pwc-archive/papers-with-abstracts huggingface dataset:
Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study
Authors: Pedram Ghamisi, Gabriele Cavallaro, Dan, Wu, Jon Atli Benediktsson, Antonio Plaza
Date: 2017-07-09 00:00:00
Tasks: Attribute, CPU, General Classification, Land Cover Classification

In this paper, an approach is proposed to fuse LiDAR and hyperspectral data,
which considers both spectral and spatial information in a single framework.
Here, an extended self-dual attribute profile (ESDAP) is investigated to
extract spatial information from a hyperspectral data set. To extract spectral
information, a few well-known classifiers have been used such as support vector
machines (SVMs), random forests (RFs), and artificial neural networks (ANNs).
The proposed method accurately classify the relatively volumetric data set in a
few CPU processing time in a real ill-posed situation where there is no balance
between the number of training samples and the number of features. The
classification part of the proposed approach is fully-automatic.
Controlling Linguistic Style Aspects in Neural Language Generation
Authors: Jessica Ficler, Yoav Goldberg
Date: 2017-07-09 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Text Generation

Most work on neural natural language generation (NNLG) focus on controlling
the content of the generated text. We experiment with controlling several
stylistic aspects of the generated text, in addition to its content. The method
is based on conditioned RNN language model, where the desired content as well
as the stylistic parameters serve as conditioning contexts. We demonstrate the
approach on the movie reviews domain and show that it is successful in
generating coherent sentences corresponding to the required linguistic style
and content.
Deepest Neural Networks
Authors: Raul Rojas
Date: 2017-07-09 00:00:00
Tasks: General Classification

This paper shows that a long chain of perceptrons (that is, a multilayer
perceptron, or MLP, with many hidden layers of width one) can be a universal
classifier. The classification procedure is not necessarily computationally
efficient, but the technique throws some light on the kind of computations
possible with narrow and deep MLPs.
A Contemporary Overview of Probabilistic Latent Variable Models
Authors: Rick Farouni
Date: 2017-06-25 00:00:00

In this paper we provide a conceptual overview of latent variable models
within a probabilistic modeling framework, an overview that emphasizes the
compositional nature and the interconnectedness of the seemingly disparate
models commonly encountered in statistical practice.
Deep Learning for Real Time Crime Forecasting
Authors: Bao Wang, Duo Zhang, Duanhao Zhang, P. Jeffery Brantingham, Andrea L. Bertozzi
Date: 2017-07-09 00:00:00
Tasks: Crime Prediction, Deep Learning

Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatically different patterns. These distributions are
of very low regularity in both space and time. In this work, we adapt the
state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et
al, AAAI, 2017], to collectively predict crime distribution over the Los
Angeles area. Our models are two staged. First, we preprocess the raw crime
data. This includes regularization in both space and time to enhance
predictable signals. Second, we adapt hierarchical structures of residual
convolutional units to train multi-factor crime prediction models. Experiments
over a half year period in Los Angeles reveal highly accurate predictive power
of our models.
Detection of bimanual gestures everywhere: why it matters, what we need and what is missing
Authors: Divya Shah, Ernesto Denicia, Tiago Pimentel, Barbara Bruno, Fulvio Mastrogiovanni
Date: 2017-07-09 00:00:00
Tasks: Classification, General Classification

Bimanual gestures are of the utmost importance for the study of motor
coordination in humans and in everyday activities. A reliable detection of
bimanual gestures in unconstrained environments is fundamental for their
clinical study and to assess common activities of daily living. This paper
investigates techniques for a reliable, unconstrained detection and
classification of bimanual gestures. It assumes the availability of inertial
data originating from the two hands/arms, builds upon a previously developed
technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and
Gaussian Mixture Regression (GMR), and compares different modelling and
classification techniques, which are based on a number of assumptions inspired
by literature about how bimanual gestures are represented and modelled in the
brain. Experiments show results related to 5 everyday bimanual activities,
which have been selected on the basis of three main parameters: (not)
constraining the two hands by a physical tool, (not) requiring a specific
sequence of single-hand gestures, being recursive (or not). In the best
performing combination of modeling approach and classification technique, five
out of five activities are recognized up to an accuracy of 97%, a precision of
82% and a level of recall of 100%.
Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising
Authors: M. Baburaj, Sudhish N. George
Date: 2016-11-18 00:00:00
Tasks: Denoising, Tensor Decomposition, Video Denoising

