pwc-archive/papers-with-abstracts huggingface dataset:
Features Extraction Based on an Origami Representation of 3D Landmarks
Authors: Juan Manuel Fernandez Montenegro, Mahdi Maktab Dar Oghaz, Athanasios Gkelias, Georgios Tzimiropoulos, Vasileios Argyriou
Date: 2018-12-12 00:00:00
Tasks: Classification, Emotion Classification, General Classification

Feature extraction analysis has been widely investigated during the last
decades in computer vision community due to the large range of possible
applications. Significant work has been done in order to improve the
performance of the emotion detection methods. Classification algorithms have
been refined, novel preprocessing techniques have been applied and novel
representations from images and videos have been introduced. In this paper, we
propose a preprocessing method and a novel facial landmarks' representation
aiming to improve the facial emotion detection accuracy. We apply our novel
methodology on the extended Cohn-Kanade (CK+) dataset and other datasets for
affect classification based on Action Units (AU). The performance evaluation
demonstrates an improvement on facial emotion classification (accuracy and F1
score) that indicates the superiority of the proposed methodology.
Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome
Authors: Laura A. Barrett, Seyedeh Neelufar Payrovnaziri, Jiang Bian, Zhe He
Date: 2018-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Heart disease remains the leading cause of death in the United States.
Compared with risk assessment guidelines that require manual calculation of
scores, machine learning-based prediction for disease outcomes such as
mortality can be utilized to save time and improve prediction accuracy. This
study built and evaluated various machine learning models to predict one-year
mortality in patients diagnosed with acute myocardial infarction or post
myocardial infarction syndrome in the MIMIC-III database. The results of the
best performing shallow prediction models were compared to a deep feedforward
neural network (Deep FNN) with back propagation. We included a cohort of 5436
admissions. Six datasets were developed and compared. The models applying
Logistic Model Trees (LMT) and Simple Logistic algorithms to the combined
dataset resulted in the highest prediction accuracy at 85.12% and the highest
AUC at .901. In addition, other factors were observed to have an impact on
outcomes as well.
Enhancing Selection Hyper-heuristics via Feature Transformations
Authors: I. Amaya, J. C. Ortiz-Bayliss, A. Rosales-Pérez, A. E. Gutiérrez-Rodríguez, S. E. Conant-Pablos, H. Terashima-Marín, C. A. Coello Coello
Date: 2018-12-12 00:00:00

Hyper-heuristics are a novel tool. They deal with complex optimization
problems where standalone solvers exhibit varied performance. Among such a tool
reside selection hyper-heuristics. By combining the strengths of each solver,
this kind of hyper-heuristic offers a more robust tool. However, their
effectiveness is highly dependent on the 'features' used to link them with the
problem that is being solved. Aiming at enhancing selection hyper-heuristics,
in this paper we propose two types of transformation: explicit and implicit.
The first one directly changes the distribution of critical points within the
feature domain while using a Euclidean distance to measure proximity. The
second one operates indirectly by preserving the distribution of critical
points but changing the distance metric through a kernel function. We focus on
analyzing the effect of each kind of transformation, and of their combinations.
We test our ideas in the domain of constraint satisfaction problems because of
their popularity and many practical applications. In this work, we compare the
performance of our proposals against those of previously published data.
Furthermore, we expand on previous research by increasing the number of
analyzed features. We found that, by incorporating transformations into the
model of selection hyper-heuristics, overall performance can be improved,
yielding more stable results. However, combining implicit and explicit
transformations was not as fruitful. Additionally, we ran some confirmatory
tests on the domain of knapsack problems. Again, we observed improved
stability, leading to the generation of hyper-heuristics whose profit had a
standard deviation between 20% and 30% smaller.
Recent Advances in Autoencoder-Based Representation Learning
Authors: Michael Tschannen, Olivier Bachem, Mario Lucic
Date: 2018-12-12 00:00:00
Tasks: Disentanglement, Representation Learning

