pwc-archive/papers-with-abstracts huggingface dataset:
Deep Texture Manifold for Ground Terrain Recognition
Authors: Jia Xue, Hang Zhang, Kristin Dana
Date: 2018-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Sand

We present a texture network called Deep Encoding Pooling Network (DEP) for
the task of ground terrain recognition. Recognition of ground terrain is an
important task in establishing robot or vehicular control parameters, as well
as for localization within an outdoor environment. The architecture of DEP
integrates orderless texture details and local spatial information and the
performance of DEP surpasses state-of-the-art methods for this task. The GTOS
database (comprised of over 30,000 images of 40 classes of ground terrain in
outdoor scenes) enables supervised recognition. For evaluation under realistic
conditions, we use test images that are not from the existing GTOS dataset, but
are instead from hand-held mobile phone videos of similar terrain. This new
evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground
terrain such as grass, gravel, asphalt and sand. The resultant network shows
excellent performance not only for GTOS-mobile, but also for more general
databases (MINC and DTD). Leveraging the discriminant features learned from
this network, we build a new texture manifold called DEP-manifold. We learn a
parametric distribution in feature space in a fully supervised manner, which
gives the distance relationship among classes and provides a means to
implicitly represent ambiguous class boundaries. The source code and database
are publicly available.
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Authors: Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore
Date: 2017-11-22 00:00:00
Tasks: Benchmarking, feature selection

Modern biomedical data mining requires feature selection methods that can (1)
be applied to large scale feature spaces (e.g. `omics' data), (2) function in
noisy problems, (3) detect complex patterns of association (e.g. gene-gene
interactions), (4) be flexibly adapted to various problem domains and data
types (e.g. genetic variants, gene expression, and clinical data) and (5) are
computationally tractable. To that end, this work examines a set of
filter-style feature selection algorithms inspired by the `Relief' algorithm,
i.e. Relief-Based algorithms (RBAs). We implement and expand these RBAs in an
open source framework called ReBATE (Relief-Based Algorithm Training
Environment). We apply a comprehensive genetic simulation study comparing
existing RBAs, a proposed RBA called MultiSURF, and other established feature
selection methods, over a variety of problems. The results of this study (1)
support the assertion that RBAs are particularly flexible, efficient, and
powerful feature selection methods that differentiate relevant features having
univariate, multivariate, epistatic, or heterogeneous associations, (2) confirm
the efficacy of expansions for classification vs. regression, discrete vs.
continuous features, missing data, multiple classes, or class imbalance, (3)
identify previously unknown limitations of specific RBAs, and (4) suggest that
while MultiSURF* performs best for explicitly identifying pure 2-way
interactions, MultiSURF yields the most reliable feature selection performance
across a wide range of problem types.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Authors: Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian
Date: 2017-12-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Point Cloud Linear Classification, Decoder, General Classification, Unsupervised 3D Point Cloud Linear Evaluation

Recent deep networks that directly handle points in a point set, e.g.,
PointNet, have been state-of-the-art for supervised learning tasks on point
clouds such as classification and segmentation. In this work, a novel
end-to-end deep auto-encoder is proposed to address unsupervised learning
challenges on point clouds. On the encoder side, a graph-based enhancement is
enforced to promote local structures on top of PointNet. Then, a novel
folding-based decoder deforms a canonical 2D grid onto the underlying 3D object
surface of a point cloud, achieving low reconstruction errors even for objects
with delicate structures. The proposed decoder only uses about 7% parameters of
a decoder with fully-connected neural networks, yet leads to a more
discriminative representation that achieves higher linear SVM classification
accuracy than the benchmark. In addition, the proposed decoder structure is
shown, in theory, to be a generic architecture that is able to reconstruct an
arbitrary point cloud from a 2D grid. Our code is available at
http://www.merl.com/research/license#FoldingNet
A Bi-population Particle Swarm Optimizer for Learning Automata based Slow Intelligent System
Authors: Mohammad Hasanzadeh Mofrad, S. K. Chang
Date: 2018-04-03 00:00:00
Tasks: Decision Making

Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) that
utilizes a swarm of particles to solve an optimization problem. Slow
Intelligence System (SIS) is a learning framework which slowly learns the
solution to a problem performing a series of operations. Moreover, Learning
Automata (LA) are minuscule but effective decision making entities which are
best suited to act as a controller component. In this paper, we combine two
isolate populations of PSO to forge the Adaptive Intelligence Optimizer (AIO)
which harnesses the advantages of a bi-population PSO to escape from the local
minimum and avoid premature convergence. Furthermore, using the rich framework
of SIS and the nifty control theory that LA derived from, we find the perfect
matching between SIS and LA where acting slowly is the pillar of both of them.
Both SIS and LA need time to converge to the optimal decision where this
enables AIO to outperform standard PSO having an incomparable performance on
evolutionary optimization benchmark functions.
Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment
Authors: Yuhang Wu, Le Anh Vu Ha, Xiang Xu, Ioannis A. Kakadiaris
Date: 2018-03-17 00:00:00
Tasks: 3D Face Alignment, Face Alignment, Missing Values

We present a robust method for estimating the facial pose and shape
information from a densely annotated facial image. The method relies on
Convolutional Point-set Representation (CPR), a carefully designed matrix
representation to summarize different layers of information encoded in the set
of detected points in the annotated image. The CPR disentangles the
dependencies of shape and different pose parameters and enables updating
different parameters in a sequential manner via convolutional neural networks
and recurrent layers. When updating the pose parameters, we sample reprojection
errors along with a predicted direction and update the parameters based on the
pattern of reprojection errors. This technique boosts the model's capability in
searching a local minimum under challenging scenarios. We also demonstrate that
annotation from different sources can be merged under the framework of CPR and
contributes to outperforming the current state-of-the-art solutions for 3D face
alignment. Experiments indicate the proposed CPRFA (CPR-based Face Alignment)
significantly improves 3D alignment accuracy when the densely annotated image
contains noise and missing values, which is common under "in-the-wild"
acquisition scenarios.
Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification
Authors: Abhinav Valada, Wolfram Burgard
Date: 2018-04-02 00:00:00
Tasks: Classification, General Classification

Terrain classification is a critical component of any autonomous mobile robot
system operating in unknown real-world environments. Over the years, several
proprioceptive terrain classification techniques have been introduced to
increase robustness or act as a fallback for traditional vision based
approaches. However, they lack widespread adaptation due to various factors
that include inadequate accuracy, robustness and slow run-times. In this paper,
we use vehicle-terrain interaction sounds as a proprioceptive modality and
propose a deep Long-Short Term Memory (LSTM) based recurrent model that
captures both the spatial and temporal dynamics of such a problem, thereby
overcoming these past limitations. Our model consists of a new Convolution
Neural Network (CNN) architecture that learns deep spatial features,
complemented with LSTM units that learn complex temporal dynamics. Experiments
on two extensive datasets collected with different microphones on various
indoor and outdoor terrains demonstrate state-of-the-art performance compared
to existing techniques. We additionally evaluate the performance in adverse
acoustic conditions with high ambient noise and propose a noise-aware training
scheme that enables learning of more generalizable models that are essential
for robust real-world deployments.
Speaker-Invariant Training via Adversarial Learning
Authors: Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang, Juang
Date: 2018-04-02 00:00:00
Tasks: General Classification, Multi-Task Learning

We propose a novel adversarial multi-task learning scheme, aiming at actively
curtailing the inter-talker feature variability while maximizing its senone
discriminability so as to enhance the performance of a deep neural network
(DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In
SIT, a DNN acoustic model and a speaker classifier network are jointly
optimized to minimize the senone (tied triphone state) classification loss, and
simultaneously mini-maximize the speaker classification loss. A
speaker-invariant and senone-discriminative deep feature is learned through
this adversarial multi-task learning. With SIT, a canonical DNN acoustic model
with significantly reduced variance in its output probabilities is learned with
no explicit speaker-independent (SI) transformations or speaker-specific
representations used in training or testing. Evaluated on the CHiME-3 dataset,
the SIT achieves 4.99% relative word error rate (WER) improvement over the
conventional SI acoustic model. With additional unsupervised speaker
adaptation, the speaker-adapted (SA) SIT model achieves 4.86% relative WER gain
over the SA SI acoustic model.
Forecasting Future Humphrey Visual Fields Using Deep Learning
Authors: Joanne C. Wen, Cecilia S. Lee, Pearse A. Keane, Sa Xiao, Yue Wu, Ariel Rokem, Philip P. Chen, Aaron Y. Lee
Date: 2018-04-02 00:00:00
Tasks: Deep Learning, Transfer Learning

