pwc-archive/papers-with-abstracts huggingface dataset:
Projective simulation with generalization
Authors: Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel
Date: 2015-04-09 00:00:00
Tasks: Reinforcement Learning

The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
present a dynamic and autonomous machinery that enables projective simulation
agents to meaningfully generalize. Projective simulation, a novel, physical
approach to artificial intelligence, was recently shown to perform well in
standard reinforcement learning problems, with applications in advanced
robotics as well as quantum experiments. Both the basic projective simulation
model and the presented generalization machinery are based on very simple
principles. This allows us to provide a full analytical analysis of the agent's
performance and to illustrate the benefit the agent gains by generalizing.
Specifically, we show that already in basic (but extreme) environments,
learning without generalization may be impossible, and demonstrate how the
presented generalization machinery enables the projective simulation agent to
learn.
Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times
Authors: Zhenhua Zhang, Lei Lin
Date: 2017-10-31 00:00:00
Tasks: Management

Border crossing delays cause problems like huge economics loss and heavy
environmental pollutions. To understand more about the nature of border
crossing delay, this study applies a dictionary-based compression algorithm to
process the historical Niagara Frontier border wait times data. It can identify
the abnormal spatial-temporal patterns for both passenger vehicles and trucks
at three bridges connecting US and Canada. Furthermore, it provides a
quantitate anomaly score to rank the wait times patterns across the three
bridges for each vehicle type and each direction. By analyzing the top three
most abnormal patterns, we find that there are at least two factors
contributing the anomaly of the patterns. The weekends and holidays may cause
unusual heave congestions at the three bridges at the same time, and the
freight transportation demand may be uneven from Canada to the USA at Peace
Bridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score.
By calculating the frequency of the top 5% abnormal patterns by hour of the
day, the results show that for cars from the USA to Canada, the frequency of
abnormal waiting time patterns is the highest during noon while for trucks in
the same direction, it is the highest during the afternoon peak hours. For
Canada to US direction, the frequency of abnormal border wait time patterns for
both cars and trucks reaches to the peak during the afternoon. The analysis of
abnormal spatial-temporal wait times patterns is promising to improve the
border crossing management
Event-based, 6-DOF Camera Tracking from Photometric Depth Maps
Authors: Guillermo Gallego, Jon E. A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, Davide Scaramuzza
Date: 2016-07-12 00:00:00

Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. These cameras do not
suffer from motion blur and have a very high dynamic range, which enables them
to provide reliable visual information during high-speed motions or in scenes
characterized by high dynamic range. These features, along with a very low
power consumption, make event cameras an ideal complement to standard cameras
for VR/AR and video game applications. With these applications in mind, this
paper tackles the problem of accurate, low-latency tracking of an event camera
from an existing photometric depth map (i.e., intensity plus depth information)
built via classic dense reconstruction pipelines. Our approach tracks the 6-DOF
pose of the event camera upon the arrival of each event, thus virtually
eliminating latency. We successfully evaluate the method in both indoor and
outdoor scenes and show that---because of the technological advantages of the
event camera---our pipeline works in scenes characterized by high-speed motion,
which are still unaccessible to standard cameras.
Whodunnit? Crime Drama as a Case for Natural Language Understanding
Authors: Lea Frermann, Shay B. Cohen, Mirella Lapata
Date: 2017-10-31 00:00:00
Proceeding: TACL 2018 1
Tasks: Natural Language Understanding

In this paper we argue that crime drama exemplified in television programs
such as CSI:Crime Scene Investigation is an ideal testbed for approximating
real-world natural language understanding and the complex inferences associated
with it. We propose to treat crime drama as a new inference task, capitalizing
on the fact that each episode poses the same basic question (i.e., who
committed the crime) and naturally provides the answer when the perpetrator is
revealed. We develop a new dataset based on CSI episodes, formalize perpetrator
identification as a sequence labeling problem, and develop an LSTM-based model
which learns from multi-modal data. Experimental results show that an
incremental inference strategy is key to making accurate guesses as well as
learning from representations fusing textual, visual, and acoustic input.
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
Authors: Changzhe Jiao, Chao Chen, Ronald G. McGarvey, Stephanie Bohlman, Licheng Jiao, Alina Zare
Date: 2017-10-31 00:00:00
Tasks: Multiple Instance Learning