Learning useful representations with little or no supervision is a key
challenge in artificial intelligence. We provide an in-depth review of recent
advances in representation learning with a focus on autoencoder-based models.
To organize these results we make use of meta-priors believed useful for
downstream tasks, such as disentanglement and hierarchical organization of
features. In particular, we uncover three main mechanisms to enforce such
properties, namely (i) regularizing the (approximate or aggregate) posterior
distribution, (ii) factorizing the encoding and decoding distribution, or (iii)
introducing a structured prior distribution. While there are some promising
results, implicit or explicit supervision remains a key enabler and all current
methods use strong inductive biases and modeling assumptions. Finally, we
provide an analysis of autoencoder-based representation learning through the
lens of rate-distortion theory and identify a clear tradeoff between the amount
of prior knowledge available about the downstream tasks, and how useful the
representation is for this task.
Asynchronous Online Testing of Multiple Hypotheses
Authors: Tijana Zrnic, Aaditya Ramdas, Michael. I. Jordan
Date: 2018-12-12 00:00:00

We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times. This setting increasingly characterizes real-world applications in science and industry, where teams of researchers across large organizations may conduct tests of hypotheses in a decentralized manner. The overlap in time and space also tends to induce dependencies among test statistics, a challenge for classical methodology, which either assumes (overly optimistically) independence or (overly pessimistically) arbitrary dependence between test statistics. We present a general framework that addresses both of these issues via a unified computational abstraction that we refer to as "conflict sets." We show how this framework yields algorithms with formal FDR guarantees under a more intermediate, local notion of dependence. We illustrate our algorithms in simulations by comparing to existing algorithms for online FDR control.
Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks
Authors: Mucong Ding, Kwok Yip Szeto
Date: 2018-12-12 00:00:00

We design a method to optimize the global mean first-passage time (GMFPT) of
multiple random walkers searching in complex networks for a general target,
without specifying the property of the target node. According to the Laplace
transformed formula of the GMFPT, we can equivalently minimize the overlap
between the probability distribution of sites visited by the random walkers. We
employ a mutation only genetic algorithm to solve this optimization problem
using a population of walkers with different starting positions and a
corresponding mutation matrix to modify them. The numerical experiments on two
kinds of random networks (WS and BA) show satisfactory results in selecting the
origins for the walkers to achieve minimum overlap. Our method thus provides
guidance for setting up the search process by multiple random walkers on
complex networks.
Fast Online Object Tracking and Segmentation: A Unifying Approach
Authors: Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H. S. Torr
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Object Tracking, Real-Time Visual Tracking, Segmentation, Semi-Supervised Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Object Tracking, Visual Object Tracking, Visual Tracking

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state of the art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semi-supervised video object segmentation task on DAVIS-2016 and DAVIS-2017. The project website is http://www.robots.ox.ac.uk/~qwang/SiamMask.
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses
Authors: Mucong Ding, Kai Yang, Dit-yan Yeung, Ting-Chuen Pong
Date: 2018-12-12 00:00:00
Tasks: Prediction

The effectiveness of learning in massive open online courses (MOOCs) can be
significantly enhanced by introducing personalized intervention schemes which
rely on building predictive models of student learning behaviors such as some
engagement or performance indicators. A major challenge that has to be
addressed when building such models is to design handcrafted features that are
effective for the prediction task at hand. In this paper, we make the first
attempt to solve the feature learning problem by taking the unsupervised
learning approach to learn a compact representation of the raw features with a
large degree of redundancy. Specifically, in order to capture the underlying
learning patterns in the content domain and the temporal nature of the
clickstream data, we train a modified auto-encoder (AE) combined with the long
short-term memory (LSTM) network to obtain a fixed-length embedding for each
input sequence. When compared with the original features, the new features that
correspond to the embedding obtained by the modified LSTM-AE are not only more
parsimonious but also more discriminative for our prediction task. Using simple
supervised learning models, the learned features can improve the prediction
accuracy by up to 17% compared with the supervised neural networks and reduce
overfitting to the dominant low-performing group of students, specifically in
the task of predicting students' performance. Our approach is generic in the
sense that it is not restricted to a specific supervised learning model nor a
specific prediction task for MOOC learning analytics.
Transfer Learning using Representation Learning in Massive Open Online Courses
Authors: Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly
Date: 2018-12-12 00:00:00
Tasks: Representation Learning, Transfer Learning