Purpose: To determine if deep learning networks could be trained to forecast
a future 24-2 Humphrey Visual Field (HVF).
  Participants: All patients who obtained a HVF 24-2 at the University of
Washington.
  Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were
extracted from a University of Washington database. Ten-fold cross validation
with a held out test set was used to develop the three main phases of model
development: model architecture selection, dataset combination selection, and
time-interval model training with transfer learning, to train a deep learning
artificial neural network capable of generating a point-wise visual field
prediction.
  Results: More than 1.7 million perimetry points were extracted to the
hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20
million trainable parameters, CascadeNet-5, was selected. The overall MAE for
the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained
models were able to successfully predict progressive field loss in glaucomatous
eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of
predicted and actual future HVF (p < 2.2 x 10 -16 ) and an average difference
of 0.41 dB.
  Conclusions: Using unfiltered real-world datasets, deep learning networks
show an impressive ability to not only learn spatio-temporal HVF changes but
also to generate predictions for future HVFs up to 5.5 years, given only a
single HVF.
Relief-Based Feature Selection: Introduction and Review
Authors: Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore
Date: 2017-11-22 00:00:00
Tasks: feature selection

Feature selection plays a critical role in biomedical data mining, driven by
increasing feature dimensionality in target problems and growing interest in
advanced but computationally expensive methodologies able to model complex
associations. Specifically, there is a need for feature selection methods that
are computationally efficient, yet sensitive to complex patterns of
association, e.g. interactions, so that informative features are not mistakenly
eliminated prior to downstream modeling. This paper focuses on Relief-based
algorithms (RBAs), a unique family of filter-style feature selection algorithms
that have gained appeal by striking an effective balance between these
objectives while flexibly adapting to various data characteristics, e.g.
classification vs. regression. First, this work broadly examines types of
feature selection and defines RBAs within that context. Next, we introduce the
original Relief algorithm and associated concepts, emphasizing the intuition
behind how it works, how feature weights generated by the algorithm can be
interpreted, and why it is sensitive to feature interactions without evaluating
combinations of features. Lastly, we include an expansive review of RBA
methodological research beyond Relief and its popular descendant, ReliefF. In
particular, we characterize branches of RBA research, and provide comparative
summaries of RBA algorithms including contributions, strategies, functionality,
time complexity, adaptation to key data characteristics, and software
availability.
Confidence from Invariance to Image Transformations
Authors: Yuval Bahat, Gregory Shakhnarovich
Date: 2018-04-02 00:00:00
Tasks: General Classification, Novelty Detection

We develop a technique for automatically detecting the classification errors
of a pre-trained visual classifier. Our method is agnostic to the form of the
classifier, requiring access only to classifier responses to a set of inputs.
We train a parametric binary classifier (error/correct) on a representation
derived from a set of classifier responses generated from multiple copies of
the same input, each subject to a different natural image transformation. Thus,
we establish a measure of confidence in classifier's decision by analyzing the
invariance of its decision under various transformations. In experiments with
multiple data sets (STL-10,CIFAR-100,ImageNet) and classifiers, we demonstrate
new state of the art for the error detection task. In addition, we apply our
technique to novelty detection scenarios, where we also demonstrate state of
the art results.
Simplified Energy Landscape for Modularity Using Total Variation
Authors: Zachary Boyd, Egil Bae, Xue-Cheng Tai, Andrea L. Bertozzi
Date: 2017-07-28 00:00:00
Tasks: Math

Networks capture pairwise interactions between entities and are frequently
used in applications such as social networks, food networks, and protein
interaction networks, to name a few. Communities, cohesive groups of nodes,
often form in these applications, and identifying them gives insight into the
overall organization of the network. One common quality function used to
identify community structure is modularity. In Hu et al. [SIAM J. App. Math.,
73(6), 2013], it was shown that modularity optimization is equivalent to
minimizing a particular nonconvex total variation (TV) based functional over a
discrete domain. They solve this problem, assuming the number of communities is
known, using a Merriman, Bence, Osher (MBO) scheme.
  We show that modularity optimization is equivalent to minimizing a convex
TV-based functional over a discrete domain, again, assuming the number of
communities is known. Furthermore, we show that modularity has no convex
relaxation satisfying certain natural conditions. We therefore, find a
manageable non-convex approximation using a Ginzburg Landau functional, which
provably converges to the correct energy in the limit of a certain parameter.
We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et
al. and which is 7 times faster at solving the associated diffusion equation
due to the fact that the underlying discretization is unconditionally stable.
Our numerical tests include a hyperspectral video whose associated graph has
2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper
of Hu et al.
Simple and Effective Semi-Supervised Question Answering
Authors: Bhuwan Dhingra, Danish Pruthi, Dheeraj Rajagopal
Date: 2018-04-02 00:00:00
Proceeding: NAACL 2018 6
Tasks: Extractive Question-Answering, Question Answering, TriviaQA

