pwc-archive/papers-with-abstracts huggingface dataset:
Background Subtraction using Compressed Low-resolution Images
Authors: Min Chen, Andy Song, Shivanthan A. C. Yhanandan, Jing Zhang
Date: 2018-10-24 00:00:00

Image processing and recognition are an important part of the modern society,
with applications in fields such as advanced artificial intelligence, smart
assistants, and security surveillance. The essential first step involved in
almost all the visual tasks is background subtraction with a static camera.
Ensuring that this critical step is performed in the most efficient manner
would therefore improve all aspects related to objects recognition and
tracking, behavior comprehension, etc.. Although background subtraction method
has been applied for many years, its application suffers from real-time
requirement. In this letter, we present a novel approach in implementing the
background subtraction. The proposed method uses compressed, low-resolution
grayscale image for the background subtraction. These low-resolution grayscale
images were found to preserve the salient information very well. To verify the
feasibility of our methodology, two prevalent methods, ViBe and GMM, are used
in the experiment. The results of the proposed methodology confirm the
effectiveness of our approach.
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Authors: Hui Sun, Cheng Li, Boqiang Liu, Hairong Zheng, David Dagan Feng, Shan-Shan Wang
Date: 2018-10-24 00:00:00
Tasks: Breast Mass Segmentation In Whole Mammograms, Decoder, Segmentation

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.
A Deep-Learning-Based Fashion Attributes Detection Model
Authors: Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan
Date: 2018-10-24 00:00:00
Tasks: Deep Learning, Marketing

Analyzing fashion attributes is essential in the fashion design process.
Current fashion forecasting firms, such as WGSN utilizes information from all
around the world (from fashion shows, visual merchandising, blogs, etc). They
gather information by experience, by observation, by media scan, by interviews,
and by exposed to new things. Such information analyzing process is called
abstracting, which recognize similarities or differences across all the
garments and collections. In fact, such abstraction ability is useful in many
fashion careers with different purposes. Fashion forecasters abstract across
design collections and across time to identify fashion change and directions;
designers, product developers and buyers abstract across a group of garments
and collections to develop a cohesive and visually appeal lines; sales and
marketing executives abstract across product line each season to recognize
selling points; fashion journalist and bloggers abstract across runway photos
to recognize symbolic core concepts that can be translated into editorial
features. Fashion attributes analysis for such fashion insiders requires much
detailed and in-depth attributes annotation than that for consumers, and
requires inference on multiple domains. In this project, we propose a
data-driven approach for recognizing fashion attributes. Specifically, a
modified version of Faster R-CNN model is trained on images from a large-scale
localization dataset with 594 fine-grained attributes under different
scenarios, for example in online stores and street snapshots. This model will
then be used to detect garment items and classify clothing attributes for
runway photos and fashion illustrations.
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Authors: Xu Han, Hao Zhu, Pengfei Yu, ZiYun Wang, Yuan YAO, Zhiyuan Liu, Maosong Sun
Date: 2018-10-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, Few-Shot Learning, Few-Shot Relation Classification, General Classification, Relation, Relation Classification, Relation Extraction, Sentence

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.
Local Homology of Word Embeddings
Authors: Tadas Temčinas
Date: 2018-10-24 00:00:00
Tasks: Topological Data Analysis, Word Embeddings, Word Sense Disambiguation

Topological data analysis (TDA) has been widely used to make progress on a
number of problems. However, it seems that TDA application in natural language
processing (NLP) is at its infancy. In this paper we try to bridge the gap by
arguing why TDA tools are a natural choice when it comes to analysing word
embedding data. We describe a parallelisable unsupervised learning algorithm
based on local homology of datapoints and show some experimental results on
word embedding data. We see that local homology of datapoints in word embedding
data contains some information that can potentially be used to solve the word
sense disambiguation problem.
A Binary Optimization Approach for Constrained K-Means Clustering
Authors: Huu Le, Anders Eriksson, Thanh-Toan Do, Michael Milford
Date: 2018-10-24 00:00:00
Tasks: Clustering

