pwc-archive/papers-with-abstracts huggingface dataset:
A constrained clustering based approach for matching a collection of feature sets
Authors: Junchi Yan, Zhe Ren, Hongyuan Zha, Stephen Chu
Date: 2016-06-12 00:00:00
Tasks: Attribute, Clustering, Constrained Clustering, set matching

In this paper, we consider the problem of finding the feature correspondences
among a collection of feature sets, by using their point-wise unary features.
This is a fundamental problem in computer vision and pattern recognition, which
also closely relates to other areas such as operational research. Different
from two-set matching which can be transformed to a quadratic assignment
programming task that is known NP-hard, inclusion of merely unary attributes
leads to a linear assignment problem for matching two feature sets. This
problem has been well studied and there are effective polynomial global optimum
solvers such as the Hungarian method. However, it becomes ill-posed when the
unary attributes are (heavily) corrupted. The global optimal correspondence
concerning the best score defined by the attribute affinity/cost between the
two sets can be distinct to the ground truth correspondence since the score
function is biased by noises. To combat this issue, we devise a method for
matching a collection of feature sets by synergetically exploring the
information across the sets. In general, our method can be perceived from a
(constrained) clustering perspective: in each iteration, it assigns the
features of one set to the clusters formed by the rest of feature sets, and
updates the cluster centers in turn. Results on both synthetic data and real
images suggest the efficacy of our method against state-of-the-arts.
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach
Authors: Lin Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-06-06 00:00:00
Tasks: Metric Learning, Person Re-Identification, Time Series, Time Series Analysis, Video-Based Person Re-Identification

In this paper, we present an end-to-end approach to simultaneously learn
spatio-temporal features and corresponding similarity metric for video-based
person re-identification. Given the video sequence of a person, features from
each frame that are extracted from all levels of a deep convolutional network
can preserve a higher spatial resolution from which we can model finer motion
patterns. These low-level visual percepts are leveraged into a variant of
recurrent model to characterize the temporal variation between time-steps.
Features from all time-steps are then summarized using temporal pooling to
produce an overall feature representation for the complete sequence. The deep
convolutional network, recurrent layer, and the temporal pooling are jointly
trained to extract comparable hidden-unit representations from input pair of
time series to compute their corresponding similarity value. The proposed
framework combines time series modeling and metric learning to jointly learn
relevant features and a good similarity measure between time sequences of
person.
  Experiments demonstrate that our approach achieves the state-of-the-art
performance for video-based person re-identification on iLIDS-VID and PRID
2011, the two primary public datasets for this purpose.
Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective
Authors: Pantelis Bouboulis, Spyridon Pougkakiotis, Sergios Theodoridis
Date: 2016-06-12 00:00:00

We present a new framework for online Least Squares algorithms for nonlinear
modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS
(e.g., kernel trick), we map the data to a finite dimensional Euclidean space,
using random features of the kernel's Fourier transform. The advantage is that,
the inner product of the mapped data approximates the kernel function. The
resulting "linear" algorithm does not require any form of sparsification,
since, in contrast to all existing algorithms, the solution's size remains
fixed and does not increase with the iteration steps. As a result, the obtained
algorithms are computationally significantly more efficient compared to
previously derived variants, while, at the same time, they converge at similar
speeds and to similar error floors.
The bag-of-frames approach: a not so sufficient model for urban soundscapes
Authors: Mathieu Lagrange, Grégoire Lafay, Boris Defreville, Jean-Julien Aucouturier
Date: 2014-12-11 00:00:00

The "bag-of-frames" approach (BOF), which encodes audio signals as the
long-term statistical distribution of short-term spectral features, is commonly
regarded as an effective and sufficient way to represent environmental sound
recordings (soundscapes) since its introduction in an influential 2007 article.
The present paper describes a concep-tual replication of this seminal article
using several new soundscape datasets, with results strongly questioning the
adequacy of the BOF approach for the task. We show that the good accuracy
originally re-ported with BOF likely result from a particularly thankful
dataset with low within-class variability, and that for more realistic
datasets, BOF in fact does not perform significantly better than a mere
one-point av-erage of the signal's features. Soundscape modeling, therefore,
may not be the closed case it was once thought to be. Progress, we ar-gue,
could lie in reconsidering the problem of considering individual acoustical
events within each soundscape.
Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples
Authors: Ashkan Esmaeili, Farokh Marvasti
Date: 2016-06-12 00:00:00
Tasks: BIG-bench Machine Learning, compressed sensing

