pwc-archive/papers-with-abstracts huggingface dataset:
Fast Spectral Ranking for Similarity Search
Authors: Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondrej Chum
Date: 2017-03-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Retrieval

Despite the success of deep learning on representing images for particular
object retrieval, recent studies show that the learned representations still
lie on manifolds in a high dimensional space. This makes the Euclidean nearest
neighbor search biased for this task. Exploring the manifolds online remains
expensive even if a nearest neighbor graph has been computed offline. This work
introduces an explicit embedding reducing manifold search to Euclidean search
followed by dot product similarity search. This is equivalent to linear graph
filtering of a sparse signal in the frequency domain. To speed up online
search, we compute an approximate Fourier basis of the graph offline. We
improve the state of art on particular object retrieval datasets including the
challenging Instre dataset containing small objects. At a scale of 10^5 images,
the offline cost is only a few hours, while query time is comparable to
standard similarity search.
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
Authors: Toon Van Craenendonck, Sebastijan Dumančić, Elia Van Wolputte, Hendrik Blockeel
Date: 2018-03-29 00:00:00
Tasks: Clustering

Constraint-based clustering algorithms exploit background knowledge to
construct clusterings that are aligned with the interests of a particular user.
This background knowledge is often obtained by allowing the clustering system
to pose pairwise queries to the user: should these two elements be in the same
cluster or not? Active clustering methods aim to minimize the number of queries
needed to obtain a good clustering by querying the most informative pairs
first. Ideally, a user should be able to answer a couple of these queries,
inspect the resulting clustering, and repeat these two steps until a
satisfactory result is obtained. We present COBRAS, an approach to active
clustering with pairwise constraints that is suited for such an interactive
clustering process. A core concept in COBRAS is that of a super-instance: a
local region in the data in which all instances are assumed to belong to the
same cluster. COBRAS constructs such super-instances in a top-down manner to
produce high-quality results early on in the clustering process, and keeps
refining these super-instances as more pairwise queries are given to get more
detailed clusterings later on. We experimentally demonstrate that COBRAS
produces good clusterings at fast run times, making it an excellent candidate
for the iterative clustering scenario outlined above.
Inferring network connectivity from event timing patterns
Authors: Jose Casadiego, Dimitra Maoutsa, Marc Timme
Date: 2018-03-27 00:00:00
Tasks: Time Series, Time Series Analysis

Reconstructing network connectivity from the collective dynamics of a system
typically requires access to its complete continuous-time evolution although
these are often experimentally inaccessible. Here we propose a theory for
revealing physical connectivity of networked systems only from the event time
series their intrinsic collective dynamics generate. Representing the patterns
of event timings in an event space spanned by inter-event and cross-event
intervals, we reveal which other units directly influence the inter-event times
of any given unit. For illustration, we linearize an event space mapping
constructed from the spiking patterns in model neural circuits to reveal the
presence or absence of synapses between any pair of neurons as well as whether
the coupling acts in an inhibiting or activating (excitatory) manner. The
proposed model-independent reconstruction theory is scalable to larger networks
and may thus play an important role in the reconstruction of networks from
biology to social science and engineering.
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Authors: Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, Elisa Ricci
Date: 2018-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Monocular Depth Estimation

Recent works have shown the benefit of integrating Conditional Random Fields
(CRFs) models into deep architectures for improving pixel-level prediction
tasks. Following this line of research, in this paper we introduce a novel
approach for monocular depth estimation. Similarly to previous works, our
method employs a continuous CRF to fuse multi-scale information derived from
different layers of a front-end Convolutional Neural Network (CNN). Differently
from past works, our approach benefits from a structured attention model which
automatically regulates the amount of information transferred between
corresponding features at different scales. Importantly, the proposed attention
model is seamlessly integrated into the CRF, allowing end-to-end training of
the entire architecture. Our extensive experimental evaluation demonstrates the
effectiveness of the proposed method which is competitive with previous methods
on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2
dataset.
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination
Authors: Hazel Doughty, Dima Damen, Walterio Mayol-Cuevas
Date: 2017-03-29 00:00:00
Proceeding: CVPR 2018 6

We present a method for assessing skill from video, applicable to a variety
of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate
the problem as pairwise (who's better?) and overall (who's best?) ranking of
video collections, using supervised deep ranking. We propose a novel loss
function that learns discriminative features when a pair of videos exhibit
variance in skill, and learns shared features when a pair of videos exhibit
comparable skill levels. Results demonstrate our method is applicable across
tasks, with the percentage of correctly ordered pairs of videos ranging from
70% to 83% for four datasets. We demonstrate the robustness of our approach via
sensitivity analysis of its parameters. We see this work as effort toward the
automated organization of how-to video collections and overall, generic skill
determination in video.
Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots
Authors: Varuna De Silva, Jamie Roche, Ahmet Kondoz
Date: 2017-10-17 00:00:00
Tasks: Autonomous Vehicles