In a Massive Open Online Course (MOOC), predictive models of student behavior
can support multiple aspects of learning, including instructor feedback and
timely intervention. Ongoing courses, when the student outcomes are yet
unknown, must rely on models trained from the historical data of previously
offered courses. It is possible to transfer models, but they often have poor
prediction performance. One reason is features that inadequately represent
predictive attributes common to both courses. We present an automated
transductive transfer learning approach that addresses this issue. It relies on
problem-agnostic, temporal organization of the MOOC clickstream data, where,
for each student, for multiple courses, a set of specific MOOC event types is
expressed for each time unit. It consists of two alternative transfer methods
based on representation learning with auto-encoders: a passive approach using
transductive principal component analysis and an active approach that uses a
correlation alignment loss term. With these methods, we investigate the
transferability of dropout prediction across similar and dissimilar MOOCs and
compare with known methods. Results show improved model transferability and
suggest that the methods are capable of automatically learning a feature
representation that expresses common predictive characteristics of MOOCs.
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
Authors: Yuhua Chen, Wen Li, Xiaoran Chen, Luc van Gool
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Segmentation, Semantic Segmentation

Recently, increasing attention has been drawn to training semantic
segmentation models using synthetic data and computer-generated annotation.
However, domain gap remains a major barrier and prevents models learned from
synthetic data from generalizing well to real-world applications. In this work,
we take the advantage of additional geometric information from synthetic data,
a powerful yet largely neglected cue, to bridge the domain gap. Such geometric
information can be generated easily from synthetic data, and is proven to be
closely coupled with semantic information. With the geometric information, we
propose a model to reduce domain shift on two levels: on the input level, we
augment the traditional image translation network with the additional geometric
information to translate synthetic images into realistic styles; on the output
level, we build a task network which simultaneously performs depth estimation
and semantic segmentation on the synthetic data. Meanwhile, we encourage the
network to preserve correlation between depth and semantics by adversarial
training on the output space. We then validate our method on two pairs of
synthetic to real dataset: Virtual KITTI to KITTI, and SYNTHIA to Cityscapes,
where we achieve a significant performance gain compared to the non-adapt
baseline and methods using only semantic label. This demonstrates the
usefulness of geometric information from synthetic data for cross-domain
semantic segmentation.
Long-Term Feature Banks for Detailed Video Understanding
Authors: Chao-yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Classification, Action Recognition, Egocentric Activity Recognition, Video Understanding

To understand the world, we humans constantly need to relate the present to
the past, and put events in context. In this paper, we enable existing video
models to do the same. We propose a long-term feature bank---supportive
information extracted over the entire span of a video---to augment
state-of-the-art video models that otherwise would only view short clips of 2-5
seconds. Our experiments demonstrate that augmenting 3D convolutional networks
with a long-term feature bank yields state-of-the-art results on three
challenging video datasets: AVA, EPIC-Kitchens, and Charades.
Structured Neural Topic Models for Reviews
Authors: Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent
Date: 2018-12-12 00:00:00
Tasks: Sentence, Topic Models

We present Variational Aspect-based Latent Topic Allocation (VALTA), a family
of autoencoding topic models that learn aspect-based representations of
reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for
combined reviews associated with each paired user and item onto structured
embeddings, which in turn define per-aspect topic weights. We model individual
reviews in a structured manner by inferring an aspect assignment for each
sentence in a given review, where the per-aspect topic weights obtained by the
user-item encoder serve to define a mixture over topics, conditioned on the
aspect. The result is an autoencoding neural topic model for reviews, which can
be trained in a fully unsupervised manner to learn topics that are structured
into aspects. Experimental evaluation on large number of datasets demonstrates
that aspects are interpretable, yield higher coherence scores than
non-structured autoencoding topic model variants, and can be utilized to
perform aspect-based comparison and genre discovery.
Thwarting Adversarial Examples: An $L_0$-RobustSparse Fourier Transform
Authors: Mitali Bafna, Jack Murtagh, Nikhil Vyas
Date: 2018-12-12 00:00:00
Tasks: General Classification, image-classification, Image Classification