Recent success of deep learning models for the task of extractive Question
Answering (QA) is hinged on the availability of large annotated corpora.
However, large domain specific annotated corpora are limited and expensive to
construct. In this work, we envision a system where the end user specifies a
set of base documents and only a few labelled examples. Our system exploits the
document structure to create cloze-style questions from these base documents;
pre-trains a powerful neural network on the cloze style questions; and further
fine-tunes the model on the labeled examples. We evaluate our proposed system
across three diverse datasets from different domains, and find it to be highly
effective with very little labeled data. We attain more than 50% F1 score on
SQuAD and TriviaQA with less than a thousand labelled examples. We are also
releasing a set of 3.2M cloze-style questions for practitioners to use while
building QA systems.
An Event Detection Approach Based On Twitter Hashtags
Authors: Shih-Feng Yang, Julia Taylor Rayz
Date: 2018-04-02 00:00:00
Tasks: Clustering, Event Detection

Twitter is one of the most popular microblogging services in the world. The
great amount of information within Twitter makes it an important information
channel for people to learn and share news. Twitter hashtag is an popular
feature that can be viewed as human-labeled information which people use to
identify the topic of a tweet. Many researchers have proposed event-detection
approaches that can monitor Twitter data and determine whether special events,
such as accidents, extreme weather, earthquakes, or crimes take place. Although
many approaches use hashtags as one of their features, few of them explicitly
focus on the effectiveness of using hashtags on event detection. In this study,
we proposed an event detection approach that utilizes hashtags in tweets. We
adopted the feature extraction used in STREAMCUBE and applied a clustering
K-means approach to it. The experiments demonstrated that the K-means approach
performed better than STREAMCUBE in the clustering results. A discussion on
optimal K values for the K-means approach is also provided.
Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks
Authors: Anshul Ramachandran, Ashwin Balakrishna, Peter Kundzicz, Anirudh Neti
Date: 2018-04-02 00:00:00

Electric vehicles (EVs) have been gaining popularity due to their
environmental friendliness and efficiency. EV charging station networks are
scalable solutions for supporting increasing numbers of EVs within modern
electric grid constraints, yet few tools exist to aid the physical
configuration design of new networks. We use neural networks to predict
individual charging station usage statistics from the station's physical
location within a network. We have shown this quickly gives accurate estimates
of average usage statistics given a proposed configuration, without the need
for running many computationally expensive simulations. The trained neural
network can help EV charging network designers rapidly test various placements
of charging stations under additional individual constraints in order to find
an optimal configuration given their design objectives.
Scalable Deep Learning Logo Detection
Authors: Hang Su, Shaogang Gong, Xiatian Zhu
Date: 2018-03-30 00:00:00
Tasks: Deep Learning, Incremental Learning

Existing logo detection methods usually consider a small number of logo
classes and limited images per class with a strong assumption of requiring
tedious object bounding box annotations, therefore not scalable to real-world
dynamic applications. In this work, we tackle these challenges by exploring the
webly data learning principle without the need for exhaustive manual labelling.
Specifically, we propose a novel incremental learning approach, called Scalable
Logo Self-co-Learning (SL^2), capable of automatically self-discovering
informative training images from noisy web data for progressively improving
model capability in a cross-model co-learning manner. Moreover, we introduce a
very large (2,190,757 images of 194 logo classes) logo dataset "WebLogo-2M" by
an automatic web data collection and processing method. Extensive comparative
evaluations demonstrate the superiority of the proposed SL^2 method over the
state-of-the-art strongly and weakly supervised detection models and
contemporary webly data learning approaches.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Authors: Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
Date: 2018-01-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Classification, Image Segmentation, Object Detection, Person Re-Identification, Retinal OCT Disease Classification, Semantic Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
  The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameters