pwc-archive/papers-with-abstracts huggingface dataset:
OCTen: Online Compression-based Tensor Decomposition
Authors: Ekta Gujral, Ravdeep Pasricha, Tianxiong Yang, Evangelos E. Papalexakis
Date: 2018-07-03 00:00:00
Tasks: CPU, Tensor Decomposition

Tensor decompositions are powerful tools for large data analytics as they
jointly model multiple aspects of data into one framework and enable the
discovery of the latent structures and higher-order correlations within the
data. One of the most widely studied and used decompositions, especially in
data mining and machine learning, is the Canonical Polyadic or CP
decomposition. However, today's datasets are not static and these datasets
often dynamically growing and changing with time. To operate on such large
data, we present OCTen the first ever compression-based online parallel
implementation for the CP decomposition. We conduct an extensive empirical
analysis of the algorithms in terms of fitness, memory used and CPU time, and
in order to demonstrate the compression and scalability of the method, we apply
OCTen to big tensor data. Indicatively, OCTen performs on-par or better than
state-of-the-art online and online methods in terms of decomposition accuracy
and efficiency, while saving up to 40-200 % memory space.
Anomaly Detection for Skin Disease Images Using Variational Autoencoder
Authors: Yuchen Lu, Peng Xu
Date: 2018-07-03 00:00:00
Tasks: Anomaly Detection

In this paper, we demonstrate the potential of applying Variational
Autoencoder (VAE) [10] for anomaly detection in skin disease images. VAE is a
class of deep generative models which is trained by maximizing the evidence
lower bound of data distribution [10]. When trained on only normal data, the
resulting model is able to perform efficient inference and to determine if a
test image is normal or not. We perform experiments on ISIC2018 Challenge
Disease Classification dataset (Task 3) and compare different methods to use
VAE to detect anomaly. The model is able to detect all diseases with 0.779
AUCROC. If we focus on specific diseases, the model is able to detect melanoma
with 0.864 AUCROC and detect actinic keratosis with 0.872 AUCROC, even if it
only sees the images of nevus. To the best of our knowledge, this is the first
applied work of deep generative models for anomaly detection in dermatology.
A Dataset for Lane Instance Segmentation in Urban Environments
Authors: Brook Roberts, Sebastian Kaltwang, Sina Samangooei, Mark Pender-Bare, Konstantinos Tertikas, John Redford
Date: 2018-07-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Vehicles, Instance Segmentation, Segmentation, Semantic Segmentation

Autonomous vehicles require knowledge of the surrounding road layout, which
can be predicted by state-of-the-art CNNs. This work addresses the current lack
of data for determining lane instances, which are needed for various driving
manoeuvres. The main issue is the time-consuming manual labelling process,
typically applied per image. We notice that driving the car is itself a form of
annotation. Therefore, we propose a semi-automated method that allows for
efficient labelling of image sequences by utilising an estimated road plane in
3D based on where the car has driven and projecting labels from this plane into
all images of the sequence. The average labelling time per image is reduced to
5 seconds and only an inexpensive dash-cam is required for data capture. We are
releasing a dataset of 24,000 images and additionally show experimental
semantic segmentation and instance segmentation results.
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
Authors: Piero Molino, Huaixiu Zheng, Yi-Chia Wang
Date: 2018-07-03 00:00:00
Tasks: Answer Selection, Classification, Decoder, Feature Engineering, General Classification

For a company looking to provide delightful user experiences, it is of
paramount importance to take care of any customer issues. This paper proposes
COTA, a system to improve speed and reliability of customer support for end
users through automated ticket classification and answers selection for support
representatives. Two machine learning and natural language processing
techniques are demonstrated: one relying on feature engineering (COTA v1) and
the other exploiting raw signals through deep learning architectures (COTA v2).
COTA v1 employs a new approach that converts the multi-classification task into
a ranking problem, demonstrating significantly better performance in the case
of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a
novel deep learning architecture that allows for heterogeneous input and output
feature types and injection of prior knowledge through network architecture
choices. This paper compares these models and their variants on the task of
ticket classification and answer selection, showing model COTA v2 outperforms
COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B
test is conducted in a production setting validating the real-world impact of
COTA in reducing issue resolution time by 10 percent without reducing customer
satisfaction.
Breast Cancer Diagnosis via Classification Algorithms
Authors: Reihaneh Entezari
Date: 2018-07-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Diagnostic, General Classification, regression

