pwc-archive/papers-with-abstracts huggingface dataset:
Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN
Authors: Teik Koon Cheang, Yong Shean Chong, Yong Haur Tay
Date: 2017-01-23 00:00:00
Tasks: License Plate Recognition

While vehicle license plate recognition (VLPR) is usually done with a sliding
window approach, it can have limited performance on datasets with characters
that are of variable width. This can be solved by hand-crafting algorithms to
prescale the characters. While this approach can work fairly well, the
recognizer is only aware of the pixels within each detector window, and fails
to account for other contextual information that might be present in other
parts of the image. A sliding window approach also requires training data in
the form of presegmented characters, which can be more difficult to obtain. In
this paper, we propose a unified ConvNet-RNN model to recognize real-world
captured license plate photographs. By using a Convolutional Neural Network
(ConvNet) to perform feature extraction and using a Recurrent Neural Network
(RNN) for sequencing, we address the problem of sliding window approaches being
unable to access the context of the entire image by feeding the entire image as
input to the ConvNet. This has the added benefit of being able to perform
end-to-end training of the entire model on labelled, full license plate images.
Experimental results comparing the ConvNet-RNN architecture to a sliding
window-based approach shows that the ConvNet-RNN architecture performs
significantly better.
Comparative study on supervised learning methods for identifying phytoplankton species
Authors: Thi-Thu-Hong Phan, Emilie Poisson Caillault, André Bigand
Date: 2017-01-23 00:00:00
Tasks: PICO

Phytoplankton plays an important role in marine ecosystem. It is defined as a
biological factor to assess marine quality. The identification of phytoplankton
species has a high potential for monitoring environmental, climate changes and
for evaluating water quality. However, phytoplankton species identification is
not an easy task owing to their variability and ambiguity due to thousands of
micro and pico-plankton species. Therefore, the aim of this paper is to build a
framework for identifying phytoplankton species and to perform a comparison on
different features types and classifiers. We propose a new features type
extracted from raw signals of phytoplankton species. We then analyze the
performance of various classifiers on the proposed features type as well as two
other features types for finding the robust one. Through experiments, it is
found that Random Forest using the proposed features gives the best
classification results with average accuracy up to 98.24%.
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces
Authors: David Schultz, Brijnesh Jain
Date: 2017-01-23 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis, Time Series Averaging

Time series averaging in dynamic time warping (DTW) spaces has been
successfully applied to improve pattern recognition systems. This article
proposes and analyzes subgradient methods for the problem of finding a sample
mean in DTW spaces. The class of subgradient methods generalizes existing
sample mean algorithms such as DTW Barycenter Averaging (DBA). We show that DBA
is a majorize-minimize algorithm that converges to necessary conditions of
optimality after finitely many iterations. Empirical results show that for
increasing sample sizes the proposed stochastic subgradient (SSG) algorithm is
more stable and finds better solutions in shorter time than the DBA algorithm
on average. Therefore, SSG is useful in online settings and for non-small
sample sizes. The theoretical and empirical results open new paths for devising
sample mean algorithms: nonsmooth optimization methods and modified variants of
pairwise averaging methods.
Constraint programming for planning test campaigns of communications satellites
Authors: Emmanuel Hébrard, Marie-José Huguet, Daniel Veysseire, Ludivine Sauvan, Bertrand Cabon
Date: 2017-01-23 00:00:00

The payload of communications satellites must go through a series of tests to
assert their ability to survive in space. Each test involves some equipment of
the payload to be active, which has an impact on the temperature of the
payload. Sequencing these tests in a way that ensures the thermal stability of
the payload and minimizes the overall duration of the test campaign is a very
important objective for satellite manufacturers. The problem can be decomposed
in two sub-problems corresponding to two objectives: First, the number of
distinct configurations necessary to run the tests must be minimized. This can
be modeled as packing the tests into configurations, and we introduce a set of
implied constraints to improve the lower bound of the model. Second, tests must
be sequenced so that the number of times an equipment unit has to be switched
on or off is minimized. We model this aspect using the constraint Switch, where
a buffer with limited capacity represents the currently active equipment units,
and we introduce an improvement of the propagation algorithm for this
constraint. We then introduce a search strategy in which we sequentially solve
the sub-problems (packing and sequencing). Experiments conducted on real and
random instances show the respective interest of our contributions.
Learning to reinforcement learn
Authors: Jane. X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick
Date: 2016-11-17 00:00:00
Tasks: Deep Reinforcement Learning, Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years deep reinforcement learning (RL) systems have attained
superhuman performance in a number of challenging task domains. However, a
major limitation of such applications is their demand for massive amounts of
training data. A critical present objective is thus to develop deep RL methods
that can adapt rapidly to new tasks. In the present work we introduce a novel
approach to this challenge, which we refer to as deep meta-reinforcement
learning. Previous work has shown that recurrent networks can support
meta-learning in a fully supervised context. We extend this approach to the RL
setting. What emerges is a system that is trained using one RL algorithm, but
whose recurrent dynamics implement a second, quite separate RL procedure. This
second, learned RL algorithm can differ from the original one in arbitrary
ways. Importantly, because it is learned, it is configured to exploit structure
in the training domain. We unpack these points in a series of seven
proof-of-concept experiments, each of which examines a key aspect of deep
meta-RL. We consider prospects for extending and scaling up the approach, and
also point out some potentially important implications for neuroscience.
3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds
Authors: Samuel Kadoury, William Mandel, Marjolaine Roy-Beaudry, Marie-Lyne Nault, Stefan Parent
Date: 2017-01-17 00:00:00
Tasks: General Classification, Prognosis

