pwc-archive/papers-with-abstracts huggingface dataset:
Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
Authors: Filipe Marques, Florian Dubost, Mariette Kemner-van de Corput, Harm A. W. Tiddens, Marleen De Bruijne
Date: 2018-03-21 00:00:00
Tasks: Texture Classification

Cystic fibrosis is a genetic disease which may appear in early life with
structural abnormalities in lung tissues. We propose to detect these
abnormalities using a texture classification approach. Our method is a cascade
of two convolutional neural networks. The first network detects the presence of
abnormal tissues. The second network identifies the type of the structural
abnormalities: bronchiectasis, atelectasis or mucus plugging.We also propose a
network computing pixel-wise heatmaps of abnormality presence learning only
from the patch-wise annotations. Our database consists of CT scans of 194
subjects. We use 154 subjects to train our algorithms and the 40 remaining ones
as a test set. We compare our method with random forest and a single neural
network approach. The first network reaches an accuracy of 0,94 for disease
detection, 0,18 higher than the random forest classifier and 0,37 higher than
the single neural network. Our cascade approach yields a final class-averaged
F1-score of 0,33, outperforming the baseline method and the single network by
0,10 and 0,12.
Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation
Authors: Mohamed El Banani, Jason J. Corso
Date: 2018-02-05 00:00:00
Tasks: Viewpoint Estimation

Humans have an unparalleled visual intelligence and can overcome visual
ambiguities that machines currently cannot. Recent works have shown that
incorporating guidance from humans during inference for monocular
viewpoint-estimation can help overcome difficult cases in which the
computer-alone would have otherwise failed. These hybrid intelligence
approaches are hence gaining traction. However, deciding what question to ask
the human at inference time remains an unknown for these problems.
  We address this question by formulating it as an Adviser Problem: can we
learn a mapping from the input to a specific question to ask the human to
maximize the expected positive impact to the overall task? We formulate a
solution to the adviser problem for viewpoint estimation using a deep network
where the question asks for the location of a keypoint in the input image. We
show that by using the Adviser Network's recommendations, the model and the
human outperforms the previous hybrid-intelligence state-of-the-art by 3.7%,
and the computer-only state-of-the-art by 5.28% absolute.
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Authors: Jack Lanchantin, Ji Gao
Date: 2018-03-21 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling

Statistical language models are powerful tools which have been used for many
tasks within natural language processing. Recently, they have been used for
other sequential data such as source code.(Ray et al., 2015) showed that it is
possible train an n-gram source code language mode, and use it to predict buggy
lines in code by determining "unnatural" lines via entropy with respect to the
language model. In this work, we propose using a more advanced language
modeling technique, Long Short-term Memory recurrent neural networks, to model
source code and classify buggy lines based on entropy. We show that our method
slightly outperforms an n-gram model in the buggy line classification task
using AUC.
BioTracker: An Open-Source Computer Vision Framework for Visual Animal Tracking
Authors: Hauke Jürgen Mönck, Andreas Jörg, Tobias von Falkenhausen, Julian Tanke, Benjamin Wild, David Dormagen, Jonas Piotrowski, Claudia Winklmayr, David Bierbach, Tim Landgraf
Date: 2018-03-21 00:00:00

The study of animal behavior increasingly relies on (semi-) automatic methods
for the extraction of relevant behavioral features from video or picture data.
To date, several specialized software products exist to detect and track
animals' positions in simple (laboratory) environments. Tracking animals in
their natural environments, however, often requires substantial customization
of the image processing algorithms to the problem-specific image
characteristics. Here we introduce BioTracker, an open-source computer vision
framework, that provides programmers with core functionalities that are
essential parts of a tracking software, such as video I/O, graphics overlays
and mouse and keyboard interfaces. BioTracker additionally provides a number of
different tracking algorithms suitable for a variety of image recording
conditions. The main feature of BioTracker is however the straightforward
implementation of new problem-specific tracking modules and vision algorithms
that can build upon BioTracker's core functionalities. With this open-source
framework the scientific community can accelerate their research and focus on
the development of new vision algorithms.
Non-rigid 3D Shape Registration using an Adaptive Template
Authors: Hang Dai, Nick Pears, William Smith
Date: 2018-03-21 00:00:00

We present a new fully-automatic non-rigid 3D shape registration (morphing)
framework comprising (1) a new 3D landmarking and pose normalisation method;
(2) an adaptive shape template method to accelerate the convergence of
registration algorithms and achieve a better final shape correspondence and (3)
a new iterative registration method that combines Iterative Closest Points with
Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence
establishment than standard CPD. We call this new morphing approach Iterative
Coherent Point Drift (ICPD). Our proposed framework is evaluated qualitatively
and quantitatively on three datasets and compared with several other methods.
The proposed framework is shown to give state-of-the-art performance.
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Authors: Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön
Date: 2017-08-25 00:00:00
Tasks: parameter estimation, Probabilistic Programming