In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using
Machine Learning classification techniques, such as the SVM, Bayesian Logistic
Regression (Variational Approximation), and K-Nearest-Neighbors. We describe
each model, and compare their performance through different measures. We
conclude that SVM has the best performance among all other classifiers, while
it competes closely with the Bayesian Logistic Regression that is ranked second
best method for this dataset.
Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification
Authors: Sobhan Soleymani, Ali Dabouei, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2018-07-03 00:00:00
Tasks: General Classification, Person Identification

In this paper, we propose a deep multimodal fusion network to fuse multiple
modalities (face, iris, and fingerprint) for person identification. The
proposed deep multimodal fusion algorithm consists of multiple streams of
modality-specific Convolutional Neural Networks (CNNs), which are jointly
optimized at multiple feature abstraction levels. Multiple features are
extracted at several different convolutional layers from each modality-specific
CNN for joint feature fusion, optimization, and classification. Features
extracted at different convolutional layers of a modality-specific CNN
represent the input at several different levels of abstract representations. We
demonstrate that an efficient multimodal classification can be accomplished
with a significant reduction in the number of network parameters by exploiting
these multi-level abstract representations extracted from all the
modality-specific CNNs. We demonstrate an increase in multimodal person
identification performance by utilizing the proposed multi-level feature
abstract representations in our multimodal fusion, rather than using only the
features from the last layer of each modality-specific CNNs. We show that our
deep multi-modal CNNs with multimodal fusion at several different feature level
abstraction can significantly outperform the unimodal representation accuracy.
We also demonstrate that the joint optimization of all the modality-specific
CNNs excels the score and decision level fusions of independently optimized
CNNs.
Approximate Survey Propagation for Statistical Inference
Authors: Fabrizio Antenucci, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Date: 2018-07-03 00:00:00
Tasks: Survey

Approximate message passing algorithm enjoyed considerable attention in the
last decade. In this paper we introduce a variant of the AMP algorithm that
takes into account glassy nature of the system under consideration. We coin
this algorithm as the approximate survey propagation (ASP) and derive it for a
class of low-rank matrix estimation problems. We derive the state evolution for
the ASP algorithm and prove that it reproduces the one-step replica symmetry
breaking (1RSB) fixed-point equations, well-known in physics of disordered
systems. Our derivation thus gives a concrete algorithmic meaning to the 1RSB
equations that is of independent interest. We characterize the performance of
ASP in terms of convergence and mean-squared error as a function of the free
Parisi parameter s. We conclude that when there is a model mismatch between the
true generative model and the inference model, the performance of AMP rapidly
degrades both in terms of MSE and of convergence, while ASP converges in a
larger regime and can reach lower errors. Among other results, our analysis
leads us to a striking hypothesis that whenever s (or other parameters) can be
set in such a way that the Nishimori condition $M=Q>0$ is restored, then the
corresponding algorithm is able to reach mean-squared error as low as the
Bayes-optimal error obtained when the model and its parameters are known and
exactly matched in the inference procedure.
BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level
Authors: Javier Ferrer, Enrique Alba
Date: 2018-07-03 00:00:00
Tasks: Management

The fast demographic growth, together with the concentration of the
population in cities and the increasing amount of daily waste, are factors that
push to the limit the ability of waste assimilation by Nature. Therefore, we
need technological means to make an optimal management of the waste collection
process, which represents 70% of the operational cost in waste treatment. In
this article, we present a free intelligent software system, based on
computational learning algorithms, which plans the best routes for waste
collection supported by past (historical) and future (predictions) data.
  The objective of the system is the cost reduction of the waste collection
service by means of the minimization in distance traveled by any truck to
collect a container, hence the fuel consumption. At the same time the quality
of service to the citizen is increased avoiding the annoying overflows of
containers thanks to the accurate fill level predictions performed by BIN-CT.
In this article we show the features of our software system, illustrating it
operation with a real case study of a Spanish city. We conclude that the use of
BIN-CT avoids unnecessary visits to containers, reduces the distance traveled
to collect a container and therefore we obtain a reduction of total costs and
harmful emissions thrown to the atmosphere.
Answering Hindsight Queries with Lifted Dynamic Junction Trees
Authors: Marcel Gehrke, Tanya Braun, Ralf Möller
Date: 2018-07-02 00:00:00

The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. We extend LDJT to (i) solve
the smoothing inference problem to answer hindsight queries by introducing an
efficient backward pass and (ii) discuss different options to instantiate a
first-order cluster representation during a backward pass. Further, our
relational forward backward algorithm makes hindsight queries to the very
beginning feasible. LDJT answers multiple temporal queries faster than the
static lifted junction tree algorithm on an unrolled model, which performs
smoothing during message passing.
Fast Nonparametric Conditional Density Estimation
Authors: Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell
Date: 2012-06-20 00:00:00
Tasks: Density Estimation, Prediction Intervals