In this paper, we will investigate the efficacy of IMAT (Iterative Method of
Adaptive Thresholding) in recovering the sparse signal (parameters) for linear
models with missing data. Sparse recovery rises in compressed sensing and
machine learning problems and has various applications necessitating viable
reconstruction methods specifically when we work with big data. This paper will
focus on comparing the power of IMAT in reconstruction of the desired sparse
signal with LASSO. Additionally, we will assume the model has random missing
information. Missing data has been recently of interest in big data and machine
learning problems since they appear in many cases including but not limited to
medical imaging datasets, hospital datasets, and massive MIMO. The dominance of
IMAT over the well-known LASSO will be taken into account in different
scenarios. Simulations and numerical results are also provided to verify the
arguments.
Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets
Authors: Alexandre Fioravante de Siqueira, Flávio Camargo Cabrera, Aylton Pagamisse, Aldo Eloizo Job
Date: 2016-06-12 00:00:00

Electronic microscopy has been used for morphology evaluation of different
materials structures. However, microscopy results may be affected by several
factors. Image processing methods can be used to correct and improve the
quality of these results. In this paper we propose an algorithm based on
starlets to perform the segmentation of scanning electron microscopy images. An
application is presented in order to locate gold nanoparticles in natural
rubber membranes. In this application, our method showed accuracy greater than
85% for all test images. Results given by this method will be used in future
studies, to computationally estimate the density distribution of gold
nanoparticles in natural rubber samples and to predict reduction kinetics of
gold nanoparticles at different time periods.
Robust Influence Maximization
Authors: Wei Chen, Tian Lin, Zihan Tan, Mingfei Zhao, Xuren Zhou
Date: 2016-01-25 00:00:00

In this paper, we address the important issue of uncertainty in the edge
influence probability estimates for the well studied influence maximization
problem --- the task of finding $k$ seed nodes in a social network to maximize
the influence spread. We propose the problem of robust influence maximization,
which maximizes the worst-case ratio between the influence spread of the chosen
seed set and the optimal seed set, given the uncertainty of the parameter
input. We design an algorithm that solves this problem with a
solution-dependent bound. We further study uniform sampling and adaptive
sampling methods to effectively reduce the uncertainty on parameters and
improve the robustness of the influence maximization task. Our empirical
results show that parameter uncertainty may greatly affect influence
maximization performance and prior studies that learned influence probabilities
could lead to poor performance in robust influence maximization due to
relatively large uncertainty in parameter estimates, and information cascade
based adaptive sampling method may be an effective way to improve the
robustness of influence maximization.
Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Authors: Soumyabrata Dev, Yee Hui Lee, Stefan Winkler
Date: 2016-06-12 00:00:00
Tasks: Segmentation

Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers)
are increasingly used nowadays because of their applications in a number of
fields, including climate modeling, weather prediction, renewable energy
generation, and satellite communications. Due to the wide variety of cloud
types and lighting conditions in such images, accurate and robust segmentation
of clouds is challenging. In this paper, we present a supervised segmentation
framework for ground-based sky/cloud images based on a systematic analysis of
different color spaces and components, using partial least squares (PLS)
regression. Unlike other state-of-the-art methods, our proposed approach is
entirely learning-based and does not require any manually-defined parameters.
In addition, we release the Singapore Whole Sky IMaging SEGmentation Database
(SWIMSEG), a large database of annotated sky/cloud images, to the research
community.
Weakly Supervised Scalable Audio Content Analysis
Authors: Anurag Kumar, Bhiksha Raj
Date: 2016-06-12 00:00:00
Tasks: Event Detection, Multiple Instance Learning, Weakly-supervised Learning

Audio Event Detection is an important task for content analysis of multimedia
data. Most of the current works on detection of audio events is driven through
supervised learning approaches. We propose a weakly supervised learning
framework which can make use of the tremendous amount of web multimedia data
with significantly reduced annotation effort and expense. Specifically, we use
several multiple instance learning algorithms to show that audio event
detection through weak labels is feasible. We also propose a novel scalable
multiple instance learning algorithm and show that its competitive with other
multiple instance learning algorithms for audio event detection tasks.
Store Location Selection via Mining Search Query Logs of Baidu Maps
Authors: Mengwen Xu, Tianyi Wang, Zhengwei Wu, Jingbo Zhou, Jian Li, Haishan Wu
Date: 2016-06-12 00:00:00
Tasks: Clustering

