pwc-archive/papers-with-abstracts huggingface dataset:
How to Read Many-Objective Solution Sets in Parallel Coordinates
Authors: Miqing Li, Liangli Zhen, Xin Yao
Date: 2017-04-30 00:00:00
Tasks: Evolutionary Algorithms

Rapid development of evolutionary algorithms in handling many-objective
optimization problems requires viable methods of visualizing a high-dimensional
solution set. Parallel coordinates which scale well to high-dimensional data
are such a method, and have been frequently used in evolutionary many-objective
optimization. However, the parallel coordinates plot is not as straightforward
as the classic scatter plot to present the information contained in a solution
set. In this paper, we make some observations of the parallel coordinates plot,
in terms of comparing the quality of solution sets, understanding the shape and
distribution of a solution set, and reflecting the relation between objectives.
We hope that these observations could provide some guidelines as to the proper
use of parallel coordinates in evolutionary many-objective optimization.
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
Authors: Danna Gurari, Kun He, Bo Xiong, Jianming Zhang, Mehrnoosh Sameki, Suyog Dutt Jain, Stan Sclaroff, Margrit Betke, Kristen Grauman
Date: 2017-04-30 00:00:00
Tasks: Diversity, Object, Semantic Segmentation, valid

We propose the ambiguity problem for the foreground object segmentation task
and motivate the importance of estimating and accounting for this ambiguity
when designing vision systems. Specifically, we distinguish between images
which lead multiple annotators to segment different foreground objects
(ambiguous) versus minor inter-annotator differences of the same object. Taking
images from eight widely used datasets, we crowdsource labeling the images as
"ambiguous" or "not ambiguous" to segment in order to construct a new dataset
we call STATIC. Using STATIC, we develop a system that automatically predicts
which images are ambiguous. Experiments demonstrate the advantage of our
prediction system over existing saliency-based methods on images from vision
benchmarks and images taken by blind people who are trying to recognize objects
in their environment. Finally, we introduce a crowdsourcing system to achieve
cost savings for collecting the diversity of all valid "ground truth"
foreground object segmentations by collecting extra segmentations only when
ambiguity is expected. Experiments show our system eliminates up to 47% of
human effort compared to existing crowdsourcing methods with no loss in
capturing the diversity of ground truths.
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
Authors: John Wieting, Kevin Gimpel
Date: 2017-04-30 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence, Sentence Embeddings, Transfer Learning

We consider the problem of learning general-purpose, paraphrastic sentence
embeddings, revisiting the setting of Wieting et al. (2016b). While they found
LSTM recurrent networks to underperform word averaging, we present several
developments that together produce the opposite conclusion. These include
training on sentence pairs rather than phrase pairs, averaging states to
represent sequences, and regularizing aggressively. These improve LSTMs in both
transfer learning and supervised settings. We also introduce a new recurrent
architecture, the Gated Recurrent Averaging Network, that is inspired by
averaging and LSTMs while outperforming them both. We analyze our learned
models, finding evidence of preferences for particular parts of speech and
dependency relations.
Yum-me: A Personalized Nutrient-based Meal Recommender System
Authors: Longqi Yang, Cheng-Kang Hsieh, Hongjian Yang, Nicola Dell, Serge Belongie, Curtis Cole, Deborah Estrin
Date: 2016-05-25 00:00:00
Tasks: Benchmarking, Recommendation Systems

Nutrient-based meal recommendations have the potential to help individuals
prevent or manage conditions such as diabetes and obesity. However, learning
people's food preferences and making recommendations that simultaneously appeal
to their palate and satisfy nutritional expectations are challenging. Existing
approaches either only learn high-level preferences or require a prolonged
learning period. We propose Yum-me, a personalized nutrient-based meal
recommender system designed to meet individuals' nutritional expectations,
dietary restrictions, and fine-grained food preferences. Yum-me enables a
simple and accurate food preference profiling procedure via a visual quiz-based
user interface, and projects the learned profile into the domain of
nutritionally appropriate food options to find ones that will appeal to the
user. We present the design and implementation of Yum-me, and further describe
and evaluate two innovative contributions. The first contriution is an open
source state-of-the-art food image analysis model, named FoodDist. We
demonstrate FoodDist's superior performance through careful benchmarking and
discuss its applicability across a wide array of dietary applications. The
second contribution is a novel online learning framework that learns food
preference from item-wise and pairwise image comparisons. We evaluate the
framework in a field study of 227 anonymous users and demonstrate that it
outperforms other baselines by a significant margin. We further conducted an
end-to-end validation of the feasibility and effectiveness of Yum-me through a
60-person user study, in which Yum-me improves the recommendation acceptance
rate by 42.63%.
Deep Learning in the Automotive Industry: Applications and Tools
Authors: Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster
Date: 2017-04-30 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, speech-recognition, Speech Recognition

Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.
Deriving Quests from Open World Mechanics
Authors: Ryan Alexander, Chris Martens
Date: 2017-04-30 00:00:00
Tasks: Minecraft

Open world games present players with more freedom than games with linear
progression structures. However, without clearly-defined objectives, they often
leave players without a sense of purpose. Most of the time, quests and
objectives are hand-authored and overlaid atop an open world's mechanics. But
what if they could be generated organically from the gameplay itself? The goal
of our project was to develop a model of the mechanics in Minecraft that could
be used to determine the ideal placement of objectives in an open world
setting. We formalized the game logic of Minecraft in terms of logical rules
that can be manipulated in two ways: they may be executed to generate graphs
representative of the player experience when playing an open world game with
little developer direction; and they may be statically analyzed to determine
dependency orderings, feedback loops, and bottlenecks. These analyses may then
be used to place achievements on gameplay actions algorithmically.
Quantifying Mental Health from Social Media with Neural User Embeddings
Authors: Silvio Amir, Glen Coppersmith, Paula Carvalho, Mário J. Silva, Byron C. Wallace
Date: 2017-04-30 00:00:00
Tasks: Representation Learning

Mental illnesses adversely affect a significant proportion of the population
worldwide. However, the methods traditionally used for estimating and
characterizing the prevalence of mental health conditions are time-consuming
and expensive. Consequently, best-available estimates concerning the prevalence
of mental health conditions are often years out of date. Automated approaches
to supplement these survey methods with broad, aggregated information derived
from social media content provides a potential means for near real-time
estimates at scale. These may, in turn, provide grist for supporting,
evaluating and iteratively improving upon public health programs and
interventions.
  We propose a novel model for automated mental health status quantification
that incorporates user embeddings. This builds upon recent work exploring
representation learning methods that induce embeddings by leveraging social
media post histories. Such embeddings capture latent characteristics of
individuals (e.g., political leanings) and encode a soft notion of homophily.
In this paper, we investigate whether user embeddings learned from twitter post
histories encode information that correlates with mental health statuses. To
this end, we estimated user embeddings for a set of users known to be affected
by depression and post-traumatic stress disorder (PTSD), and for a set of
demographically matched `control' users. We then evaluated these embeddings
with respect to: (i) their ability to capture homophilic relations with respect
to mental health status; and (ii) the performance of downstream mental health
prediction models based on these features. Our experimental results demonstrate
that the user embeddings capture similarities between users with respect to
mental conditions, and are predictive of mental health.
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models
Authors: Mohammad Amin Morid, Olivia R. Liu Sheng, Samir AbdelRahman
Date: 2017-04-25 00:00:00
Tasks: Change Point Detection, Classification, Dimensionality Reduction, Feature Engineering, General Classification, ICU Mortality, Imputation, Missing Values, Mortality Prediction, Time Series, Time Series Analysis, Time Series Classification

Patient time series classification faces challenges in high degrees of
dimensionality and missingness. In light of patient similarity theory, this
study explores effective temporal feature engineering and reduction, missing
value imputation, and change point detection methods that can afford
similarity-based classification models with desirable accuracy enhancement. We
select a piecewise aggregation approximation method to extract fine-grain
temporal features and propose a minimalist method to impute missing values in
temporal features. For dimensionality reduction, we adopt a gradient descent
search method for feature weight assignment. We propose new patient status and
directional change definitions based on medical knowledge or clinical
guidelines about the value ranges for different patient status levels, and
develop a method to detect change points indicating positive or negative
patient status changes. We evaluate the effectiveness of the proposed methods
in the context of early Intensive Care Unit mortality prediction. The
evaluation results show that the k-Nearest Neighbor algorithm that incorporates
methods we select and propose significantly outperform the relevant benchmarks
for early ICU mortality prediction. This study makes contributions to time
series classification and early ICU mortality prediction via identifying and
enhancing temporal feature engineering and reduction methods for
similarity-based time series classification.
Clustering in Hilbert simplex geometry
Authors: Frank Nielsen, Ke Sun
Date: 2017-04-03 00:00:00
Tasks: Clustering