We give a new algorithm for approximating the Discrete Fourier transform of
an approximately sparse signal that has been corrupted by worst-case $L_0$
noise, namely a bounded number of coordinates of the signal have been corrupted
arbitrarily. Our techniques generalize to a wide range of linear
transformations that are used in data analysis such as the Discrete Cosine and
Sine transforms, the Hadamard transform, and their high-dimensional analogs. We
use our algorithm to successfully defend against well known $L_0$ adversaries
in the setting of image classification. We give experimental results on the
Jacobian-based Saliency Map Attack (JSMA) and the Carlini Wagner (CW) $L_0$
attack on the MNIST and Fashion-MNIST datasets as well as the Adversarial Patch
on the ImageNet dataset.
Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia
Authors: Narumol Prangnawarat, John P. McCrae, Conor Hayes
Date: 2018-12-12 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Many researchers have made use of the Wikipedia network for relatedness and
similarity tasks. However, most approaches use only the most recent information
and not historical changes in the network. We provide an analysis of entity
relatedness using temporal graph-based approaches over different versions of
the Wikipedia article link network and DBpedia, which is an open-source
knowledge base extracted from Wikipedia. We consider creating the Wikipedia
article link network as both a union and intersection of edges over multiple
time points and present a novel variation of the Jaccard index to weight edges
based on their transience. We evaluate our results against the KORE dataset,
which was created in 2010, and show that using the 2010 Wikipedia article link
network produces the strongest result, suggesting that semantic similarity is
time sensitive. We then show that integrating multiple time frames in our
methods can give a better overall similarity demonstrating that temporal
evolution can have an important effect on entity relatedness.
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data
Authors: Seyed Mostafa Kia, Andre F. Marquand
Date: 2018-12-12 00:00:00
Tasks: Diagnostic, Novelty Detection, regression

Normative modeling has recently been introduced as a promising approach for
modeling variation of neuroimaging measures across individuals in order to
derive biomarkers of psychiatric disorders. Current implementations rely on
Gaussian process regression, which provides coherent estimates of uncertainty
needed for the method but also suffers from drawbacks including poor scaling to
large datasets and a reliance on fixed parametric kernels. In this paper, we
propose a deep normative modeling framework based on neural processes (NPs) to
solve these problems. To achieve this, we define a stochastic process
formulation for mixed-effect models and show how NPs can be adopted for
spatially structured mixed-effect modeling of neuroimaging data. This enables
us to learn optimal feature representations and covariance structure for the
random-effect and noise via global latent variables. In this scheme, predictive
uncertainty can be approximated by sampling from the distribution of these
global latent variables. On a publicly available clinical fMRI dataset, we
compare the novelty detection performance of multivariate normative models
estimated by the proposed NP approach to a baseline multi-task Gaussian process
regression approach and show substantial improvements for certain diagnostic
problems.
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
Authors: Wolfgang Fruehwirt, Adam D. Cobb, Martin Mairhofer, Leonard Weydemann, Heinrich Garn, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Markus Waser, Dieter Grossegger, Pengfei Zhang, Georg Dorffner, Stephen Roberts
Date: 2018-12-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

As societies around the world are ageing, the number of Alzheimer's disease
(AD) patients is rapidly increasing. To date, no low-cost, non-invasive
biomarkers have been established to advance the objectivization of AD diagnosis
and progression assessment. Here, we utilize Bayesian neural networks to
develop a multivariate predictor for AD severity using a wide range of
quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both
automatically controls model complexity and provides a predictive distribution
over the target function, giving uncertainty bounds for our regression task. It
is therefore well suited to clinical neuroscience, where data sets are
typically sparse and practitioners require a precise assessment of the
predictive uncertainty. We use data of one of the largest prospective AD EEG
trials ever conducted to demonstrate the potential of Bayesian deep learning in
this domain, while comparing two distinct Bayesian neural network approaches,
i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.
Real-Time Anomaly Detection With HMOF Feature
Authors: Huihui Zhu, Bin Liu, Guojun Yin, Yan Lu, Weihai Li, Nenghai Yu
Date: 2018-12-12 00:00:00
Tasks: Anomaly Detection, Clustering, Optical Flow Estimation

Anomaly detection is a challenging problem in intelligent video surveillance.
Most existing methods are computation consuming, which cannot satisfy the
real-time requirement. In this paper, we propose a real-time anomaly detection
framework with low computational complexity and high efficiency. A new feature,
named Histogram of Magnitude Optical Flow (HMOF), is proposed to capture the
motion of video patches. Compared with existing feature descriptors, HMOF is
more sensitive to motion magnitude and more efficient to distinguish anomaly
information. The HMOF features are computed for foreground patches, and are
reconstructed by the auto-encoder for better clustering. Then, we use Gaussian
Mixture Model (GMM) Classifiers to distinguish anomalies from normal activities
in videos. Experimental results show that our framework outperforms
state-of-the-art methods, and can reliably detect anomalies in real-time.