Choosing a good location when opening a new store is crucial for the future
success of a business. Traditional methods include offline manual survey, which
is very time consuming, and analytic models based on census data, which are un-
able to adapt to the dynamic market. The rapid increase of the availability of
big data from various types of mobile devices, such as online query data and
offline positioning data, provides us with the possibility to develop automatic
and accurate data-driven prediction models for business store placement. In
this paper, we propose a Demand Distribution Driven Store Placement (D3SP)
framework for business store placement by mining search query data from Baidu
Maps. D3SP first detects the spatial-temporal distributions of customer demands
on different business services via query data from Baidu Maps, the largest
online map search engine in China, and detects the gaps between demand and sup-
ply. Then we determine candidate locations via clustering such gaps. In the
final stage, we solve the location optimization problem by predicting and
ranking the number of customers. We not only deploy supervised regression
models to predict the number of customers, but also learn to rank models to
directly rank the locations. We evaluate our framework on various types of
businesses in real-world cases, and the experiments results demonstrate the
effectiveness of our methods. D3SP as the core function for store placement has
already been implemented as a core component of our business analytics platform
and could be potentially used by chain store merchants on Baidu Nuomi.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Authors: Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
Date: 2016-06-12 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Generative Adversarial Network, Image Generation, Representation Learning, Unsupervised Image Classification, Unsupervised MNIST

This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent variables and the observation. We derive a lower bound to
the mutual information objective that can be optimized efficiently, and show
that our training procedure can be interpreted as a variation of the Wake-Sleep
algorithm. Specifically, InfoGAN successfully disentangles writing styles from
digit shapes on the MNIST dataset, pose from lighting of 3D rendered images,
and background digits from the central digit on the SVHN dataset. It also
discovers visual concepts that include hair styles, presence/absence of
eyeglasses, and emotions on the CelebA face dataset. Experiments show that
InfoGAN learns interpretable representations that are competitive with
representations learned by existing fully supervised methods.
Learning Granger Causality for Hawkes Processes
Authors: Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha
Date: 2016-02-14 00:00:00
Tasks: Clustering, Point Processes

Learning Granger causality for general point processes is a very challenging
task. In this paper, we propose an effective method, learning Granger
causality, for a special but significant type of point processes --- Hawkes
process. We reveal the relationship between Hawkes process's impact function
and its Granger causality graph. Specifically, our model represents impact
functions using a series of basis functions and recovers the Granger causality
graph via group sparsity of the impact functions' coefficients. We propose an
effective learning algorithm combining a maximum likelihood estimator (MLE)
with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of
our model allows to incorporate the clustering structure event types into
learning framework. We analyze our learning algorithm and propose an adaptive
procedure to select basis functions. Experiments on both synthetic and
real-world data show that our method can learn the Granger causality graph and
the triggering patterns of the Hawkes processes simultaneously.
metricDTW: local distance metric learning in Dynamic Time Warping
Authors: Jiaping Zhao, Zerong Xi, Laurent Itti
Date: 2016-06-11 00:00:00
Tasks: Dynamic Time Warping, General Classification, Metric Learning, Temporal Sequences, Time Series, Time Series Analysis

We propose to learn multiple local Mahalanobis distance metrics to perform
k-nearest neighbor (kNN) classification of temporal sequences. Temporal
sequences are first aligned by dynamic time warping (DTW); given the alignment
path, similarity between two sequences is measured by the DTW distance, which
is computed as the accumulated distance between matched temporal point pairs
along the alignment path. Traditionally, Euclidean metric is used for distance
computation between matched pairs, which ignores the data regularities and
might not be optimal for applications at hand. Here we propose to learn
multiple Mahalanobis metrics, such that DTW distance becomes the sum of
Mahalanobis distances. We adapt the large margin nearest neighbor (LMNN)
framework to our case, and formulate multiple metric learning as a linear
programming problem. Extensive sequence classification results show that our
proposed multiple metrics learning approach is effective, insensitive to the
preceding alignment qualities, and reaches the state-of-the-art performances on
UCR time series datasets.
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Authors: Muhammad Ammad-Ud-Din, Suleiman A. Khan, Disha Malani, Astrid Murumägi, Olli Kallioniemi, Tero Aittokallio, Samuel Kaski
Date: 2016-06-11 00:00:00
Tasks: Data Integration, Drug Response Prediction

A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially valuable in oncology, where molecular and genetic
heterogeneity of the cells has a major impact on the response. However, the
prediction task is extremely challenging, raising the need for methods that can
effectively model and predict drug responses. In this study, we propose a novel
formulation of multi-task matrix factorization that allows selective data
integration for predicting drug responses. To solve the modeling task, we
extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF)
method with component-wise multiple kernel learning. In addition, our approach
exploits the known pathway information in a novel and biologically meaningful
fashion to learn the drug response associations. Our method quantitatively
outperforms the state of the art on predicting drug responses in two publicly
available cancer data sets as well as on a synthetic data set. In addition, we
validated our model predictions with lab experiments using an in-house cancer
cell line panel. We finally show the practical applicability of the proposed
method by utilizing prior knowledge to infer pathway-drug response
associations, opening up the opportunity for elucidating drug action
mechanisms. We demonstrate that pathway-response associations can be learned by
the proposed model for the well known EGFR and MEK inhibitors.
Data Recombination for Neural Semantic Parsing
Authors: Robin Jia, Percy Liang
Date: 2016-06-11 00:00:00
Proceeding: ACL 2016 8
Tasks: Semantic Parsing

