pwc-archive/papers-with-abstracts huggingface dataset:
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
Authors: S. Alireza Golestaneh, Lina J. Karam
Date: 2017-03-22 00:00:00
Proceeding: CVPR 2017 7

The detection of spatially-varying blur without having any information about
the blur type is a challenging task. In this paper, we propose a novel
effective approach to address the blur detection problem from a single image
without requiring any knowledge about the blur type, level, or camera settings.
Our approach computes blur detection maps based on a novel High-frequency
multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The
evaluations of the proposed approach on a diverse set of blurry images with
different blur types, levels, and contents demonstrate that the proposed
algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise Linear Priors
Authors: Christopher Zach, Christian Häne
Date: 2013-08-14 00:00:00

Label assignment problems with large state spaces are important tasks
especially in computer vision. Often the pairwise interaction (or smoothness
prior) between labels assigned at adjacent nodes (or pixels) can be described
as a function of the label difference. Exact inference in such labeling tasks
is still difficult, and therefore approximate inference methods based on a
linear programming (LP) relaxation are commonly used in practice. In this work
we study how compact linear programs can be constructed for general piecwise
linear smoothness priors. The number of unknowns is O(LK) per pairwise clique
in terms of the state space size $L$ and the number of linear segments K. This
compares to an O(L^2) size complexity of the standard LP relaxation if the
piecewise linear structure is ignored. Our compact construction and the
standard LP relaxation are equivalent and lead to the same (approximate) label
assignment.
Deep Metric Learning via Facility Location
Authors: Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy
Date: 2016-12-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Clustering, Metric Learning, Retrieval, Structured Prediction

Learning the representation and the similarity metric in an end-to-end
fashion with deep networks have demonstrated outstanding results for clustering
and retrieval. However, these recent approaches still suffer from the
performance degradation stemming from the local metric training procedure which
is unaware of the global structure of the embedding space.
  We propose a global metric learning scheme for optimizing the deep metric
embedding with the learnable clustering function and the clustering metric
(NMI) in a novel structured prediction framework.
  Our experiments on CUB200-2011, Cars196, and Stanford online products
datasets show state of the art performance both on the clustering and retrieval
tasks measured in the NMI and Recall@K evaluation metrics.
Forecasting Human Dynamics from Static Images
Authors: Yu-Wei Chao, Jimei Yang, Brian Price, Scott Cohen, Jia Deng
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human Dynamics, Pose Estimation

This paper presents the first study on forecasting human dynamics from static
images. The problem is to input a single RGB image and generate a sequence of
upcoming human body poses in 3D. To address the problem, we propose the 3D Pose
Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on
single-image human pose estimation and sequence prediction, and converts the 2D
predictions into 3D space. We train our 3D-PFNet using a three-step training
strategy to leverage a diverse source of training data, including image and
video based human pose datasets and 3D motion capture (MoCap) data. We
demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and
3D pose recovery through quantitative and qualitative results.
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Authors: Xiaolong Wang, Abhinav Shrivastava, Abhinav Gupta
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection

How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it really possible to see all the occlusions in a dataset?
We argue that like categories, occlusions and object deformations also follow a
long-tail. Some occlusions and deformations are so rare that they hardly
happen; yet we want to learn a model invariant to such occurrences. In this
paper, we propose an alternative solution. We propose to learn an adversarial
network that generates examples with occlusions and deformations. The goal of
the adversary is to generate examples that are difficult for the object
detector to classify. In our framework both the original detector and adversary
are learned in a joint manner. Our experimental results indicate a 2.3% mAP
boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
compared to the Fast-RCNN pipeline. We also release the code for this paper.
ENWalk: Learning Network Features for Spam Detection in Twitter
Authors: K. C. Santosh, Suman Kalyan Maity, Arjun Mukherjee
Date: 2017-04-11 00:00:00
Tasks: Marketing, Spam detection

Social medias are increasing their influence with the vast public information
leading to their active use for marketing by the companies and organizations.
Such marketing promotions are difficult to identify unlike the traditional
medias like TV and newspaper. So, it is very much important to identify the
promoters in the social media. Although, there are active ongoing researches,
existing approaches are far from solving the problem. To identify such
imposters, it is very much important to understand their strategies of social
circle creation and dynamics of content posting. Are there any specific spammer
types? How successful are each types? We analyze these questions in the light
of social relationships in Twitter. Our analyses discover two types of spammers
and their relationships with the dynamics of content posts. Our results
discover novel dynamics of spamming which are intuitive and arguable. We
propose ENWalk, a framework to detect the spammers by learning the feature
representations of the users in the social media. We learn the feature
representations using the random walks biased on the spam dynamics.
Experimental results on large-scale twitter network and the corresponding
tweets show the effectiveness of our approach that outperforms the existing
approaches
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation
Authors: Yue M. Lu, Gen Li
Date: 2017-02-21 00:00:00
Tasks: Retrieval, Vocal Bursts Intensity Prediction