We introduce a dynamic mechanism for the solution of analytically-tractable
substructure in probabilistic programs, using conjugate priors and affine
transformations to reduce variance in Monte Carlo estimators. For inference
with Sequential Monte Carlo, this automatically yields improvements such as
locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a
directed graph alongside the running program that evolves dynamically as
operations are triggered upon it. Nodes of the graph represent random
variables, edges the analytically-tractable relationships between them. Random
variables remain in the graph for as long as possible, to be sampled only when
they are used by the program in a way that cannot be resolved analytically. In
the meantime, they are conditioned on as many observations as possible. We
demonstrate the mechanism with a few pedagogical examples, as well as a
linear-nonlinear state-space model with simulated data, and an epidemiological
model with real data of a dengue outbreak in Micronesia. In all cases one or
more variables are automatically marginalized out to significantly reduce
variance in estimates of the marginal likelihood, in the final case
facilitating a random-weight or pseudo-marginal-type importance sampler for
parameter estimation. We have implemented the approach in Anglican and a new
probabilistic programming language called Birch.
Stochastic Learning under Random Reshuffling with Constant Step-sizes
Authors: Bicheng Ying, Kun Yuan, Stefan Vlaski, Ali H. Sayed
Date: 2018-03-21 00:00:00

In empirical risk optimization, it has been observed that stochastic gradient
implementations that rely on random reshuffling of the data achieve better
performance than implementations that rely on sampling the data uniformly.
Recent works have pursued justifications for this behavior by examining the
convergence rate of the learning process under diminishing step-sizes. This
work focuses on the constant step-size case and strongly convex loss function.
In this case, convergence is guaranteed to a small neighborhood of the
optimizer albeit at a linear rate. The analysis establishes analytically that
random reshuffling outperforms uniform sampling by showing explicitly that
iterates approach a smaller neighborhood of size $O(\mu^2)$ around the
minimizer rather than $O(\mu)$. Furthermore, we derive an analytical expression
for the steady-state mean-square-error performance of the algorithm, which
helps clarify in greater detail the differences between sampling with and
without replacement. We also explain the periodic behavior that is observed in
random reshuffling implementations.
A Cascaded Convolutional Neural Network for Single Image Dehazing
Authors: Chongyi Li, Jichang Guo, Fatih Porikli, Huazhu Fu, Yanwei Pang
Date: 2018-03-21 00:00:00
Tasks: Image Dehazing, Image Restoration, Single Image Dehazing

Images captured under outdoor scenes usually suffer from low contrast and
limited visibility due to suspended atmospheric particles, which directly
affects the quality of photos. Despite numerous image dehazing methods have
been proposed, effective hazy image restoration remains a challenging problem.
Existing learning-based methods usually predict the medium transmission by
Convolutional Neural Networks (CNNs), but ignore the key global atmospheric
light. Different from previous learning-based methods, we propose a flexible
cascaded CNN for single hazy image restoration, which considers the medium
transmission and global atmospheric light jointly by two task-driven
subnetworks. Specifically, the medium transmission estimation subnetwork is
inspired by the densely connected CNN while the global atmospheric light
estimation subnetwork is a light-weight CNN. Besides, these two subnetworks are
cascaded by sharing the common features. Finally, with the estimated model
parameters, the haze-free image is obtained by the atmospheric scattering model
inversion, which achieves more accurate and effective restoration performance.
Qualitatively and quantitatively experimental results on the synthetic and
real-world hazy images demonstrate that the proposed method effectively removes
haze from such images, and outperforms several state-of-the-art dehazing
methods.
End-to-End Video Captioning with Multitask Reinforcement Learning
Authors: Lijun Li, Boqing Gong
Date: 2018-03-21 00:00:00
Tasks: GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Rolling Shutter Correction, Video Captioning

Although end-to-end (E2E) learning has led to impressive progress on a
variety of visual understanding tasks, it is often impeded by hardware
constraints (e.g., GPU memory) and is prone to overfitting. When it comes to
video captioning, one of the most challenging benchmark tasks in computer
vision, those limitations of E2E learning are especially amplified by the fact
that both the input videos and output captions are lengthy sequences. Indeed,
state-of-the-art methods for video captioning process video frames by
convolutional neural networks and generate captions by unrolling recurrent
neural networks. If we connect them in an E2E manner, the resulting model is
both memory-consuming and data-hungry, making it extremely hard to train. In
this paper, we propose a multitask reinforcement learning approach to training
an E2E video captioning model. The main idea is to mine and construct as many
effective tasks (e.g., attributes, rewards, and the captions) as possible from
the human captioned videos such that they can jointly regulate the search space
of the E2E neural network, from which an E2E video captioning model can be
found and generalized to the testing phase. To the best of our knowledge, this
is the first video captioning model that is trained end-to-end from the raw
video input to the caption output. Experimental results show that such a model
outperforms existing ones to a large margin on two benchmark video captioning
datasets.
Information Planning for Text Data
Authors: Vadim Smolyakov
Date: 2018-02-09 00:00:00