Autonomous robots that assist humans in day to day living tasks are becoming
increasingly popular. Autonomous mobile robots operate by sensing and
perceiving their surrounding environment to make accurate driving decisions. A
combination of several different sensors such as LiDAR, radar, ultrasound
sensors and cameras are utilized to sense the surrounding environment of
autonomous vehicles. These heterogeneous sensors simultaneously capture various
physical attributes of the environment. Such multimodality and redundancy of
sensing need to be positively utilized for reliable and consistent perception
of the environment through sensor data fusion. However, these multimodal sensor
data streams are different from each other in many ways, such as temporal and
spatial resolution, data format, and geometric alignment. For the subsequent
perception algorithms to utilize the diversity offered by multimodal sensing,
the data streams need to be spatially, geometrically and temporally aligned
with each other. In this paper, we address the problem of fusing the outputs of
a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image
sensor for free space detection. The outputs of LiDAR scanner and the image
sensor are of different spatial resolutions and need to be aligned with each
other. A geometrical model is used to spatially align the two sensor outputs,
followed by a Gaussian Process (GP) regression-based resolution matching
algorithm to interpolate the missing data with quantifiable uncertainty. The
results indicate that the proposed sensor data fusion framework significantly
aids the subsequent perception steps, as illustrated by the performance
improvement of a uncertainty aware free space detection algorithm
On Hyperparameter Search in Cluster Ensembles
Authors: Luzie Helfmann, Johannes von Lindheim, Mattes Mollenhauer, Ralf Banisch
Date: 2018-03-29 00:00:00
Tasks: Clustering, Clustering Ensemble

Quality assessments of models in unsupervised learning and clustering
verification in particular have been a long-standing problem in the machine
learning research. The lack of robust and universally applicable cluster
validity scores often makes the algorithm selection and hyperparameter
evaluation a tough guess. In this paper, we show that cluster ensemble
aggregation techniques such as consensus clustering may be used to evaluate
clusterings and their hyperparameter configurations. We use normalized mutual
information to compare individual objects of a clustering ensemble to the
constructed consensus of the whole ensemble and show, that the resulting score
can serve as an overall quality measure for clustering problems. This method is
capable of highlighting the standout clustering and hyperparameter
configuration in the ensemble even in the case of a distorted consensus. We
apply this very general framework to various data sets and give possible
directions for future research.
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks
Authors: Ruth Fong, Andrea Vedaldi
Date: 2018-01-10 00:00:00
Proceeding: CVPR 2018 6

In an effort to understand the meaning of the intermediate representations
captured by deep networks, recent papers have tried to associate specific
semantic concepts to individual neural network filter responses, where
interesting correlations are often found, largely by focusing on extremal
filter responses. In this paper, we show that this approach can favor
easy-to-interpret cases that are not necessarily representative of the average
behavior of a representation.
  A more realistic but harder-to-study hypothesis is that semantic
representations are distributed, and thus filters must be studied in
conjunction. In order to investigate this idea while enabling systematic
visualization and quantification of multiple filter responses, we introduce the
Net2Vec framework, in which semantic concepts are mapped to vectorial
embeddings based on corresponding filter responses. By studying such
embeddings, we are able to show that 1., in most cases, multiple filters are
required to code for a concept, that 2., often filters are not concept specific
and help encode multiple concepts, and that 3., compared to single filter
activations, filter embeddings are able to better characterize the meaning of a
representation and its relationship to other concepts.
Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description
Authors: Le Van-Duyet, Vo Minh Quan, Dang Quang An
Date: 2017-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Word Embeddings

Unsupervise learned word embeddings have seen tremendous success in numerous Natural Language Processing (NLP) tasks in recent years. The main contribution of this paper is to develop a technique called Skill2vec, which applies machine learning techniques in recruitment to enhance the search strategy to find candidates possessing the appropriate skills. Skill2vec is a neural network architecture inspired by Word2vec, developed by Mikolov et al. in 2013. It transforms skills to new vector space, which has the characteristics of calculation and presents skills relationships. We conducted an experiment evaluation manually by a recruitment company's domain experts to demonstrate the effectiveness of our approach.
A Systematic Review of Automated Grammar Checking in English Language
Authors: Madhvi Soni, Jitendra Singh Thakur
Date: 2018-03-29 00:00:00

