occlusion
events and thus separation of objects or semantic concepts. In this paper, we
aim to generate contour drawings, boundary-like drawings that capture the
outline of the visual scene. Prior art often cast this problem as boundary
detection. However, the set of visual cues presented in the boundary detection
output are different from the ones in contour drawings, and also the artistic
style is ignored. We address these issues by collecting a new dataset of
contour drawings and proposing a learning-based method that resolves diversity
in the annotation and, unlike boundary detectors, can work with imperfect
alignment of the annotation and the actual ground truth. Our method surpasses
previous methods quantitatively and qualitatively. Surprisingly, when our model
fine-tunes on BSDS500, we achieve the state-of-the-art performance in salient
boundary detection, suggesting contour drawing might be a scalable alternative
to boundary annotation, which at the same time is easier and more interesting
for annotators to draw.
Visualizing Deep Similarity Networks
Authors: Abby Stylianou, Richard Souvenir, Robert Pless
Date: 2019-01-02 00:00:00
Tasks: General Classification

For convolutional neural network models that optimize an image embedding, we
propose a method to highlight the regions of images that contribute most to
pairwise similarity. This work is a corollary to the visualization tools
developed for classification networks, but applicable to the problem domains
better suited to similarity learning. The visualization shows how similarity
networks that are fine-tuned learn to focus on different features. We also
generalize our approach to embedding networks that use different pooling
strategies and provide a simple mechanism to support image similarity searches
on objects or sub-regions in the query image.
Linear colour segmentation revisited
Authors: Anna Smagina, Valentina Bozhkova, Sergey Gladilin, Dmitry Nikolaev
Date: 2019-01-02 00:00:00
Tasks: Segmentation

In this work we discuss the known algorithms for linear colour segmentation
based on a physical approach and propose a new modification of segmentation
algorithm. This algorithm is based on a region adjacency graph framework
without a pre-segmentation stage. Proposed edge weight functions are defined
from linear image model with normal noise. The colour space projective
transform is introduced as a novel pre-processing technique for better handling
of shadow and highlight areas. The resulting algorithm is tested on a benchmark
dataset consisting of the images of 19 natural scenes selected from the
Barnard's DXC-930 SFU dataset and 12 natural scene images newly published for
common use. The dataset is provided with pixel-by-pixel ground truth colour
segmentation for every image. Using this dataset, we show that the proposed
algorithm modifications lead to qualitative advantages over other model-based
segmentation algorithms, and also show the positive effect of each proposed
modification. The source code and datasets for this work are available for free
access at http://github.com/visillect/segmentation.
A Simple Algorithm for Scalable Monte Carlo Inference
Authors: Alexander Borisenko, Maksym Byshkin, Alessandro Lomi
Date: 2019-01-02 00:00:00
Tasks: Sociology

The methods of statistical physics are widely used for modelling complex networks. Building on the recently proposed Equilibrium Expectation approach, we derive a simple and efficient algorithm for maximum likelihood estimation (MLE) of parameters of exponential family distributions - a family of statistical models, that includes Ising model, Markov Random Field and Exponential Random Graph models. Computational experiments and analysis of empirical data demonstrate that the algorithm increases by orders of magnitude the size of network data amenable to Monte Carlo based inference. We report results suggesting that the applicability of the algorithm may readily be extended to the analysis of large samples of dependent observations commonly found in biology, sociology, astrophysics, and ecology.
Adversarial Robustness May Be at Odds With Simplicity
Authors: Preetum Nakkiran
Date: 2019-01-02 00:00:00
Tasks: Adversarial Robustness, Classification, General Classification, Robust classification

Current techniques in machine learning are so far are unable to learn
classifiers that are robust to adversarial perturbations. However, they are
able to learn non-robust classifiers with very high accuracy, even in the
presence of random perturbations. Towards explaining this gap, we highlight the
hypothesis that $\textit{robust classification may require more complex
classifiers (i.e. more capacity) than standard classification.}$
  In this note, we show that this hypothesis is indeed possible, by giving
several theoretical examples of classification tasks and sets of "simple"
classifiers for which: (1) There exists a simple classifier with high standard
accuracy, and also high accuracy under random $\ell_\infty$ noise. (2) Any
simple classifier is not robust: it must have high adversarial loss with
$\ell_\infty$ perturbations. (3) Robust classification is possible, but only
with more complex classifiers (exponentially more complex, in some examples).
  Moreover, $\textit{there is a quantitative trade-off between robustness and
standard accuracy among simple classifiers.}$ This suggests an alternate
explanation of this phenomenon, which appears in practice: the tradeoff may
occur not because the classification task inherently requires such a tradeoff
(as in [Tsipras-Santurkar-Engstrom-Turner-Madry `18]), but because the
structure of our current classifiers imposes such a tradeoff.
Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer
Authors: Daniel Kent, Fathi M. Salem
Date: 2019-01-02 00:00:00
Tasks: Translation

The Long Short-Term Memory (LSTM) layer is an important advancement in the
field of neural networks and machine learning, allowing for effective training
and impressive inference performance. LSTM-based neural networks have been
successfully employed in various applications such as speech processing and
language translation. The LSTM layer can be simplified by removing certain
components, potentially speeding up training and runtime with limited change in
performance. In particular, the recently introduced variants, called SLIM
LSTMs, have shown success in initial experiments to support this view. Here, we
perform computational analysis of the validation accuracy of a convolutional
plus recurrent neural network architecture using comparatively the standard
LSTM and three SLIM LSTM layers. We have found that some realizations of the
SLIM LSTM layers can potentially perform as well as the standard LSTM layer for
our considered architecture.
Flow Based Self-supervised Pixel Embedding for Image Segmentation
Authors: Bin Ma, Shubao Liu, Yingxuan Zhi, Qi Song
Date: 2019-01-02 00:00:00
Tasks: Image Segmentation, Optical Flow Estimation, Segmentation, Semantic Segmentation

We propose a new self-supervised approach to image feature learning from
motion cue. This new approach leverages recent advances in deep learning in two
directions: 1) the success of training deep neural network in estimating
optical flow in real data using synthetic flow data; and 2) emerging work in
learning image features from motion cues, such as optical flow. Building on
these, we demonstrate that image features can be learned in self-supervision by
first training an optical flow estimator with synthetic flow data, and then
learning image features from the estimated flows in real motion data. We
demonstrate and evaluate this approach on an image segmentation task. Using the
learned image feature representation, the network performs significantly better
than the ones trained from scratch in few-shot segmentation tasks.
Real-Time EEG Classification via Coresets for BCI Applications
Authors: Eitan Netzer, Alex Frid, Dan Feldman
Date: 2019-01-02 00:00:00
Tasks: Brain Computer Interface, Classification, Data Summarization, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

A brain-computer interface (BCI) based on the motor imagery (MI) paradigm
translates one's motor intention into a control signal by classifying the
Electroencephalogram (EEG) signal of different tasks. However, most existing
systems either (i) use a high-quality algorithm to train the data off-line and
run only classification in real-time, since the off-line algorithm is too slow,
or (ii) use low-quality heuristics that are sufficiently fast for real-time
training but introduces relatively large classification error. In this work, we
propose a novel processing pipeline that allows real-time and parallel learning
of EEG signals using high-quality but possibly inefficient algorithms. This is
done by forging a link between BCI and core-sets, a technique that originated
in computational geometry for handling streaming data via data summarization.
  We suggest an algorithm that maintains the representation such coreset
tailored to handle the EEG signal which enables: (i) real time and continuous
computation of the Common Spatial Pattern (CSP) feature extraction method on a
coreset representation of the signal (instead on the signal itself) , (ii)
improvement of the CSP algorithm efficiency with provable guarantees by
applying CSP algorithm on the coreset, and (iii) real time addition of the data
trials (EEG data windows) to the coreset.
  For simplicity, we focus on the CSP algorithm, which is a classic algorithm.
Nevertheless, we expect that our coreset will be extended to other algorithms
in future papers. In the experimental results we show that our system can
indeed learn EEG signals in real-time for example a 64 channels setup with
hundreds of time samples per second. Full open source is provided to reproduce
the experiment and in the hope that it will be used and extended to more
coresets and BCI applications in the future.
Improving Face Anti-Spoofing by 3D Virtual Synthesis
Authors: Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li
Date: 2019-01-02 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be re-printed and re-captured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face anti-spoofing using cheap and large-scale synthetic data.
Action2Vec: A Crossmodal Embedding Approach to Action Learning
Authors: Meera Hahn, Andrew Silva, James M. Rehg
Date: 2019-01-02 00:00:00
Tasks: Action Recognition, General Classification, Temporal Action Localization, Zero-Shot Action Recognition

We describe a novel cross-modal embedding space for actions, named
Action2Vec, which combines linguistic cues from class labels with
spatio-temporal features derived from video clips. Our approach uses a
hierarchical recurrent network to capture the temporal structure of video
features. We train our embedding using a joint loss that combines
classification accuracy with similarity to Word2Vec semantics. We evaluate
Action2Vec by performing zero shot action recognition and obtain state of the
art results on three standard datasets. In addition, we present two novel
analogy tests which quantify the extent to which our joint embedding captures
distributional semantics. This is the first joint embedding space to combine
verbs and action videos, and the first to be thoroughly evaluated with respect
to its distributional semantics.