The Multiple Instance Hybrid Estimator for discriminative target
characterization from imprecisely labeled hyperspectral data is presented. In
many hyperspectral target detection problems, acquiring accurately labeled
training data is difficult. Furthermore, each pixel containing target is likely
to be a mixture of both target and non-target signatures (i.e., sub-pixel
targets), making extracting a pure prototype signature for the target class
from the data extremely difficult. The proposed approach addresses these
problems by introducing a data mixing model and optimizing the response of the
hybrid sub-pixel detector within a multiple instance learning framework. The
proposed approach iterates between estimating a set of discriminative target
and non-target signatures and solving a sparse unmixing problem. After learning
target signatures, a signature based detector can then be applied on test data.
Both simulated and real hyperspectral target detection experiments show the
proposed algorithm is effective at learning discriminative target signatures
and achieves superior performance over state-of-the-art comparison algorithms.
Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree
Authors: Lei Lin, Qian Wang, Adel W. Sadek
Date: 2017-01-20 00:00:00
Tasks: Missing Values, Variable Selection

Traffic accident data are usually noisy, contain missing values, and
heterogeneous. How to select the most important variables to improve real-time
traffic accident risk prediction has become a concern of many recent studies.
This paper proposes a novel variable selection method based on the Frequent
Pattern tree (FP tree) algorithm. First, all the frequent patterns in the
traffic accident dataset are discovered. Then for each frequent pattern, a new
criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is
calculated. This ROPR is added to the importance score of the variables that
differentiate one frequent pattern from the others. To test the proposed
method, a dataset was compiled from the traffic accidents records detected by
only one detector on interstate highway I-64 in Virginia in 2005. This dataset
was then linked to other variables such as real-time traffic information and
weather conditions. Both the proposed method based on the FP tree algorithm, as
well as the widely utilized, random forest method, were then used to identify
the important variables or the Virginia dataset. The results indicate that
there are some differences between the variables deemed important by the FP
tree and those selected by the random forest method. Following this, two
baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network)
were developed to predict accident risk based on the variables identified by
both the FP tree method and the random forest method. The results show that the
models based on the variable selection using the FP tree performed better than
those based on the random forest method for several versions of the k-NN and
Bayesian network models.The best results were derived from a Bayesian network
model using variables from FP tree. That model could predict 61.11% of
accidents accurately while having a false alarm rate of 38.16%.
On Learning Mixtures of Well-Separated Gaussians
Authors: Oded Regev, Aravindan Vijayaraghavan
Date: 2017-10-31 00:00:00

We consider the problem of efficiently learning mixtures of a large number of
spherical Gaussians, when the components of the mixture are well separated. In
the most basic form of this problem, we are given samples from a uniform
mixture of $k$ standard spherical Gaussians, and the goal is to estimate the
means up to accuracy $\delta$ using $poly(k,d, 1/\delta)$ samples.
  In this work, we study the following question: what is the minimum separation
needed between the means for solving this task? The best known algorithm due to
Vempala and Wang [JCSS 2004] requires a separation of roughly
$\min\{k,d\}^{1/4}$. On the other hand, Moitra and Valiant [FOCS 2010] showed
that with separation $o(1)$, exponentially many samples are required. We
address the significant gap between these two bounds, by showing the following
results.
  1. We show that with separation $o(\sqrt{\log k})$, super-polynomially many
samples are required. In fact, this holds even when the $k$ means of the
Gaussians are picked at random in $d=O(\log k)$ dimensions.
  2. We show that with separation $\Omega(\sqrt{\log k})$, $poly(k,d,1/\delta)$
samples suffice. Note that the bound on the separation is independent of
$\delta$. This result is based on a new and efficient "accuracy boosting"
algorithm that takes as input coarse estimates of the true means and in time
$poly(k,d, 1/\delta)$ outputs estimates of the means up to arbitrary accuracy
$\delta$ assuming the separation between the means is $\Omega(\min\{\sqrt{\log
k},\sqrt{d}\})$ (independently of $\delta$).
  We also present a computationally efficient algorithm in $d=O(1)$ dimensions
with only $\Omega(\sqrt{d})$ separation. These results together essentially
characterize the optimal order of separation between components that is needed
to learn a mixture of $k$ spherical Gaussians with polynomial samples.
Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso
Authors: Alexander J. Gibberd, James D. B. Nelson
Date: 2015-12-19 00:00:00
Tasks: Time Series, Time Series Analysis

The time-evolving precision matrix of a piecewise-constant Gaussian graphical
model encodes the dynamic conditional dependency structure of a multivariate
time-series. Traditionally, graphical models are estimated under the assumption
that data is drawn identically from a generating distribution. Introducing
sparsity and sparse-difference inducing priors we relax these assumptions and
propose a novel regularized M-estimator to jointly estimate both the graph and
changepoint structure. The resulting estimator possesses the ability to
therefore favor sparse dependency structures and/or smoothly evolving graph
structures, as required. Moreover, our approach extends current methods to
allow estimation of changepoints that are grouped across multiple dependencies
in a system. An efficient algorithm for estimating structure is proposed. We
study the empirical recovery properties in a synthetic setting. The qualitative
effect of grouped changepoint estimation is then demonstrated by applying the
method on two real-world data-sets.
Generating Time-Based Label Refinements to Discover More Precise Process Models
Authors: Niek Tax, Emin Alasgarov, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma
Date: 2017-05-25 00:00:00
Tasks: Attribute