Clustering categorical distributions in the finite-dimensional probability simplex is a fundamental task met in many applications dealing with normalized histograms. Traditionally, the differential-geometric structures of the probability simplex have been used either by (i) setting the Riemannian metric tensor to the Fisher information matrix of the categorical distributions, or (ii) defining the dualistic information-geometric structure induced by a smooth dissimilarity measure, the Kullback-Leibler divergence. In this work, we introduce for clustering tasks a novel computationally-friendly framework for modeling geometrically the probability simplex: The {\em Hilbert simplex geometry}. In the Hilbert simplex geometry, the distance is the non-separable Hilbert's metric distance which satisfies the property of information monotonicity with distance level set functions described by polytope boundaries. We show that both the Aitchison and Hilbert simplex distances are norm distances on normalized logarithmic representations with respect to the $\ell_2$ and variation norms, respectively. We discuss the pros and cons of those different statistical modelings, and benchmark experimentally these different kind of geometries for center-based $k$-means and $k$-center clustering. Furthermore, since a canonical Hilbert distance can be defined on any bounded convex subset of the Euclidean space, we also consider Hilbert's geometry of the elliptope of correlation matrices and study its clustering performances compared to Fr\"obenius and log-det divergences.
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification
Authors: Zaidao Wen, Biao Hou, Licheng Jiao
Date: 2017-04-30 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification, Operator learning

Linear synthesis model based dictionary learning framework has achieved
remarkable performances in image classification in the last decade. Behaved as
a generative feature model, it however suffers from some intrinsic
deficiencies. In this paper, we propose a novel parametric nonlinear analysis
cosparse model (NACM) with which a unique feature vector will be much more
efficiently extracted. Additionally, we derive a deep insight to demonstrate
that NACM is capable of simultaneously learning the task adapted feature
transformation and regularization to encode our preferences, domain prior
knowledge and task oriented supervised information into the features. The
proposed NACM is devoted to the classification task as a discriminative feature
model and yield a novel discriminative nonlinear analysis operator learning
framework (DNAOL). The theoretical analysis and experimental performances
clearly demonstrate that DNAOL will not only achieve the better or at least
competitive classification accuracies than the state-of-the-art algorithms but
it can also dramatically reduce the time complexities in both training and
testing phases.
SurfCut: Surfaces of Minimal Paths From Topological Structures
Authors: Marei Algarni, Ganesh Sundaramoorthi
Date: 2017-04-30 00:00:00

We present SurfCut, an algorithm for extracting a smooth, simple surface with
an unknown 3D curve boundary from a noisy 3D image and a seed point. Our method
is built on the novel observation that certain ridge curves of a function
defined on a front propagated using the Fast Marching algorithm lie on the
surface. Our method extracts and cuts these ridges to form the surface
boundary. Our surface extraction algorithm is built on the novel observation
that the surface lies in a valley of the distance from Fast Marching. We show
that the resulting surface is a collection of minimal paths. Using the
framework of cubical complexes and Morse theory, we design algorithms to
extract these critical structures robustly. Experiments on three 3D datasets
show the robustness of our method, and that it achieves higher accuracy with
lower computational cost than state-of-the-art.
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches
Authors: Ester Hait, Guy Gilboa
Date: 2016-09-27 00:00:00
Tasks: Denoising, Image Enhancement, Super-Resolution

We propose to combine semantic data and registration algorithms to solve
various image processing problems such as denoising, super-resolution and
color-correction. It is shown how such new techniques can achieve significant
quality enhancement, both visually and quantitatively, in the case of facial
image enhancement. Our model assumes prior high quality data of the person to
be processed, but no knowledge of the degradation model. We try to overcome the
classical processing limits by using semantically-aware patches, with adaptive
size and location regions of coherent structure and context, as building
blocks. The method is demonstrated on the problem of cellular photography
enhancement of dark facial images for different identities, expressions and
poses.
Sequence Graph Transform (SGT): A Feature Extraction Function for Sequence Data Mining (Extended Version)
Authors: Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar
Date: 2016-08-11 00:00:00
Tasks: Clustering