The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes
low rank multi-linear signal corrupted by gross errors into low multi-rank and
sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm.
But if the multi-rank of the signal is considerably large and/or large amount
of noise is present, the performance of TRPCA deteriorates. To overcome this
problem, this paper proposes a new efficient iterative reweighted tensor
decomposition scheme based on t-SVD which significantly improves tensor
multi-rank in TRPCA. Further, the sparse component of the tensor is also
recovered by reweighted l 1 norm which enhances the accuracy of decomposition.
The effectiveness of the proposed method is established by applying it to the
video denoising problem and the experimental results reveal that the proposed
algorithm outperforms its counterparts.
Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery
Authors: Baburaj M., Sudhish N. George
Date: 2016-11-18 00:00:00

This paper focus on recovering multi-dimensional data called tensor from
randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm
minimization for sparsity enhancement, this paper proposes a reweighted
singular value enhancement scheme to improve tensor low tubular rank in the
tensor completion process. An efficient iterative decomposition scheme based on
t-SVD is proposed which improves low-rank signal recovery significantly. The
effectiveness of the proposed method is established by applying to video
completion problem, and experimental results reveal that the algorithm
outperforms its counterparts.
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
Authors: Daniel Seita, Xinlei Pan, Haoyu Chen, John Canny
Date: 2016-10-19 00:00:00

We present a novel Metropolis-Hastings method for large datasets that uses
small expected-size minibatches of data. Previous work on reducing the cost of
Metropolis-Hastings tests yield variable data consumed per sample, with only
constant factor reductions versus using the full dataset for each sample. Here
we present a method that can be tuned to provide arbitrarily small batch sizes,
by adjusting either proposal step size or temperature. Our test uses the
noise-tolerant Barker acceptance test with a novel additive correction
variable. The resulting test has similar cost to a normal SGD update. Our
experiments demonstrate several order-of-magnitude speedups over previous work.
Unsupervised Creation of Parameterized Avatars
Authors: Lior Wolf, Yaniv Taigman, Adam Polyak
Date: 2017-04-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

We study the problem of mapping an input image to a tied pair consisting of a
vector of parameters and an image that is created using a graphical engine from
the vector of parameters. The mapping's objective is to have the output image
as similar as possible to the input image. During training, no supervision is
given in the form of matching inputs and outputs.
  This learning problem extends two literature problems: unsupervised domain
adaptation and cross domain transfer. We define a generalization bound that is
based on discrepancy, and employ a GAN to implement a network solution that
corresponds to this bound. Experimentally, our method is shown to solve the
problem of automatically creating avatars.
Distributional Adversarial Networks
Authors: Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra
Date: 2017-06-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: Domain Adaptation

We propose a framework for adversarial training that relies on a sample
rather than a single sample point as the fundamental unit of discrimination.
Inspired by discrepancy measures and two-sample tests between probability
distributions, we propose two such distributional adversaries that operate and
predict on samples, and show how they can be easily implemented on top of
existing models. Various experimental results show that generators trained with
our distributional adversaries are much more stable and are remarkably less
prone to mode collapse than traditional models trained with pointwise
prediction discriminators. The application of our framework to domain
adaptation also results in considerable improvement over recent
state-of-the-art.
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Authors: Dingwen Zhang, Huazhu Fu, Junwei Han, Ali Borji, Xuelong. Li
Date: 2016-04-24 00:00:00
Tasks: Co-Salient Object Detection, Saliency Detection

Co-saliency detection is a newly emerging and rapidly growing research area
in computer vision community. As a novel branch of visual saliency, co-saliency
detection refers to the discovery of common and salient foregrounds from two or
more relevant images, and can be widely used in many computer vision tasks. The
existing co-saliency detection algorithms mainly consist of three components:
extracting effective features to represent the image regions, exploring the
informative cues or factors to characterize co-saliency, and designing
effective computational frameworks to formulate co-saliency. Although numerous
methods have been developed, the literature is still lacking a deep review and
evaluation of co-saliency detection techniques. In this paper, we aim at
providing a comprehensive review of the fundamentals, challenges, and
applications of co-saliency detection. Specifically, we provide an overview of
some related computer vision works, review the history of co-saliency
detection, summarize and categorize the major algorithms in this research area,
discuss some open issues in this area, present the potential applications of
co-saliency detection, and finally point out some unsolved challenges and
promising future works. We expect this review to be beneficial to both fresh
and senior researchers in this field, and give insights to researchers in other
related areas regarding the utility of co-saliency detection algorithms.
Class-Weighted Convolutional Features for Visual Instance Search
Authors: Albert Jimenez, Jose M. Alvarez, Xavier Giro-i-Nieto
Date: 2017-07-09 00:00:00
Tasks: image-classification, Image Retrieval, Instance Search, Re-Ranking, Retrieval

Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been proven effective feature extractors for image retrieval. The
most successful approaches are based on encoding the activations of
convolutional layers, as they convey the image spatial information. In this
paper, we go beyond this spatial information and propose a local-aware encoding
of convolutional features based on semantic information predicted in the target
image. To this end, we obtain the most discriminative regions of an image using
Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the
network and therefore, our approach, has the additional advantage of not
requiring external information. In addition, we use CAMs to generate object
proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval,
Oxford5k and Paris6k, demonstrate the competitiveness of our approach
outperforming the current state-of-the-art when using off-the-shelf models
trained on ImageNet. The source code and model used in this paper are publicly
available at http://imatge-upc.github.io/retrieval-2017-cam/.
Neural Machine Translation between Herbal Prescriptions and Diseases
Authors: Sun-Chong Wang
Date: 2017-07-09 00:00:00
Tasks: Anatomy, Machine Translation, Translation

The current study applies deep learning to herbalism. Toward the goal, we
acquired the de-identified health insurance reimbursements that were claimed in
a 10-year period from 2004 to 2013 in the National Health Insurance Database of
Taiwan, the total number of reimbursement records equaling 340 millions. Two
artificial intelligence techniques were applied to the dataset: residual
convolutional neural network multitask classifier and attention-based recurrent
neural network. The former works to translate from herbal prescriptions to
diseases; and the latter from diseases to herbal prescriptions. Analysis of the
classification results indicates that herbal prescriptions are specific to:
anatomy, pathophysiology, sex and age of the patient, and season and year of
the prescription. Further analysis identifies temperature and gross domestic
product as the meteorological and socioeconomic factors that are associated
with herbal prescriptions. Analysis of the neural machine transitional result
indicates that the recurrent neural network learnt not only syntax but also
semantics of diseases and herbal prescriptions.
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Authors: Konstantin Chekanov, Polina Mamoshina, Roman V. Yampolskiy, Radu Timofte, Morten Scheibye-Knudsen, Alex Zhavoronkov
Date: 2017-07-09 00:00:00
Tasks: Diversity, Management

Diversity is one of the fundamental properties for the survival of species,
populations, and organizations. Recent advances in deep learning allow for the
rapid and automatic assessment of organizational diversity and possible
discrimination by race, sex, age and other parameters. Automating the process
of assessing the organizational diversity using the deep neural networks and
eliminating the human factor may provide a set of real-time unbiased reports to
all stakeholders. In this pilot study we applied the deep-learned predictors of
race and sex to the executive management and board member profiles of the 500
largest companies from the 2016 Forbes Global 2000 list and compared the
predicted ratios to the ratios within each company's country of origin and
ranked them by the sex-, age- and race- diversity index (DI). While the study
has many limitations and no claims are being made concerning the individual
companies, it demonstrates a method for the rapid and impartial assessment of
organizational diversity using deep neural networks.
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach
Authors: Zhenghao Chen, Jianlong Zhou, Xiuying Wang
Date: 2017-07-09 00:00:00
Tasks: Clustering, Management, Time Series Analysis

Time-Spatial data plays a crucial role for different fields such as traffic
management. These data can be collected via devices such as surveillance
sensors or tracking systems. However, how to efficiently an- alyze and
visualize these data to capture essential embedded pattern information is
becoming a big challenge today. Classic visualization ap- proaches focus on
revealing 2D and 3D spatial information and modeling statistical test. Those
methods would easily fail when data become mas- sive. Recent attempts concern
on how to simply cluster data and perform prediction with time-oriented
information. However, those approaches could still be further enhanced as they
also have limitations for han- dling massive clusters and labels. In this
paper, we propose a visualiza- tion methodology for mobility data using
artificial neural net techniques. This method aggregates three main parts that
are Back-end Data Model, Neural Net Algorithm including clustering method
Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN)
for ex- tracting the features and lastly a solid front-end that displays the
results to users with an interactive system. SOM is able to cluster the
visiting patterns and detect the abnormal pattern. RNN can perform the predic-
tion for time series analysis using its dynamic architecture. Furthermore, an
interactive system will enable user to interpret the result with graph- ics,
animation and 3D model for a close-loop feedback. This method can be
particularly applied in two tasks that Commercial-based Promotion and abnormal
traffic patterns detection.
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Authors: Xun Liu, Wei Xue, Lei Xiao, Bo Zhang
Date: 2017-07-04 00:00:00
Tasks: Click-Through Rate Prediction