Process mining is a research field focused on the analysis of event data with
the aim of extracting insights related to dynamic behavior. Applying process
mining techniques on data from smart home environments has the potential to
provide valuable insights in (un)healthy habits and to contribute to ambient
assisted living solutions. Finding the right event labels to enable the
application of process mining techniques is however far from trivial, as simply
using the triggering sensor as the label for sensor events results in
uninformative models that allow for too much behavior (overgeneralizing).
Refinements of sensor level event labels suggested by domain experts have been
shown to enable discovery of more precise and insightful process models.
However, there exists no automated approach to generate refinements of event
labels in the context of process mining. In this paper we propose a framework
for the automated generation of label refinements based on the time attribute
of events, allowing us to distinguish behaviourally different instances of the
same event type based on their time attribute. We show on a case study with
real life smart home event data that using automatically generated refined
labels in process discovery, we can find more specific, and therefore more
insightful, process models. We observe that one label refinement could have an
effect on the usefulness of other label refinements when used together.
Therefore, we explore four strategies to generate useful combinations of
multiple label refinements and evaluate those on three real life smart home
event logs.
TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting
Authors: Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev, Alexander Grushetsky
Date: 2017-10-31 00:00:00

TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed
training of gradient boosted trees. It is based on TensorFlow, and its
distinguishing features include a novel architecture, automatic loss
differentiation, layer-by-layer boosting that results in smaller ensembles and
faster prediction, principled multi-class handling, and a number of
regularization techniques to prevent overfitting.
Compact Multi-Class Boosted Trees
Authors: Natalia Ponomareva, Thomas Colthurst, Gilbert Hendry, Salem Haykal, Soroush Radpour
Date: 2017-10-31 00:00:00

Gradient boosted decision trees are a popular machine learning technique, in
part because of their ability to give good accuracy with small models. We
describe two extensions to the standard tree boosting algorithm designed to
increase this advantage. The first improvement extends the boosting formalism
from scalar-valued trees to vector-valued trees. This allows individual trees
to be used as multiclass classifiers, rather than requiring one tree per class,
and drastically reduces the model size required for multiclass problems. We
also show that some other popular vector-valued gradient boosted trees
modifications fit into this formulation and can be easily obtained in our
implementation. The second extension, layer-by-layer boosting, takes smaller
steps in function space, which is empirically shown to lead to a faster
convergence and to a more compact ensemble. We have added both improvements to
the open-source TensorFlow Boosted trees (TFBT) package, and we demonstrate
their efficacy on a variety of multiclass datasets. We expect these extensions
will be of particular interest to boosted tree applications that require small
models, such as embedded devices, applications requiring fast inference, or
applications desiring more interpretable models.
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks
Authors: Fréderic Godin, Jonas Degrave, Joni Dambre, Wesley De Neve
Date: 2017-07-25 00:00:00
Tasks: Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

In this paper, we introduce a novel type of Rectified Linear Unit (ReLU),
called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an
unbounded positive and negative image, can be used as a drop-in replacement for
a tanh activation function in the recurrent step of Quasi-Recurrent Neural
Networks (QRNNs) (Bradbury et al. (2017)). Similar to ReLUs, DReLUs are less
prone to the vanishing gradient problem, they are noise robust, and they induce
sparse activations.
  We independently reproduce the QRNN experiments of Bradbury et al. (2017) and
compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long
Short-Term Memory networks (LSTMs) on sentiment classification and word-level
language modeling. Additionally, we evaluate on character-level language
modeling, showing that we are able to stack up to eight QRNN layers with
DReLUs, thus making it possible to improve the current state-of-the-art in
character-level language modeling over shallow architectures based on LSTMs.
Mesh-to-raster based non-rigid registration of multi-modal images
Authors: Rosalia Tatano, Benjamin Berkels, Thomas M. Deserno
Date: 2017-03-06 00:00:00