We study a spectral initialization method that serves a key role in recent work on estimating signals in nonconvex settings. Previous analysis of this method focuses on the phase retrieval problem and provides only performance bounds. In this paper, we consider arbitrary generalized linear sensing models and present a precise asymptotic characterization of the performance of the method in the high-dimensional limit. Our analysis also reveals a phase transition phenomenon that depends on the ratio between the number of samples and the signal dimension. When the ratio is below a minimum threshold, the estimates given by the spectral method are no better than random guesses drawn from a uniform distribution on the hypersphere, thus carrying no information; above a maximum threshold, the estimates become increasingly aligned with the target signal. The computational complexity of the method, as measured by the spectral gap, is also markedly different in the two phases. Worked examples and numerical results are provided to illustrate and verify the analytical predictions. In particular, simulations show that our asymptotic formulas provide accurate predictions for the actual performance of the spectral method even at moderate signal dimensions.
Next Generation Business Intelligence and Analytics: A Survey
Authors: Quoc Duy Vo, Jaya Thomas, Shinyoung Cho, Pradipta De, Bong Jun Choi, Lee Sael
Date: 2017-04-11 00:00:00
Tasks: Cloud Computing, Survey

Business Intelligence and Analytics (BI&A) is the process of extracting and
predicting business-critical insights from data. Traditional BI focused on data
collection, extraction, and organization to enable efficient query processing
for deriving insights from historical data. With the rise of big data and cloud
computing, there are many challenges and opportunities for the BI. Especially
with the growing number of data sources, traditional BI\&A are evolving to
provide intelligence at different scales and perspectives - operational BI,
situational BI, self-service BI. In this survey, we review the evolution of
business intelligence systems in full scale from back-end architecture to and
front-end applications. We focus on the changes in the back-end architecture
that deals with the collection and organization of the data. We also review the
changes in the front-end applications, where analytic services and
visualization are the core components. Using a uses case from BI in Healthcare,
which is one of the most complex enterprises, we show how BI\&A will play an
important role beyond the traditional usage. The survey provides a holistic
view of Business Intelligence and Analytics for anyone interested in getting a
complete picture of the different pieces in the emerging next generation BI\&A
solutions.
Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification
Authors: Lu Tian, Shengjin Wang
Date: 2017-04-08 00:00:00
Tasks: Clustering, Metric Learning, Person Re-Identification

Person re-identification is generally divided into two part: first how to
represent a pedestrian by discriminative visual descriptors and second how to
compare them by suitable distance metrics. Conventional methods isolate these
two parts, the first part usually unsupervised and the second part supervised.
The Bag-of-Words (BoW) model is a widely used image representing descriptor in
part one. Its codebook is simply generated by clustering visual features in
Euclidian space. In this paper, we propose to use part two metric learning
techniques in the codebook generation phase of BoW. In particular, the proposed
codebook is clustered under Mahalanobis distance which is learned supervised.
Extensive experiments prove that our proposed method is effective. With several
low level features extracted on superpixel and fused together, our method
outperforms state-of-the-art on person re-identification benchmarks including
VIPeR, PRID450S, and Market1501.
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors
Authors: J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. M. Alaa, M. van der Schaar
Date: 2017-04-11 00:00:00
Tasks: Decision Making, Diagnostic, Specificity, Survival Analysis

Given the limited pool of donor organs, accurate predictions of survival on
the wait list and post transplantation are crucial for cardiac transplantation
decisions and policy. However, current clinical risk scores do not yield
accurate predictions. We develop a new methodology (ToPs, Trees of Predictors)
built on the principle that specific predictors should be used for specific
clusters within the target population. ToPs discovers these specific clusters
of patients and the specific predictor that perform best for each cluster. In
comparison with current clinical risk scoring systems, our method provides
significant improvements in the prediction of survival time on the wait list
and post transplantation. For example, in terms of 3 month survival for
patients who were on the US patient wait list in the period 1985 to 2015, our
method achieves AUC of 0.847, the best commonly used clinical risk score
(MAGGIC) achieves 0.630. In terms of 3 month survival/mortality predictions (in
comparison to MAGGIC), holding specificity at 80.0 percents, our algorithm
correctly predicts survival for 1,228 (26.0 percents more patients out of 4,723
who actually survived, holding sensitivity at 80.0 percents, our algorithm
correctly predicts mortality for 839 (33.0 percents) more patients out of 2,542
who did not survive. Our method achieves similar improvements for other time
horizons and for predictions post transplantation. Therefore, we offer a more
accurate, personalized approach to survival analysis that can benefit patients,
clinicians and policymakers in making clinical decisions and setting clinical
policy. Because risk prediction is widely used in diagnostic and prognostic
clinical decision making across diseases and clinical specialties, the
implications of our methods are far reaching.
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Authors: Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, Anshul Kundaje
Date: 2016-05-05 00:00:00