Grammar checking is the task of detection and correction of grammatical
errors in the text. English is the dominating language in the field of science
and technology. Therefore, the non-native English speakers must be able to use
correct English grammar while reading, writing or speaking. This generates the
need of automatic grammar checking tools. So far many approaches have been
proposed and implemented. But less efforts have been made in surveying the
literature in the past decade. The objective of this systematic review is to
examine the existing literature, highlighting the current issues and suggesting
the potential directions of future research. This systematic review is a result
of analysis of 12 primary studies obtained after designing a search strategy
for selecting papers found on the web. We also present a possible scheme for
the classification of grammar errors. Among the main observations, we found
that there is a lack of efficient and robust grammar checking tools for real
time applications. We present several useful illustrations- most prominent are
the schematic diagrams that we provide for each approach and a table that
summarizes these approaches along different dimensions such as target error
types, linguistic dataset used, strengths and limitations of the approach. This
facilitates better understandability, comparison and evaluation of previous
research.
Modified SMOTE Using Mutual Information and Different Sorts of Entropies
Authors: Sima Sharifirad, Azra Nazari, Mehdi Ghatee
Date: 2018-03-29 00:00:00
Tasks: Attribute

SMOTE is one of the oversampling techniques for balancing the datasets and it
is considered as a pre-processing step in learning algorithms. In this paper,
four new enhanced SMOTE are proposed that include an improved version of KNN in
which the attribute weights are defined by mutual information firstly and then
they are replaced by maximum entropy, Renyi entropy and Tsallis entropy. These
four pre-processing methods are combined with 1NN and J48 classifiers and their
performance are compared with the previous methods on 11 imbalanced datasets
from KEEL repository. The results show that these pre-processing methods
improves the accuracy compared with the previous stablished works. In addition,
as a case study, the first pre-processing method is applied on transportation
data of Tehran-Bazargan Highway in Iran with IR equal to 36.
Deconvolution and Restoration of Optical Endomicroscopy Images
Authors: Ahmed Karam Eldaly, Yoann Altmann, Antonios Perperidis, Nikola Krstajic, Tushar Choudhary, Kevin Dhaliwal, Stephen McLaughlin
Date: 2017-01-27 00:00:00

Optical endomicroscopy (OEM) is an emerging technology platform with
preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles
has the potential to provide in vivo, in situ molecular signatures of disease
such as infection and inflammation. However, enhancing the quality of data
acquired by this technique for better visualization and subsequent analysis
remains a challenging problem. Cross coupling between fiber cores and sparse
sampling by imaging fiber bundles are the main reasons for image degradation,
and poor detection performance (i.e., inflammation, bacteria, etc.). In this
work, we address the problem of deconvolution and restoration of OEM data. We
propose a hierarchical Bayesian model to solve this problem and compare three
estimation algorithms to exploit the resulting joint posterior distribution.
The first method is based on Markov chain Monte Carlo (MCMC) methods, however,
it exhibits a relatively long computational time. The second and third
algorithms deal with this issue and are based on a variational Bayes (VB)
approach and an alternating direction method of multipliers (ADMM) algorithm
respectively. Results on both synthetic and real datasets illustrate the
effectiveness of the proposed methods for restoration of OEM images.
Copula Variational Bayes inference via information geometry
Authors: Viet Hung Tran
Date: 2018-03-29 00:00:00
Tasks: Clustering, compressed sensing

Variational Bayes (VB), also known as independent mean-field approximation,
has become a popular method for Bayesian network inference in recent years. Its
application is vast, e.g. in neural network, compressed sensing, clustering,
etc. to name just a few. In this paper, the independence constraint in VB will
be relaxed to a conditional constraint class, called copula in statistics.
Since a joint probability distribution always belongs to a copula class, the
novel copula VB (CVB) approximation is a generalized form of VB. Via
information geometry, we will see that CVB algorithm iteratively projects the
original joint distribution to a copula constraint space until it reaches a
local minimum Kullback-Leibler (KL) divergence. By this way, all mean-field
approximations, e.g. iterative VB, Expectation-Maximization (EM), Iterated
Conditional Mode (ICM) and k-means algorithms, are special cases of CVB
approximation.
  For a generic Bayesian network, an augmented hierarchy form of CVB will also
be designed. While mean-field algorithms can only return a locally optimal
approximation for a correlated network, the augmented CVB network, which is an
optimally weighted average of a mixture of simpler network structures, can
potentially achieve the globally optimal approximation for the first time. Via
simulations of Gaussian mixture clustering, the classification's accuracy of
CVB will be shown to be far superior to that of state-of-the-art VB, EM and
k-means algorithms.
Dihedral angle prediction using generative adversarial networks
Authors: Hyeongki Kim
Date: 2018-03-29 00:00:00
Tasks: Density Estimation, Generative Adversarial Network, Prediction, Protein Structure Prediction, regression