We introduce a novel approach for predicting the progression of adolescent
idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
images. Recent progress in machine learning have allowed to improve
classification and prognosis rates, but lack a probabilistic framework to
measure uncertainty in the data. We propose a discriminative probabilistic
manifold embedding where locally linear mappings transform data points from
high-dimensional space to corresponding low-dimensional coordinates. A
discriminant adjacency matrix is constructed to maximize the separation between
progressive and non-progressive groups of patients diagnosed with scoliosis,
while minimizing the distance in latent variables belonging to the same class.
To predict the evolution of deformation, a baseline reconstruction is projected
onto the manifold, from which a spatiotemporal regression model is built from
parallel transport curves inferred from neighboring exemplars. Rate of
progression is modulated from the spine flexibility and curve magnitude of the
3D spine deformation. The method was tested on 745 reconstructions from 133
subjects using longitudinal 3D reconstructions of the spine, with results
demonstrating the discriminatory framework can identify between progressive and
non-progressive of scoliotic patients with a classification rate of 81% and
prediction differences of 2.1$^{o}$ in main curve angulation, outperforming
other manifold learning methods. Our method achieved a higher prediction
accuracy and improved the modeling of spatiotemporal morphological changes in
highly deformed spines compared to other learning methods.
Person Re-Identification via Recurrent Feature Aggregation
Authors: Yichao Yan, Bingbing Ni, Zhichao Song, Chao Ma, Yan Yan, Xiaokang Yang
Date: 2017-01-23 00:00:00
Tasks: Patch Matching, Person Re-Identification

We address the person re-identification problem by effectively exploiting a
globally discriminative feature representation from a sequence of tracked human
regions/patches. This is in contrast to previous person re-id works, which rely
on either single frame based person to person patch matching, or graph based
sequence to sequence matching. We show that a progressive/sequential fusion
framework based on long short term memory (LSTM) network aggregates the
frame-wise human region representation at each time stamp and yields a sequence
level human feature representation. Since LSTM nodes can remember and propagate
previously accumulated good features and forget newly input inferior ones, even
with simple hand-crafted features, the proposed recurrent feature aggregation
network (RFA-Net) is effective in generating highly discriminative sequence
level human representations. Extensive experimental results on two person
re-identification benchmarks demonstrate that the proposed method performs
favorably against state-of-the-art person re-identification methods.
Characterisation of speech diversity using self-organising maps
Authors: Tom A. F. Anderson, David M. W. Powers
Date: 2017-01-23 00:00:00
Tasks: Diversity, General Classification

We report investigations into speaker classification of larger quantities of
unlabelled speech data using small sets of manually phonemically annotated
speech. The Kohonen speech typewriter is a semi-supervised method comprised of
self-organising maps (SOMs) that achieves low phoneme error rates. A SOM is a
2D array of cells that learn vector representations of the data based on
neighbourhoods. In this paper, we report a method to evaluate pronunciation
using multilevel SOMs with /hVd/ single syllable utterances for the study of
vowels, for Australian pronunciation.
dna2vec: Consistent vector representations of variable-length k-mers
Authors: Patrick Ng
Date: 2017-01-23 00:00:00