K-Means clustering still plays an important role in many computer vision
problems. While the conventional Lloyd method, which alternates between
centroid update and cluster assignment, is primarily used in practice, it may
converge to a solution with empty clusters. Furthermore, some applications may
require the clusters to satisfy a specific set of constraints, e.g., cluster
sizes, must-link/cannot-link. Several methods have been introduced to solve
constrained K-Means clustering. Due to the non-convex nature of K-Means,
however, existing approaches may result in sub-optimal solutions that poorly
approximate the true clusters. In this work, we provide a new perspective to
tackle this problem. Particularly, we reconsider constrained K-Means as a
Binary Optimization Problem and propose a novel optimization scheme to search
for feasible solutions in the binary domain. This approach allows us to solve
constrained K-Means where multiple types of constraints can be simultaneously
enforced. Experimental results on synthetic and real datasets show that our
method provides better clustering accuracy with faster runtime compared to
several commonly used techniques.
Optimal design of experiments to identify latent behavioral types
Authors: Stefano Balietti, Brennan Klein, Christoph Riedl
Date: 2018-07-10 00:00:00
Tasks: Decision Making, Experimental Design, Model Selection, Reinforcement Learning

Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a "wisdom of experts" prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.
Smoothed Online Optimization for Regression and Control
Authors: Gautam Goel, Adam Wierman
Date: 2018-10-23 00:00:00
Tasks: regression

We consider Online Convex Optimization (OCO) in the setting where the costs
are $m$-strongly convex and the online learner pays a switching cost for
changing decisions between rounds. We show that the recently proposed Online
Balanced Descent (OBD) algorithm is constant competitive in this setting, with
competitive ratio $3 + O(1/m)$, irrespective of the ambient dimension.
Additionally, we show that when the sequence of cost functions is
$\epsilon$-smooth, OBD has near-optimal dynamic regret and maintains strong
per-round accuracy. We demonstrate the generality of our approach by showing
that the OBD framework can be used to construct competitive algorithms for a
variety of online problems across learning and control, including online
variants of ridge regression, logistic regression, maximum likelihood
estimation, and LQR control.
Area Attention
Authors: Yang Li, Lukasz Kaiser, Samy Bengio, Si Si
Date: 2018-10-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Captioning, Machine Translation, Translation

Existing attention mechanisms are trained to attend to individual items in a collection (the memory) with a predefined, fixed granularity, e.g., a word token or an image grid. We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e.g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences. Importantly, the shape and the size of an area are dynamically determined via learning, which enables a model to attend to information with varying granularity. Area attention can easily work with existing model architectures such as multi-head attention for simultaneously attending to multiple areas in the memory. We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases. These improvements are obtainable with a basic form of area attention that is parameter free.
PoPPy: A Point Process Toolbox Based on PyTorch
Authors: Hongteng Xu
Date: 2018-10-23 00:00:00
Tasks: Point Processes

PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and efficient learning of point process models. It can be used for interpretable sequential data modeling and analysis, e.g., Granger causality analysis of multi-variate point processes, point process-based simulation and prediction of event sequences. In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data? 2) How to learn the proposed intensity functions from observed data? The goal of PoPPy is providing a user-friendly solution to the key points above and achieving large-scale point process-based sequential data analysis, simulation and prediction.
Interpreting Black Box Predictions using Fisher Kernels
Authors: Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo
Date: 2018-10-23 00:00:00
Tasks: Data Summarization

Research in both machine learning and psychology suggests that salient
examples can help humans to interpret learning models. To this end, we take a
novel look at black box interpretation of test predictions in terms of training
examples. Our goal is to ask `which training examples are most responsible for
a given set of predictions'? To answer this question, we make use of Fisher
kernels as the defining feature embedding of each data point, combined with
Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. In
contrast to prior work, our method is able to seamlessly handle any sized
subset of test predictions in a principled way. We theoretically analyze our
approach, providing novel convergence bounds for SBQ over discrete candidate
atoms. Our approach recovers the application of influence functions for
interpretability as a special case yielding novel insights from this
connection. We also present applications of the proposed approach to three use
cases: cleaning training data, fixing mislabeled examples and data
summarization.
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
Authors: Gerard Snaauw, Dong Gong, Gabriel Maicas, Anton Van Den Hengel, Wiro J. Niessen, Johan Verjans, Gustavo Carneiro
Date: 2018-10-23 00:00:00
Tasks: General Classification, Management, Multi-Task Learning, Segmentation

Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and
management of cardiovascular disease. Deep learning methods have proven to
deliver segmentation results comparable to human experts in CMR imaging, but
there have been no convincing results for the problem of end-to-end
segmentation and diagnosis from CMR. This is in part due to a lack of
sufficiently large datasets required to train robust diagnosis models. In this
paper, we propose a learning method to train diagnosis models, where our
approach is designed to work with relatively small datasets. In particular, the
optimisation loss is based on multi-task learning that jointly trains for the
tasks of segmentation and diagnosis classification. We hypothesize that
segmentation has a regularizing effect on the learning of features relevant for
diagnosis. Using the 100 training and 50 testing samples available from the
Automated Cardiac Diagnosis Challenge (ACDC) dataset, which has a balanced
distribution of 5 cardiac diagnoses, we observe a reduction of the
classification error from 32% to 22%, and a faster convergence compared to a
baseline without segmentation. To the best of our knowledge, this is the best
diagnosis results from CMR using an end-to-end diagnosis and segmentation
learning method.
Resource-Constrained Simultaneous Detection and Labeling of Objects in High-Resolution Satellite Images
Authors: Gilbert Rotich, Rodrigo Minetto, Sudeep Sarkar
Date: 2018-10-23 00:00:00
Tasks: CPU

We describe a strategy for detection and classification of man-made objects
in large high-resolution satellite photos under computational resource
constraints. We detect and classify candidate objects by using five pipelines
of convolutional neural network processing (CNN), run in parallel. Each
pipeline has its own unique strategy for fine tunning parameters, proposal
region filtering, and dealing with image scales. The conflicting region
proposals are merged based on region confidence and not just based on overlap
areas, which improves the quality of the final bounding-box regions selected.
We demonstrate this strategy using the recent xView challenge, which is a
complex benchmark with more than 1,100 high-resolution images, spanning 800,000
aerial objects around the world covering a total area of 1,400 square
kilometers at 0.3 meter ground sample distance. To tackle the
resource-constrained problem posed by the xView challenge, where inferences are
restricted to be on CPU with 8GB memory limit, we used lightweight CNN's
trained with the single shot detector algorithm. Our approach was competitive
on sequestered sets; it was ranked third.
Reproducing AmbientGAN: Generative models from lossy measurements
Authors: Mehdi Ahmadi, Timothy Nest, Mostafa Abdelnaim, Thanh-Dung Le
Date: 2018-10-23 00:00:00
Tasks: compressed sensing

In recent years, Generative Adversarial Networks (GANs) have shown
substantial progress in modeling complex distributions of data. These networks
have received tremendous attention since they can generate implicit
probabilistic models that produce realistic data using a stochastic procedure.
While such models have proven highly effective in diverse scenarios, they
require a large set of fully-observed training samples. In many applications
access to such samples are difficult or even impractical and only noisy or
partial observations of the desired distribution is available. Recent research
has tried to address the problem of incompletely observed samples to recover
the distribution of the data. \citep{zhu2017unpaired} and
\citep{yeh2016semantic} proposed methods to solve ill-posed inverse problem
using cycle-consistency and latent-space mappings in adversarial networks,
respectively. \citep{bora2017compressed} and \citep{kabkab2018task} have
applied similar adversarial approaches to the problem of compressed sensing. In
this work, we focus on a new variant of GAN models called AmbientGAN, which
incorporates a measurement process (e.g. adding noise, data removal and
projection) into the GAN training. While in the standard GAN, the discriminator
distinguishes a generated image from a real image, in AmbientGAN model the
discriminator has to separate a real measurement from a simulated measurement
of a generated image. The results shown by \citep{bora2018ambientgan} are quite
promising for the problem of incomplete data, and have potentially important
implications for generative approaches to compressed sensing and ill-posed
problems.
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
Authors: Abubakar Abid, James Zou
Date: 2018-10-23 00:00:00
Proceeding: NeurIPS 2018
Tasks: Astronomy, Dynamic Time Warping, Time Series, Time Series Analysis