The ubiquitous presence of sequence data across fields such as the web,
healthcare, bioinformatics, and text mining has made sequence mining a vital
research area. However, sequence mining is particularly challenging because of
difficulty in finding (dis)similarity/distance between sequences. This is
because a distance measure between sequences is not obvious due to their
unstructuredness---arbitrary strings of arbitrary length. Feature
representations, such as n-grams, are often used but they either compromise on
extracting both short- and long-term sequence patterns or have a high
computation. We propose a new function, Sequence Graph Transform (SGT), that
extracts the short- and long-term sequence features and embeds them in a
finite-dimensional feature space. Importantly, SGT has low computation and can
extract any amount of short- to long-term patterns without any increase in the
computation, also proved theoretically in this paper. Due to this, SGT yields
superior result with significantly higher accuracy and lower computation
compared to the existing methods. We show it via several experimentation and
SGT's real world application for clustering, classification, search and
visualization as examples.
Indoor Frame Recovery from Refined Line Segments
Authors: Luanzheng Guo, Jun Chu
Date: 2017-04-30 00:00:00

An important yet challenging problem in understanding indoor scene is
recovering indoor frame structure from a monocular image. It is more difficult
when occlusions and illumination vary, and object boundaries are weak. To
overcome these difficulties, a new approach based on line segment refinement
with two constraints is proposed. First, the line segments are refined by four
consecutive operations, i.e., reclassifying, connecting, fitting, and voting.
Specifically, misclassified line segments are revised by the reclassifying
operation, some short line segments are joined by the connecting operation, the
undetected key line segments are recovered by the fitting operation with the
help of the vanishing points, the line segments converging on the frame are
selected by the voting operation. Second, we construct four frame models
according to four classes of possible shooting angles of the monocular image,
the natures of all frame models are introduced via enforcing the cross ratio
and depth constraints. The indoor frame is then constructed by fitting those
refined line segments with related frame model under the two constraints, which
jointly advance the accuracy of the frame. Experimental results on a collection
of over 300 indoor images indicate that our algorithm has the capability of
recovering the frame from complex indoor scenes.
Deep Self-Taught Learning for Weakly Supervised Object Localization
Authors: Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng, Wei Liu
Date: 2017-04-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Localization, Weakly Supervised Object Detection, Weakly-Supervised Object Localization

Most existing weakly supervised localization (WSL) approaches learn detectors
by finding positive bounding boxes based on features learned with image-level
supervision. However, those features do not contain spatial location related
information and usually provide poor-quality positive samples for training a
detector. To overcome this issue, we propose a deep self-taught learning
approach, which makes the detector learn the object-level features reliable for
acquiring tight positive samples and afterwards re-train itself based on them.
Consequently, the detector progressively improves its detection ability and
localizes more informative positive samples. To implement such self-taught
learning, we propose a seed sample acquisition method via image-to-object
transferring and dense subgraph discovery to find reliable positive samples for
initializing the detector. An online supportive sample harvesting scheme is
further proposed to dynamically select the most confident tight positive
samples and train the detector in a mutual boosting way. To prevent the
detector from being trapped in poor optima due to overfitting, we propose a new
relative improvement of predicted CNN scores for guiding the self-taught
learning process. Extensive experiments on PASCAL 2007 and 2012 show that our
approach outperforms the state-of-the-arts, strongly validating its
effectiveness.
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree
Authors: Amin Zheng, Gene Cheung, Dinei Florencio
Date: 2017-04-30 00:00:00
Tasks: Action Recognition, Denoising, object-detection, Object Detection, Temporal Action Localization

With the advent of depth sensing technologies, the extraction of object
contours in images---a common and important pre-processing step for later
higher-level computer vision tasks like object detection and human action
recognition---has become easier. However, acquisition noise in captured depth
images means that detected contours suffer from unavoidable errors. In this
paper, we propose to jointly denoise and compress detected contours in an image
for bandwidth-constrained transmission to a client, who can then carry out
aforementioned application-specific tasks using the decoded contours as input.
We first prove theoretically that in general a joint denoising / compression
approach can outperform a separate two-stage approach that first denoises then
encodes contours lossily. Adopting a joint approach, we first propose a burst
error model that models typical errors encountered in an observed string y of
directional edges. We then formulate a rate-constrained maximum a posteriori
(MAP) problem that trades off the posterior probability p(x'|y) of an estimated
string x' given y with its code rate R(x'). We design a dynamic programming
(DP) algorithm that solves the posed problem optimally, and propose a compact
context representation called total suffix tree (TST) that can reduce
complexity of the algorithm dramatically. Experimental results show that our
joint denoising / compression scheme outperformed a competing separate scheme
in rate-distortion performance noticeably.