One of the ubiquitous representation of long DNA sequence is dividing it into
shorter k-mer components. Unfortunately, the straightforward vector encoding of
k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse
yet, the distance between any pair of one-hot vectors is equidistant. This is
particularly problematic when applying the latest machine learning algorithms
to solve problems in biological sequence analysis. In this paper, we propose a
novel method to train distributed representations of variable-length k-mers.
Our method is based on the popular word embedding model word2vec, which is
trained on a shallow two-layer neural network. Our experiments provide evidence
that the summing of dna2vec vectors is akin to nucleotides concatenation. We
also demonstrate that there is correlation between Needleman-Wunsch similarity
score and cosine similarity of dna2vec vectors.
A Multichannel Convolutional Neural Network For Cross-language Dialog State Tracking
Authors: Hongjie Shi, Takashi Ushio, Mitsuru Endo, Katsuyoshi Yamagami, Noriaki Horii
Date: 2017-01-23 00:00:00
Tasks: dialog state tracking, Translation

The fifth Dialog State Tracking Challenge (DSTC5) introduces a new
cross-language dialog state tracking scenario, where the participants are asked
to build their trackers based on the English training corpus, while evaluating
them with the unlabeled Chinese corpus. Although the computer-generated
translations for both English and Chinese corpus are provided in the dataset,
these translations contain errors and careless use of them can easily hurt the
performance of the built trackers. To address this problem, we propose a
multichannel Convolutional Neural Networks (CNN) architecture, in which we
treat English and Chinese language as different input channels of one single
CNN model. In the evaluation of DSTC5, we found that such multichannel
architecture can effectively improve the robustness against translation errors.
Additionally, our method for DSTC5 is purely machine learning based and
requires no prior knowledge about the target language. We consider this a
desirable property for building a tracker in the cross-language context, as not
every developer will be familiar with both languages.
Learning to Recognize Objects by Retaining other Factors of Variation
Authors: Jiaping Zhao, Chin-kai Chang, Laurent Itti
Date: 2016-07-20 00:00:00
Tasks: Object, Object Recognition

Natural images are generated under many factors, including shape, pose,
illumination etc. Most existing ConvNets formulate object recognition from
natural images as a single task classification problem, and attempt to learn
features useful for object categories, but invariant to other factors of
variation as much as possible. These architectures do not explicitly learn
other factors, like pose and lighting, instead, they usually discard them by
pooling and normalization. In this work, we take the opposite approach: we
train ConvNets for object recognition by retaining other factors (pose in our
case) and learn them jointly with object category. We design a new multi-task
leaning (MTL) ConvNet, named disentangling CNN (disCNN), which explicitly
enforces the disentangled representations of object identity and pose, and is
trained to predict object categories and pose transformations. We show that
disCNN achieves significantly better object recognition accuracies than AlexNet
trained solely to predict object categories on the iLab-20M dataset, which is a
large scale turntable dataset with detailed object pose and lighting
information. We further show that the pretrained disCNN/AlexNet features on
iLab- 20M generalize to object recognition on both Washington RGB-D and
ImageNet datasets, and the pretrained disCNN features are significantly better
than the pretrained AlexNet features for fine-tuning object recognition on the
ImageNet dataset.
Improved Deep Learning of Object Category using Pose Information
Authors: Jiaping Zhao, Laurent Itti
Date: 2016-07-20 00:00:00
Tasks: Deep Learning, Object, Object Recognition

Despite significant recent progress, the best available computer vision
algorithms still lag far behind human capabilities, even for recognizing
individual discrete objects under various poses, illuminations, and
backgrounds. Here we present a new approach to using object pose information to
improve deep network learning. While existing large-scale datasets, e.g.
ImageNet, do not have pose information, we leverage the newly published
turntable dataset, iLab-20M, which has ~22M images of 704 object instances shot
under different lightings, camera viewpoints and turntable rotations, to do
more controlled object recognition experiments. We introduce a new
convolutional neural network architecture, what/where CNN (2W-CNN), built on a
linear-chain feedforward CNN (e.g., AlexNet), augmented by hierarchical layers
regularized by object poses. Pose information is only used as feedback signal
during training, in addition to category information; during test, the
feedforward network only predicts category. To validate the approach, we train
both 2W-CNN and AlexNet using a fraction of the dataset, and 2W-CNN achieves 6%
performance improvement in category prediction. We show mathematically that
2W-CNN has inherent advantages over AlexNet under the stochastic gradient
descent (SGD) optimization procedure. Further more, we fine-tune object
recognition on ImageNet by using the pretrained 2W-CNN and AlexNet features on
iLab-20M, results show that significant improvements have been achieved,
compared with training AlexNet from scratch. Moreover, fine-tuning 2W-CNN
features performs even better than fine-tuning the pretrained AlexNet features.
These results show pretrained features on iLab- 20M generalizes well to natural
image datasets, and 2WCNN learns even better features for object recognition
than AlexNet.
What the Language You Tweet Says About Your Occupation
Authors: Tianran Hu, Haoyuan Xiao, Thuy-vy Thi Nguyen, Jiebo Luo
Date: 2017-01-22 00:00:00
Tasks: Clustering, Job Prediction, Marketing