Information planning enables faster learning with fewer training examples. It
is particularly applicable when training examples are costly to obtain. This
work examines the advantages of information planning for text data by focusing
on three supervised models: Naive Bayes, supervised LDA and deep neural
networks. We show that planning based on entropy and mutual information
outperforms random selection baseline and therefore accelerates learning.
Crowd-Machine Collaboration for Item Screening
Authors: Evgeny Krivosheev, Bahareh Harandizadeh, Fabio Casati, Boualem Benatallah
Date: 2018-03-21 00:00:00

In this paper we describe how crowd and machine classifier can be efficiently
combined to screen items that satisfy a set of predicates. We show that this is
a recurring problem in many domains, present machine-human (hybrid) algorithms
that screen items efficiently and estimate the gain over human-only or
machine-only screening in terms of performance and cost.
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian
Authors: Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Andres Duque
Date: 2017-11-16 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation

We explore the use of unsupervised methods in Cross-Lingual Word Sense
Disambiguation (CL-WSD) with the application of English to Persian. Our
proposed approach targets the languages with scarce resources (low-density) by
exploiting word embedding and semantic similarity of the words in context. We
evaluate the approach on a recent evaluation benchmark and compare it with the
state-of-the-art unsupervised system (CO-Graph). The results show that our
approach outperforms both the standard baseline and the CO-Graph system in both
of the task evaluation metrics (Out-Of-Five and Best result).
Fine-tuning the Ant Colony System algorithm through Particle Swarm Optimization
Authors: D Gómez-Cabrero, D. N. Ranasinghe
Date: 2018-03-21 00:00:00

Ant Colony System (ACS) is a distributed (agent- based) algorithm which has
been widely studied on the Symmetric Travelling Salesman Problem (TSP). The
optimum parameters for this algorithm have to be found by trial and error. We
use a Particle Swarm Optimization algorithm (PSO) to optimize the ACS
parameters working in a designed subset of TSP instances. First goal is to
perform the hybrid PSO-ACS algorithm on a single instance to find the optimum
parameters and optimum solutions for the instance. Second goal is to analyze
those sets of optimum parameters, in relation to instance characteristics.
Computational results have shown good quality solutions for single instances
though with high computational times, and that there may be sets of parameters
that work optimally for a majority of instances.
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition
Authors: Roberto Menicatti, Barbara Bruno, Antonio Sgorbissa
Date: 2018-03-21 00:00:00
Tasks: Activity Recognition, Cultural Vocal Bursts Intensity Prediction, Human Activity Recognition

Daily life activities, such as eating and sleeping, are deeply influenced by
a person's culture, hence generating differences in the way a same activity is
performed by individuals belonging to different cultures. We argue that taking
cultural information into account can improve the performance of systems for
the automated recognition of human activities. We propose four different
solutions to the problem and present a system which uses a Naive Bayes model to
associate cultural information with semantic information extracted from still
images. Preliminary experiments with a dataset of images of individuals lying
on the floor, sleeping on a futon and sleeping on a bed suggest that: i)
solutions explicitly taking cultural information into account are more accurate
than culture-unaware solutions; and ii) the proposed system is a promising
starting point for the development of culture-aware Human Activity Recognition
methods.
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
Authors: Diogo C. Luvizon, David Picard, Hedi Tabia
Date: 2018-02-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Action Recognition, Action Recognition In Videos, Pose Estimation, Temporal Action Localization

Action recognition and human pose estimation are closely related but both
problems are generally handled as distinct tasks in the literature. In this
work, we propose a multitask framework for jointly 2D and 3D pose estimation
from still images and human action recognition from video sequences. We show
that a single architecture can be used to solve the two problems in an
efficient way and still achieves state-of-the-art results. Additionally, we
demonstrate that optimization from end-to-end leads to significantly higher
accuracy than separated learning. The proposed architecture can be trained with
data from different categories simultaneously in a seamlessly way. The reported
results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the
effectiveness of our method on the targeted tasks.
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
Authors: Amos Sironi, Manuele Brambilla, Nicolas Bourdis, Xavier Lagorce, Ryad Benosman
Date: 2018-03-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Autonomous Vehicles, Classification, General Classification, Object, Robot Navigation, Robust classification