Modeling crisp logical regularities is crucial in semantic parsing, making it
difficult for neural models with no task-specific prior knowledge to achieve
good results. In this paper, we introduce data recombination, a novel framework
for injecting such prior knowledge into a model. From the training data, we
induce a high-precision synchronous context-free grammar, which captures
important conditional independence properties commonly found in semantic
parsing. We then train a sequence-to-sequence recurrent network (RNN) model
with a novel attention-based copying mechanism on datapoints sampled from this
grammar, thereby teaching the model about these structural properties. Data
recombination improves the accuracy of our RNN model on three semantic parsing
datasets, leading to new state-of-the-art performance on the standard GeoQuery
dataset for models with comparable supervision.
Distributional Smoothing with Virtual Adversarial Training
Authors: Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii
Date: 2015-07-02 00:00:00

We propose local distributional smoothness (LDS), a new notion of smoothness
for statistical model that can be used as a regularization term to promote the
smoothness of the model distribution. We named the LDS based regularization as
virtual adversarial training (VAT). The LDS of a model at an input datapoint is
defined as the KL-divergence based robustness of the model distribution against
local perturbation around the datapoint. VAT resembles adversarial training,
but distinguishes itself in that it determines the adversarial direction from
the model distribution alone without using the label information, making it
applicable to semi-supervised learning. The computational cost for VAT is
relatively low. For neural network, the approximated gradient of the LDS can be
computed with no more than three pairs of forward and back propagations. When
we applied our technique to supervised and semi-supervised learning for the
MNIST dataset, it outperformed all the training methods other than the current
state of the art method, which is based on a highly advanced generative model.
We also applied our method to SVHN and NORB, and confirmed our method's
superior performance over the current state of the art semi-supervised method
applied to these datasets.
TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications
Authors: Mohamed Aly, Guangming Zang, Wolfgang Heidrich, Peter Wonka
Date: 2016-06-11 00:00:00

We present TRex, a flexible and robust Tomographic Reconstruction framework
using proximal algorithms. We provide an overview and perform an experimental
comparison between the famous iterative reconstruction methods in terms of
reconstruction quality in sparse view situations. We then derive the proximal
operators for the four best methods. We show the flexibility of our framework
by deriving solvers for two noise models: Gaussian and Poisson; and by plugging
in three powerful regularizers. We compare our framework to state of the art
methods, and show superior quality on both synthetic and real datasets.
Shape from Mixed Polarization
Authors: Vage Taamazyan, Achuta Kadambi, Ramesh Raskar
Date: 2016-05-06 00:00:00
Tasks: valid

Shape from Polarization (SfP) estimates surface normals using photos captured
at different polarizer rotations. Fundamentally, the SfP model assumes that
light is reflected either diffusely or specularly. However, this model is not
valid for many real-world surfaces exhibiting a mixture of diffuse and specular
properties. To address this challenge, previous methods have used a sequential
solution: first, use an existing algorithm to separate the scene into diffuse
and specular components, then apply the appropriate SfP model. In this paper,
we propose a new method that jointly uses viewpoint and polarization data to
holistically separate diffuse and specular components, recover refractive
index, and ultimately recover 3D shape. By involving the physics of
polarization in the separation process, we demonstrate competitive results with
a benchmark method, while recovering additional information (e.g. refractive
index).
Alternative Technique to Asymmetry Analysis-Based Overlapping for Foot Ulcer Examination: Scalable Scanning
Authors: Naima Kaabouch, Wen-Chen Hu, Yi Chen
Date: 2016-06-11 00:00:00

Asymmetry analysis based on the overlapping of thermal images proved able to
detect inflammation and, predict foot ulceration. This technique involves three
main steps: segmentation, geometric transformation, and overlapping. However,
the overlapping technique, which consists of subtracting the intensity levels
of the right foot from those of the left foot, can also detect false abnormal
areas if the projections of the left and right feet are not the same. In this
paper, we present an alternative technique to asymmetry analysis-based
overlapping. The proposed technique, scalable scanning, allows for an effective
comparison even if the shapes and sizes of the feet projections appear
differently in the image. The tested results show that asymmetry analysis-
based scalable scanning provides fewer false abnormal areas than does asymmetry
analysis -based overlapping.