Several dihedral angles prediction methods were developed for protein
structure prediction and their other applications. However, distribution of
predicted angles would not be similar to that of real angles. To address this
we employed generative adversarial networks (GAN). Generative adversarial
networks are composed of two adversarially trained networks: a discriminator
and a generator. A discriminator distinguishes samples from a dataset and
generated samples while a generator generates realistic samples. Although the
discriminator of GANs is trained to estimate density, GAN model is intractable.
On the other hand, noise-contrastive estimation (NCE) was introduced to
estimate a normalization constant of an unnormalized statistical model and thus
the density function. In this thesis, we introduce noise-contrastive estimation
generative adversarial networks (NCE-GAN) which enables explicit density
estimation of a GAN model. And a new loss for the generator is proposed. We
also propose residue-wise variants of auxiliary classifier GAN (AC-GAN) and
Semi-supervised GAN to handle sequence information in a window. In our
experiment, the conditional generative adversarial network (C-GAN), AC-GAN and
Semi-supervised GAN were compared. And experiments done with improved
conditions were invested. We identified a phenomenon of AC-GAN that
distribution of its predicted angles is composed of unusual clusters. The
distribution of the predicted angles of Semi-supervised GAN was most similar to
the Ramachandran plot. We found that adding the output of the NCE as an
additional input of the discriminator is helpful to stabilize the training of
the GANs and to capture the detailed structures. Adding regression loss and
using predicted angles by regression loss only model could improve the
conditional generation performance of the C-GAN and AC-GAN.
Protection against Cloning for Deep Learning
Authors: Richard Kenway
Date: 2018-03-29 00:00:00
Tasks: Adversarial Attack, Deep Learning

The susceptibility of deep learning to adversarial attack can be understood
in the framework of the Renormalisation Group (RG) and the vulnerability of a
specific network may be diagnosed provided the weights in each layer are known.
An adversary with access to the inputs and outputs could train a second network
to clone these weights and, having identified a weakness, use them to compute
the perturbation of the input data which exploits it. However, the RG framework
also provides a means to poison the outputs of the network imperceptibly,
without affecting their legitimate use, so as to prevent such cloning of its
weights and thereby foil the generation of adversarial data.
Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting
Authors: Vahé Asvatourian, Clélia Coutzac, Nathalie Chaput, Caroline Robert, Stefan Michiels, Emilie Lanoy
Date: 2018-03-28 00:00:00

Recently, the intervention calculus when the DAG is absent (IDA) method was
developed to estimate lower bounds of causal effects from observational
high-dimensional data. Originally it was introduced to assess the effect of
baseline biomarkers which do not vary over time. However, in many clinical
settings, measurements of biomarkers are repeated at fixed time points during
treatment exposure and, therefore, this method need to be extended. The purpose
of this paper is then to extend the first step of the IDA, the Peter Clarks
(PC)-algorithm, to a time-dependent exposure in the context of a binary
outcome. We generalised the PC-algorithm for taking into account the
chronological order of repeated measurements of the exposure and propose to
apply the IDA with our new version, the chronologically ordered PC-algorithm
(COPC-algorithm). A simulation study has been performed before applying the
method for estimating causal effects of time-dependent immunological biomarkers
on toxicity, death and progression in patients with metastatic melanoma. The
simulation study showed that the completed partially directed acyclic graphs
(CPDAGs) obtained using COPC-algorithm were structurally closer to the true
CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more
accurate when they were estimated based on CPDAGs obtained using
COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing
non-chronologic arrows with a variable measured at a time t pointing to a
variable measured at a time t' where t'< t. Bidirected edges were less present
in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was
less variability in causal effects estimated from these CPDAGs. The
COPC-algorithm provided CPDAGs that keep the chronological structure present in
the data, thus allowed to estimate lower bounds of the causal effect of
time-dependent biomarkers.
A real-time warning system for rear-end collision based on random forest classifier
Authors: Fateme Teimouri, Mehdi Ghatee
Date: 2018-03-29 00:00:00

Rear-end collision warning system has a great role to enhance the driving
safety. In this system some measures are used to estimate the dangers and the
system warns drivers to be more cautious. The real-time processes should be
executed in such system, to remain enough time and distance to avoid collision
with the front vehicle. To this end, in this paper a new system is developed by
using random forest classifier. To evaluate the performance of the proposed
system, vehicles trajectory data of 100 car's database from Virginia tech
transportation institute are used and the methods are compared based on their
accuracy and their processing time. By using TOPSIS multi-criteria selection
method, we show that the results of the implemented classifier is better than
the results of different classifiers including Bayesian network, naive Bayes,
MLP neural network, support vector machine, nearest neighbor, rule-based
methods and decision tree. The presented experiments reveals that the random
forest is an acceptable algorithm for the proposed driver assistant system with
88.4% accuracy for detecting warning situations and 94.7% for detecting safe
situations.