Conditional density estimation generalizes regression by modeling a full
density f(yjx) rather than only the expected value E(yjx). This is important
for many tasks, including handling multi-modality and generating prediction
intervals. Though fundamental and widely applicable, nonparametric conditional
density estimators have received relatively little attention from statisticians
and little or none from the machine learning community. None of that work has
been applied to greater than bivariate data, presumably due to the
computational difficulty of data-driven bandwidth selection. We describe the
double kernel conditional density estimator and derive fast dual-tree-based
algorithms for bandwidth selection using a maximum likelihood criterion. These
techniques give speedups of up to 3.8 million in our experiments, and enable
the first applications to previously intractable large multivariate datasets,
including a redshift prediction problem from the Sloan Digital Sky Survey.
MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip
Authors: Somdip Dey, Grigorios Kalliatakis, Sangeet Saha, Amit Kumar Singh, Shoaib Ehsan, Klaus McDonald-Maier
Date: 2018-07-05 00:00:00

Intelligent Transportation Systems (ITS) have become an important pillar in
modern "smart city" framework which demands intelligent involvement of
machines. Traffic load recognition can be categorized as an important and
challenging issue for such systems. Recently, Convolutional Neural Network
(CNN) models have drawn considerable amount of interest in many areas such as
weather classification, human rights violation detection through images, due to
its accurate prediction capabilities. This work tackles real-life traffic load
recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin
it as MAT-CNN- SOPC, which uses an intelligent re-training mechanism of the CNN
with known environments. The proposed methodology is capable of enhancing the
efficacy of the approach by 2.44x in comparison to the state-of-art and proven
through experimental analysis. We have also introduced a mathematical equation,
which is capable of quantifying the suitability of using different CNN models
over the other for a particular application based implementation.
Linear Bandits with Stochastic Delayed Feedback
Authors: Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brueckner
Date: 2018-07-05 00:00:00
Proceeding: ICML 2020 1
Tasks: Marketing, Multi-Armed Bandits

Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation. One of the main challenges faced by practitioners hoping to apply existing algorithms is that usually the feedback is randomly delayed and delays are only partially observable. For example, while a purchase is usually observable some time after the display, the decision of not buying is never explicitly sent to the system. In other words, the learner only observes delayed positive events. We formalize this problem as a novel stochastic delayed linear bandit and propose ${\tt OTFLinUCB}$ and ${\tt OTFLinTS}$, two computationally efficient algorithms able to integrate new information as it becomes available and to deal with the permanently censored feedback. We prove optimal $\tilde O(\smash{d\sqrt{T}})$ bounds on the regret of the first algorithm and study the dependency on delay-dependent parameters. Our model, assumptions and results are validated by experiments on simulated and real data.
A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking
Authors: Henning Tjaden, Ulrich Schwanecke, Elmar Schömer, Daniel Cremers
Date: 2018-07-05 00:00:00
Tasks: Multiple Object Tracking, Object Tracking, Pose Tracking

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects
using a monocular RGB camera. The key idea is to derive a region-based cost
function using temporally consistent local color histograms. While such
region-based cost functions are commonly optimized using first-order gradient
descent techniques, we systematically derive a Gauss-Newton optimization scheme
which gives rise to drastically faster convergence and highly accurate and
robust tracking performance. We furthermore propose a novel complex dataset
dedicated for the task of monocular object pose tracking and make it publicly
available to the community. To our knowledge, it is the first to address the
common and important scenario in which both the camera as well as the objects
are moving simultaneously in cluttered scenes. In numerous experiments -
including our own proposed dataset - we demonstrate that the proposed
Gauss-Newton approach outperforms existing approaches, in particular in the
presence of cluttered backgrounds, heterogeneous objects and partial
occlusions.
Combining Background Subtraction Algorithms with Convolutional Neural Network
Authors: Dongdong Zeng, Ming Zhu, Arjan Kuijper
Date: 2018-07-05 00:00:00
Tasks: Decoder, Object, object-detection, Object Detection, Object Tracking