Many aspects of people's lives are proven to be deeply connected to their
jobs. In this paper, we first investigate the distinct characteristics of major
occupation categories based on tweets. From multiple social media platforms, we
gather several types of user information. From users' LinkedIn webpages, we
learn their proficiencies. To overcome the ambiguity of self-reported
information, a soft clustering approach is applied to extract occupations from
crowd-sourced data. Eight job categories are extracted, including Marketing,
Administrator, Start-up, Editor, Software Engineer, Public Relation, Office
Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for
understanding their linguistic styles, interests, and personalities. Our
results suggest that people of different jobs have unique tendencies in certain
language styles and interests. Our results also clearly reveal distinctive
levels in terms of Big Five Traits for different jobs. Finally, a classifier is
built to predict job types based on the features extracted from tweets. A high
accuracy indicates a strong discrimination power of language features for job
prediction task.
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets
Authors: Omar Montasser, Daniel Kifer
Date: 2017-01-22 00:00:00
Tasks: Survey, Vocal Bursts Intensity Prediction

In this paper, we consider the problem of predicting demographics of
geographic units given geotagged Tweets that are composed within these units.
Traditional survey methods that offer demographics estimates are usually
limited in terms of geographic resolution, geographic boundaries, and time
intervals. Thus, it would be highly useful to develop computational methods
that can complement traditional survey methods by offering demographics
estimates at finer geographic resolutions, with flexible geographic boundaries
(i.e. not confined to administrative boundaries), and at different time
intervals. While prior work has focused on predicting demographics and health
statistics at relatively coarse geographic resolutions such as the county-level
or state-level, we introduce an approach to predict demographics at finer
geographic resolutions such as the blockgroup-level. For the task of predicting
gender and race/ethnicity counts at the blockgroup-level, an approach adapted
from prior work to our problem achieves an average correlation of 0.389
(gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms
this prior approach with an average correlation of 0.671 (gender) and 0.692
(race).
Jet-Images -- Deep Learning Edition
Authors: Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, Ariel Schwartzman
Date: 2015-11-16 00:00:00
Tasks: Deep Learning, Jet Tagging

Building on the notion of a particle physics detector as a camera and the
collimated streams of high energy particles, or jets, it measures as an image,
we investigate the potential of machine learning techniques based on deep
learning architectures to identify highly boosted W bosons. Modern deep
learning algorithms trained on jet images can out-perform standard
physically-motivated feature driven approaches to jet tagging. We develop
techniques for visualizing how these features are learned by the network and
what additional information is used to improve performance. This interplay
between physically-motivated feature driven tools and supervised learning
algorithms is general and can be used to significantly increase the sensitivity
to discover new particles and new forces, and gain a deeper understanding of
the physics within jets.
A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction
Authors: Yoonsik Kim, Insung Hwang, Nam Ik Cho
Date: 2017-01-22 00:00:00
Tasks: image-classification, Image Classification, Image Restoration, Semantic Segmentation

The inception network has been shown to provide good performance on image
classification problems, but there are not much evidences that it is also
effective for the image restoration or pixel-wise labeling problems. For image
restoration problems, the pooling is generally not used because the decimated
features are not helpful for the reconstruction of an image as the output.
Moreover, most deep learning architectures for the restoration problems do not
use dense prediction that need lots of training parameters. From these
observations, for enjoying the performance of inception-like structure on the
image based problems we propose a new convolutional network-in-network
structure. The proposed network can be considered a modification of inception
structure where pool projection and pooling layer are removed for maintaining
the entire feature map size, and a larger kernel filter is added instead.
Proposed network greatly reduces the number of parameters on account of removed
dense prediction and pooling, which is an advantage, but may also reduce the
receptive field in each layer. Hence, we add a larger kernel than the original
inception structure for not increasing the depth of layers. The proposed
structure is applied to typical image-to-image learning problems, i.e., the
problems where the size of input and output are same such as skin detection,
semantic segmentation, and compression artifacts reduction. Extensive
experiments show that the proposed network brings comparable or better results
than the state-of-the-art convolutional neural networks for these problems.