Region of interest (ROI) alignment in medical images plays a crucial role in
diagnostics, procedure planning, treatment, and follow-up. Frequently, a model
is represented as triangulated mesh while the patient data is provided from CAT
scanners as pixel or voxel data. Previously, we presented a 2D method for
curve-to-pixel registration. This paper contributes (i) a general
mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a
3D surface-to-voxel application, and (iii) a comprehensive quantitative
evaluation in 2D using ground truth provided by the simultaneous truth and
performance level estimation (STAPLE) method. The registration is formulated as
a minimization problem where the objective consists of a data term, which
involves the signed distance function of the ROI from the reference image, and
a higher order elastic regularizer for the deformation. The evaluation is based
on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of
decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each
showing one corresponding tooth in both modalities. The ROI in each image is
manually marked by three experts (900 curves in total). In the QLF-DP setting,
our approach significantly outperforms the mutual information-based
registration algorithm implemented with the Insight Segmentation and
Registration Toolkit (ITK) and Elastix.
PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees
Authors: Namrata Vaswani, Praneeth Narayanamurthy
Date: 2017-02-10 00:00:00

We study Principal Component Analysis (PCA) in a setting where a part of the
corrupting noise is data-dependent and, as a result, the noise and the true
data are correlated. Under a bounded-ness assumption on the true data and the
noise, and a simple assumption on data-noise correlation, we obtain a nearly
optimal sample complexity bound for the most commonly used PCA solution,
singular value decomposition (SVD). This bound is a significant improvement
over the bound obtained by Vaswani and Guo in recent work (NIPS 2016) where
this "correlated-PCA" problem was first studied; and it holds under a
significantly weaker data-noise correlation assumption than the one used for
this earlier result.
A multi-layer network based on Sparse Ternary Codes for universal vector compression
Authors: Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
Date: 2017-10-31 00:00:00

We present the multi-layer extension of the Sparse Ternary Codes (STC) for
fast similarity search where we focus on the reconstruction of the database
vectors from the ternary codes. To consider the trade-offs between the
compactness of the STC and the quality of the reconstructed vectors, we study
the rate-distortion behavior of these codes under different setups. We show
that a single-layer code cannot achieve satisfactory results at high rates.
Therefore, we extend the concept of STC to multiple layers and design the
ML-STC, a codebook-free system that successively refines the reconstruction of
the residuals of previous layers. While the ML-STC keeps the sparse ternary
structure of the single-layer STC and hence is suitable for fast similarity
search in large-scale databases, we show its superior rate-distortion
performance on both model-based synthetic data and public large-scale
databases, as compared to several binary hashing methods.
Clothing Retrieval with Visual Attention Model
Authors: Zhonghao Wang, Yujun Gu, Ya zhang, Jun Zhou, Xiao Gu
Date: 2017-10-31 00:00:00
Tasks: model, Retrieval, Self-Learning

Clothing retrieval is a challenging problem in computer vision. With the
advance of Convolutional Neural Networks (CNNs), the accuracy of clothing
retrieval has been significantly improved. FashionNet[1], a recent study,
proposes to employ a set of artificial features in the form of landmarks for
clothing retrieval, which are shown to be helpful for retrieval. However, the
landmark detection module is trained with strong supervision which requires
considerable efforts to obtain. In this paper, we propose a self-learning
Visual Attention Model (VAM) to extract attention maps from clothing images.
The VAM is further connected to a global network to form an end-to-end network
structure through Impdrop connection which randomly Dropout on the feature maps
with the probabilities given by the attention map. Extensive experiments on
several widely used benchmark clothing retrieval data sets have demonstrated
the promise of the proposed method. We also show that compared to the trivial
Product connection, the Impdrop connection makes the network structure more
robust when training sets of limited size are used.
Towards Moral Autonomous Systems
Authors: Vicky Charisi, Louise Dennis, Michael Fisher, Robert Lieck, Andreas Matthias, Marija Slavkovik, Janina Sombetzki, Alan F. T. Winfield, Roman Yampolskiy
Date: 2017-03-14 00:00:00
Tasks: Ethics, Philosophy

Both the ethics of autonomous systems and the problems of their technical
implementation have by now been studied in some detail. Less attention has been
given to the areas in which these two separate concerns meet. This paper,
written by both philosophers and engineers of autonomous systems, addresses a
number of issues in machine ethics that are located at precisely the
intersection between ethics and engineering. We first discuss the main
challenges which, in our view, machine ethics posses to moral philosophy. We
them consider different approaches towards the conceptual design of autonomous
systems and their implications on the ethics implementation in such systems.
Then we examine problematic areas regarding the specification and verification
of ethical behavior in autonomous systems, particularly with a view towards the
requirements of future legislation. We discuss transparency and accountability
issues that will be crucial for any future wide deployment of autonomous
systems in society. Finally we consider the, often overlooked, possibility of
intentional misuse of AI systems and the possible dangers arising out of
deliberately unethical design, implementation, and use of autonomous robots.