We describe a parallel bayesian online deep learning framework (PBODL) for
click-through rate (CTR) prediction within today's Tencent advertising system,
which provides quick and accurate learning of user preferences. We first
explain the framework with a deep probit regression model, which is trained
with probabilistic back-propagation in the mode of assumed Gaussian density
filtering. Then we extend the model family to a variety of bayesian online
models with increasing feature embedding capabilities, such as Sparse-MLP,
FM-MLP and FFM-MLP. Finally, we implement a parallel training system based on a
stream computing infrastructure and parameter servers. Experiments with public
available datasets and Tencent industrial datasets show that models within our
framework perform better than several common online models, such as
AdPredictor, FTRL-Proximal and MatchBox. Online A/B test within Tencent
advertising system further proves that our framework could achieve CTR and CPM
lift by learning more quickly and accurately.
Exploiting Active Subspaces in Global Optimization: How Complex is your Problem?
Authors: Pramudita Satria Palar, Koji Shimoyama
Date: 2017-07-09 00:00:00
Tasks: global-optimization

When applying optimization method to a real-world problem, the possession of
prior knowledge and preliminary analysis on the landscape of a global
optimization problem can give us an insight into the complexity of the problem.
This knowledge can better inform us in deciding what optimization method should
be used to tackle the problem. However, this analysis becomes problematic when
the dimensionality of the problem is high. This paper presents a framework to
take a deeper look at the global optimization problem to be tackled: by
analyzing the low-dimensional representation of the problem through discovering
the active subspaces of the given problem. The virtue of this is that the
problem's complexity can be visualized in a one or two-dimensional plot, thus
allow one to get a better grip about the problem's difficulty. One could then
have a better idea regarding the complexity of their problem to determine the
choice of global optimizer or what surrogate-model type to be used.
Furthermore, we also demonstrate how the active subspaces can be used to
perform design exploration and analysis.
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Authors: Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie
Date: 2017-04-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Domain Adaptation

Computer vision systems are designed to work well within the context of
everyday photography. However, artists often render the world around them in
ways that do not resemble photographs. Artwork produced by people is not
constrained to mimic the physical world, making it more challenging for
machines to recognize.
  This work is a step toward teaching machines how to categorize images in ways
that are valuable to humans. First, we collect a large-scale dataset of
contemporary artwork from Behance, a website containing millions of portfolios
from professional and commercial artists. We annotate Behance imagery with rich
attribute labels for content, emotions, and artistic media. Furthermore, we
carry out baseline experiments to show the value of this dataset for artistic
style prediction, for improving the generality of existing object classifiers,
and for the study of visual domain adaptation. We believe our Behance Artistic
Media dataset will be a good starting point for researchers wishing to study
artistic imagery and relevant problems.
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
Authors: Liting Sun, Cheng Peng, Wei Zhan, Masayoshi Tomizuka
Date: 2017-07-09 00:00:00
Tasks: Autonomous Driving, Imitation Learning, Model Predictive Control

For safe and efficient planning and control in autonomous driving, we need a
driving policy which can achieve desirable driving quality in long-term horizon
with guaranteed safety and feasibility. Optimization-based approaches, such as
Model Predictive Control (MPC), can provide such optimal policies, but their
computational complexity is generally unacceptable for real-time
implementation. To address this problem, we propose a fast integrated planning
and control framework that combines learning- and optimization-based approaches
in a two-layer hierarchical structure. The first layer, defined as the "policy
layer", is established by a neural network which learns the long-term optimal
driving policy generated by MPC. The second layer, called the "execution
layer", is a short-term optimization-based controller that tracks the reference
trajecotries given by the "policy layer" with guaranteed short-term safety and
feasibility. Moreover, with efficient and highly-representative features, a
small-size neural network is sufficient in the "policy layer" to handle many
complicated driving scenarios. This renders online imitation learning with
Dataset Aggregation (DAgger) so that the performance of the "policy layer" can
be improved rapidly and continuously online. Several exampled driving scenarios
are demonstrated to verify the effectiveness and efficiency of the proposed
framework.
Variational Inference via Transformations on Distributions
Authors: Siddhartha Saxena, Shibhansh Dohare, Jaivardhan Kapoor
Date: 2017-07-09 00:00:00
Tasks: Model Optimization, Variational Inference

Variational inference methods often focus on the problem of efficient model
optimization, with little emphasis on the choice of the approximating
posterior. In this paper, we review and implement the various methods that
enable us to develop a rich family of approximating posteriors. We show that
one particular method employing transformations on distributions results in
developing very rich and complex posterior approximation. We analyze its
performance on the MNIST dataset by implementing with a Variational Autoencoder
and demonstrate its effectiveness in learning better posterior distributions.