Accurate and fast extraction of foreground object is a key prerequisite for a
wide range of computer vision applications such as object tracking and
recognition. Thus, enormous background subtraction methods for foreground
object detection have been proposed in recent decades. However, it is still
regarded as a tough problem due to a variety of challenges such as illumination
variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently,
there is no single method that can handle all the challenges in a robust way.
In this letter, we try to solve this problem from a new perspective by
combining different state-of-the-art background subtraction algorithms to
create a more robust and more advanced foreground detection algorithm. More
specifically, an encoder-decoder fully convolutional neural network
architecture is trained to automatically learn how to leverage the
characteristics of different algorithms to fuse the results produced by
different background subtraction algorithms and output a more precise result.
Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that
the proposed method outperforms all the considered single background
subtraction algorithm. And we show that our solution is more efficient than
other combination strategies.
Goal-oriented Trajectories for Efficient Exploration
Authors: Fabio Pardo, Vitaly Levdik, Petar Kormushev
Date: 2018-07-05 00:00:00
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration is a difficult challenge in reinforcement learning and even
recent state-of-the art curiosity-based methods rely on the simple
epsilon-greedy strategy to generate novelty. We argue that pure random walks do
not succeed to properly expand the exploration area in most environments and
propose to replace single random action choices by random goals selection
followed by several steps in their direction. This approach is compatible with
any curiosity-based exploration and off-policy reinforcement learning agents
and generates longer and safer trajectories than individual random actions. To
illustrate this, we present a task-independent agent that learns to reach
coordinates in screen frames and demonstrate its ability to explore with the
game Super Mario Bros. improving significantly the score of a baseline DQN
agent.
Representing scenarios for process evolution management
Authors: Anton Kolonin
Date: 2018-07-05 00:00:00
Tasks: Management

In the following writing we discuss a conceptual framework for representing
events and scenarios from the perspective of a novel form of causal analysis.
This causal analysis is applied to the events and scenarios so as to determine
measures that could be used to manage the development of the processes that
they are a part of in real time. An overall terminological framework and
entity-relationship model are suggested along with a specification of the
functional sets involved in both reasoning and analytics. The model is
considered to be a specific case of the generic problem of finding sequential
series in disparate data. The specific inference and reasoning processes are
identified for future implementation.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
Authors: Peiliang Li, Tong Qin, Shaojie Shen
Date: 2018-07-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Driving, Motion Estimation, Object, Object Localization, Pose Tracking

We propose a stereo vision-based approach for tracking the camera ego-motion
and 3D semantic objects in dynamic autonomous driving scenarios. Instead of
directly regressing the 3D bounding box using end-to-end approaches, we propose
to use the easy-to-labeled 2D detection and discrete viewpoint classification
together with a light-weight semantic inference method to obtain rough 3D
object measurements. Based on the object-aware-aided camera pose tracking which
is robust in dynamic environments, in combination with our novel dynamic object
bundle adjustment (BA) approach to fuse temporal sparse feature correspondences
and the semantic 3D measurement model, we obtain 3D object pose, velocity and
anchored dynamic point cloud estimation with instance accuracy and temporal
consistency. The performance of our proposed method is demonstrated in diverse
scenarios. Both the ego-motion estimation and object localization are compared
with the state-of-of-the-art solutions.
Real-Time Subpixel Fast Bilateral Stereo
Authors: Rui Fan, Yanan Liu, Mohammud Junaid Bocus, Lujia Wang, Ming Liu
Date: 2018-07-05 00:00:00
Tasks: GPU

Stereo vision technique has been widely used in robotic systems to acquire
3-D information. In recent years, many researchers have applied bilateral
filtering in stereo vision to adaptively aggregate the matching costs. This has
greatly improved the accuracy of the estimated disparity maps. However, the
process of filtering the whole cost volume is very time consuming and therefore
the researchers have to resort to some powerful hardware for the real-time
purpose. This paper presents the implementation of fast bilateral stereo on a
state-of-the-art GPU. By highly exploiting the parallel computing architecture
of the GPU, the fast bilateral stereo performs in real time when processing the
Middlebury stereo datasets.
TFLMS: Large Model Support in TensorFlow by Graph Rewriting
Authors: Tung D. Le, Haruki Imai, Yasushi Negishi, Kiyokuni Kawachiya
Date: 2018-07-05 00:00:00
Tasks: CPU, Image Segmentation, Semantic Segmentation

While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. We propose an approach to tackle this problem by rewriting the computational graph of a neural network, in which swap-out and swap-in operations are inserted to temporarily store intermediate results on CPU memory. In particular, we first revise the concept of a computational graph by defining a concrete semantics for variables in a graph. We then formally show how to derive swap-out and swap-in operations from an existing graph and present rules to optimize the graph. To realize our approach, we developed a module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the TensorFlow repository for contributing to the TensorFlow community. With TFLMS, we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size, respectively. In particular, we were able to train 3DUNet using images of size of $192^3$ for image segmentation, which, without TFLMS, had been done only by dividing the images to smaller images, which affects the accuracy.