Note: This paper describes an older version of DeepLIFT. See
https://arxiv.org/abs/1704.02685 for the newer version. Original abstract
follows: The purported "black box" nature of neural networks is a barrier to
adoption in applications where interpretability is essential. Here we present
DeepLIFT (Learning Important FeaTures), an efficient and effective method for
computing importance scores in a neural network. DeepLIFT compares the
activation of each neuron to its 'reference activation' and assigns
contribution scores according to the difference. We apply DeepLIFT to models
trained on natural images and genomic data, and show significant advantages
over gradient-based methods.
Sampled Fictitious Play is Hannan Consistent
Authors: Zifan Li, Ambuj Tewari
Date: 2016-10-05 00:00:00

Fictitious play is a simple and widely studied adaptive heuristic for playing
repeated games. It is well known that fictitious play fails to be Hannan
consistent. Several variants of fictitious play including regret matching,
generalized regret matching and smooth fictitious play, are known to be Hannan
consistent. In this note, we consider sampled fictitious play: at each round,
the player samples past times and plays the best response to previous moves of
other players at the sampled time points. We show that sampled fictitious play,
using Bernoulli sampling, is Hannan consistent. Unlike several existing Hannan
consistency proofs that rely on concentration of measure results, ours instead
uses anti-concentration results from Littlewood-Offord theory.
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
Authors: Pim Moeskops, Jelmer M. Wolterink, Bas H. M. van der Velden, Kenneth G. A. Gilhuijs, Tim Leiner, Max A. Viergever, Ivana Išgum
Date: 2017-04-11 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Automatic segmentation of medical images is an important task for many
clinical applications. In practice, a wide range of anatomical structures are
visualised using different imaging modalities. In this paper, we investigate
whether a single convolutional neural network (CNN) can be trained to perform
different segmentation tasks.
  A single CNN is trained to segment six tissues in MR brain images, the
pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA.
The CNN therefore learns to identify the imaging modality, the visualised
anatomical structures, and the tissue classes.
  For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this
combined training procedure resulted in a segmentation performance equivalent
to that of a CNN trained specifically for that task, demonstrating the high
capacity of CNN architectures. Hence, a single system could be used in clinical
practice to automatically perform diverse segmentation tasks without
task-specific training.
Reconstruction of~3-D Rigid Smooth Curves Moving Free when Two Traceable Points Only are Available
Authors: Mieczysław A. Kłopotek
Date: 2017-04-11 00:00:00

This paper extends previous research in that sense that for orthogonal
projections of rigid smooth (true-3D) curves moving totally free it reduces the
number of required traceable points to two only (the best results known so far
to the author are 3 points from free motion and 2 for motion restricted to
rotation around a fixed direction and and 2 for motion restricted to influence
of a homogeneous force field). The method used is exploitation of information
on tangential projections. It discusses also possibility of simplification of
reconstruction of flat curves moving free for prospective projections.
Quality Aware Network for Set to Set Recognition
Authors: Yu Liu, Junjie Yan, Wanli Ouyang
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Verification, Person Re-Identification

This paper targets on the problem of set to set recognition, which learns the
metric between two image sets. Images in each set belong to the same identity.
Since images in a set can be complementary, they hopefully lead to higher
accuracy in practical applications. However, the quality of each sample cannot
be guaranteed, and samples with poor quality will hurt the metric. In this
paper, the quality aware network (QAN) is proposed to confront this problem,
where the quality of each sample can be automatically learned although such
information is not explicitly provided in the training stage. The network has
two branches, where the first branch extracts appearance feature embedding for
each sample and the other branch predicts quality score for each sample.
Features and quality scores of all samples in a set are then aggregated to
generate the final feature embedding. We show that the two branches can be
trained in an end-to-end manner given only the set-level identity annotation.
Analysis on gradient spread of this mechanism indicates that the quality
learned by the network is beneficial to set-to-set recognition and simplifies
the distribution that the network needs to fit. Experiments on both face
verification and person re-identification show advantages of the proposed QAN.
The source code and network structure can be downloaded at
https://github.com/sciencefans/Quality-Aware-Network.
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database
Authors: Edgar Altszyler, Mariano Sigman, Sidarta Ribeiro, Diego Fernández Slezak
Date: 2016-10-05 00:00:00
Tasks: Word Embeddings