Event-based cameras have recently drawn the attention of the Computer Vision
community thanks to their advantages in terms of high temporal resolution, low
power consumption and high dynamic range, compared to traditional frame-based
cameras. These properties make event-based cameras an ideal choice for
autonomous vehicles, robot navigation or UAV vision, among others. However, the
accuracy of event-based object classification algorithms, which is of crucial
importance for any reliable system working in real-world conditions, is still
far behind their frame-based counterparts. Two main reasons for this
performance gap are: 1. The lack of effective low-level representations and
architectures for event-based object classification and 2. The absence of large
real-world event-based datasets. In this paper we address both problems. First,
we introduce a novel event-based feature representation together with a new
machine learning architecture. Compared to previous approaches, we use local
memory units to efficiently leverage past temporal information and build a
robust event-based representation. Second, we release the first large
real-world event-based dataset for object classification. We compare our method
to the state-of-the-art with extensive experiments, showing better
classification performance and real-time computation.
DSAC - Differentiable RANSAC for Camera Localization
Authors: Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
Date: 2016-11-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Camera Localization, Deep Learning, Medical Image Segmentation, Reinforcement Learning, Visual Localization

RANSAC is an important algorithm in robust optimization and a central
building block for many computer vision applications. In recent years,
traditionally hand-crafted pipelines have been replaced by deep learning
pipelines, which can be trained in an end-to-end fashion. However, RANSAC has
so far not been used as part of such deep learning pipelines, because its
hypothesis selection procedure is non-differentiable. In this work, we present
two different ways to overcome this limitation. The most promising approach is
inspired by reinforcement learning, namely to replace the deterministic
hypothesis selection by a probabilistic selection for which we can derive the
expected loss w.r.t. to all learnable parameters. We call this approach DSAC,
the differentiable counterpart of RANSAC. We apply DSAC to the problem of
camera localization, where deep learning has so far failed to improve on
traditional approaches. We demonstrate that by directly minimizing the expected
loss of the output camera poses, robustly estimated by RANSAC, we achieve an
increase in accuracy. In the future, any deep learning pipeline can use DSAC as
a robust optimization component.
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids
Authors: Fanlin Meng, Xiao-jun Zeng, Yan Zhang, Chris J. Dent, Dunwei Gong
Date: 2016-12-18 00:00:00
Tasks: Decision Making, Distributed Optimization, energy management, Management

In this paper, we consider a realistic and meaningful scenario in the context
of smart grids where an electricity retailer serves three different types of
customers, i.e., customers with an optimal home energy management system
embedded in their smart meters (C-HEMS), customers with only smart meters
(C-SM), and customers without smart meters (C-NONE). The main objective of this
paper is to support the retailer to make optimal day-ahead dynamic pricing
decisions in such a mixed customer pool. To this end, we propose a two-level
decision-making framework where the retailer acting as upper-level agent
firstly announces its electricity prices of next 24 hours and customers acting
as lower-level agents subsequently schedule their energy usages accordingly.
For the lower level problem, we model the price responsiveness of different
customers according to their unique characteristics. For the upper level
problem, we optimize the dynamic prices for the retailer to maximize its profit
subject to realistic market constraints. The above two-level model is tackled
by genetic algorithms (GA) based distributed optimization methods while its
feasibility and effectiveness are confirmed via simulation results.
A Formalization of Kant's Second Formulation of the Categorical Imperative
Authors: Felix Lindner, Martin Mose Bentzen
Date: 2018-01-09 00:00:00

We present a formalization and computational implementation of the second formulation of Kant's categorical imperative. This ethical principle requires an agent to never treat someone merely as a means but always also as an end. Here we interpret this principle in terms of how persons are causally affected by actions. We introduce Kantian causal agency models in which moral patients, actions, goals, and causal influence are represented, and we show how to formalize several readings of Kant's categorical imperative that correspond to Kant's concept of strict and wide duties towards oneself and others. Stricter versions handle cases where an action directly causally affects oneself or others, whereas the wide version maximizes the number of persons being treated as an end. We discuss limitations of our formalization by pointing to one of Kant's cases that the machinery cannot handle in a satisfying way.
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
Authors: Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen
Date: 2018-03-21 00:00:00
Tasks: General Classification, Imputation, Time Series, Time Series Analysis

A large fraction of the electronic health records consists of clinical
measurements collected over time, such as blood tests, which provide important
information about the health status of a patient. These sequences of clinical
measurements are naturally represented as time series, characterized by
multiple variables and the presence of missing data, which complicate analysis.
In this work, we propose a surgical site infection detection framework for
patients undergoing colorectal cancer surgery that is completely unsupervised,
hence alleviating the problem of getting access to labelled training data. The
framework is based on powerful kernels for multivariate time series that
account for missing data when computing similarities. Our approach show
superior performance compared to baselines that have to resort to imputation
techniques and performs comparable to a supervised classification baseline.