Measuring similarities between unlabeled time series trajectories is an
important problem in domains as diverse as medicine, astronomy, finance, and
computer vision. It is often unclear what is the appropriate metric to use
because of the complex nature of noise in the trajectories (e.g. different
sampling rates or outliers). Domain experts typically hand-craft or manually
select a specific metric, such as dynamic time warping (DTW), to apply on their
data. In this paper, we propose Autowarp, an end-to-end algorithm that
optimizes and learns a good metric given unlabeled trajectories. We define a
flexible and differentiable family of warping metrics, which encompasses common
metrics such as DTW, Euclidean, and edit distance. Autowarp then leverages the
representation power of sequence autoencoders to optimize for a member of this
warping distance family. The output is a metric which is easy to interpret and
can be robustly learned from relatively few trajectories. In systematic
experiments across different domains, we show that Autowarp often outperforms
hand-crafted trajectory similarity metrics.
Comparative Evaluation of Tree-Based Ensemble Algorithms for Short-Term Travel Time Prediction
Authors: Saleh Mousa, Sherif Ishak
Date: 2018-10-23 00:00:00
Tasks: Prediction

Disseminating accurate travel time information to road users helps achieve
traffic equilibrium and reduce traffic congestion. The deployment of Connected
Vehicles technology will provide unique opportunities for the implementation of
travel time prediction models. The aim of this study is twofold: (1) estimate
travel times in the freeway network at five-minute intervals using Basic Safety
Messages (BSM); (2) develop an eXtreme Gradient Boosting (XGB) model for
short-term travel time prediction on freeways. The XGB tree-based ensemble
prediction model is evaluated against common tree-based ensemble algorithms and
the evaluations are performed at five-minute intervals over a 30-minute
horizon. BSMs generated by the Safety Pilot Model Deployment conducted in Ann
Arbor, Michigan, were used. Nearly two billion messages were processed for
providing travel time estimates for the entire freeway network. A Combination
of grid search and five-fold cross-validation techniques using the travel time
estimates were used for developing the prediction models and tuning their
parameters. About 9.6 km freeway stretch was used for evaluating the XGB
together with the most common tree-based ensemble algorithms. The results show
that XGB is superior to all other algorithms, followed by the Gradient
Boosting. XGB travel time predictions were accurate and consistent with
variations during peak periods, with mean absolute percentage error in
prediction about 5.9% and 7.8% for 5-minute and 30-minute horizons,
respectively. Additionally, through applying the developed models to another
4.7 km stretch along the eastbound segment of M-14, the XGB demonstrated its
considerable advantages in travel time prediction during congested and
uncongested conditions.
Deblending galaxy superpositions with branched generative adversarial networks
Authors: David M. Reiman, Brett E. Göhre
Date: 2018-10-23 00:00:00
Tasks: Generative Adversarial Network, Segmentation, Semantic Segmentation

Near-future large galaxy surveys will encounter blended galaxy images at a
fraction of up to 50% in the densest regions of the universe. Current
deblending techniques may segment the foreground galaxy while leaving missing
pixel intensities in the background galaxy flux. The problem is compounded by
the diffuse nature of galaxies in their outer regions, making segmentation
significantly more difficult than in traditional object segmentation
applications. We propose a novel branched generative adversarial network (GAN)
to deblend overlapping galaxies, where the two branches produce images of the
two deblended galaxies. We show that generative models are a powerful engine
for deblending given their innate ability to infill missing pixel values
occluded by the superposition. We maintain high peak signal-to-noise ratio and
structural similarity scores with respect to ground truth images upon
deblending. Our model also predicts near-instantaneously, making it a natural
choice for the immense quantities of data soon to be created by large surveys
such as LSST, Euclid and WFIRST.