Word embeddings have been extensively studied in large text datasets.
However, only a few studies analyze semantic representations of small corpora,
particularly relevant in single-person text production studies. In the present
paper, we compare Skip-gram and LSA capabilities in this scenario, and we test
both techniques to extract relevant semantic patterns in single-series dreams
reports. LSA showed better performance than Skip-gram in small size training
corpus in two semantic tests. As a study case, we show that LSA can capture
relevant words associations in dream reports series, even in cases of small
number of dreams or low-frequency words. We propose that LSA can be used to
explore words associations in dreams reports, which could bring new insight
into this classic research area of psychology
Counterexample Guided Inductive Optimization
Authors: Rodrigo F. Araujo, Higo F. Albuquerque, Iury V. de Bessa, Lucas C. Cordeiro, Joao Edgar C. Filho
Date: 2017-04-11 00:00:00

This paper describes three variants of a counterexample guided inductive
optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT)
solvers. In particular, CEGIO relies on iterative executions to constrain a
verification procedure, in order to perform inductive generalization, based on
counterexamples extracted from SMT solvers. CEGIO is able to successfully
optimize a wide range of functions, including non-linear and non-convex
optimization problems based on SMT solvers, in which data provided by
counterexamples are employed to guide the verification engine, thus reducing
the optimization domain. The present algorithms are evaluated using a large set
of benchmarks typically employed for evaluating optimization techniques.
Experimental results show the efficiency and effectiveness of the proposed
algorithms, which find the optimal solution in all evaluated benchmarks, while
traditional techniques are usually trapped by local minima.
Optimized Data Pre-Processing for Discrimination Prevention
Authors: Flavio P. Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
Date: 2017-04-11 00:00:00
Tasks: Decision Making

Non-discrimination is a recognized objective in algorithmic decision making.
In this paper, we introduce a novel probabilistic formulation of data
pre-processing for reducing discrimination. We propose a convex optimization
for learning a data transformation with three goals: controlling
discrimination, limiting distortion in individual data samples, and preserving
utility. We characterize the impact of limited sample size in accomplishing
this objective, and apply two instances of the proposed optimization to
datasets, including one on real-world criminal recidivism. The results
demonstrate that all three criteria can be simultaneously achieved and also
reveal interesting patterns of bias in American society.
TristouNet: Triplet Loss for Speaker Turn Embedding
Authors: Hervé Bredin
Date: 2016-09-14 00:00:00
Tasks: Change Detection, Triplet

TristouNet is a neural network architecture based on Long Short-Term Memory
recurrent networks, meant to project speech sequences into a fixed-dimensional
euclidean space. Thanks to the triplet loss paradigm used for training, the
resulting sequence embeddings can be compared directly with the euclidean
distance, for speaker comparison purposes. Experiments on short (between 500ms
and 5s) speech turn comparison and speaker change detection show that
TristouNet brings significant improvements over the current state-of-the-art
techniques for both tasks.
Beliefs and Probability in Bacchus' l.p. Logic: A~3-Valued Logic Solution to Apparent Counter-intuition
Authors: Mieczysław A. Kłopotek
Date: 2017-04-11 00:00:00

Fundamental discrepancy between first order logic and statistical inference
(global versus local properties of universe) is shown to be the obstacle for
integration of logic and probability in L.p. logic of Bacchus. To overcome the
counterintuitiveness of L.p. behaviour, a 3-valued logic is proposed.
Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
Authors: Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas
Date: 2015-05-08 00:00:00
Tasks: Clustering, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, parameter estimation

We introduce a novel approach for estimating Latent Dirichlet Allocation
(LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full
conditional distributions over the latent variable assignments to efficiently
average over multiple samples, for little more computational cost than drawing
a single additional collapsed Gibbs sample. Our approach can be understood as
adapting the soft clustering methodology of Collapsed Variational Bayes (CVB0)
to CGS parameter estimation, in order to get the best of both techniques. Our
estimators can straightforwardly be applied to the output of any existing
implementation of CGS, including modern accelerated variants. We perform
extensive empirical comparisons of our estimators with those of standard
collapsed inference algorithms on real-world data for both unsupervised LDA and
Prior-LDA, a supervised variant of LDA for multi-label classification. Our
results show a consistent advantage of our approach over traditional CGS under
all experimental conditions, and over CVB0 inference in the majority of
conditions. More broadly, our results highlight the importance of averaging
over multiple samples in LDA parameter estimation, and the use of efficient
computational techniques to do so.