pwc-archive/papers-with-abstracts huggingface dataset:
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
Authors: Xiaoran Liu, Qin Lin, Sicco Verwer, Dmitri Jarnikov
Date: 2017-05-24 00:00:00
Tasks: Anomaly Detection, One-class classifier

This paper focuses on detecting anomalies in a digital video broadcasting
(DVB) system from providers' perspective. We learn a probabilistic
deterministic real timed automaton profiling benign behavior of encryption
control in the DVB control access system. This profile is used as a one-class
classifier. Anomalous items in a testing sequence are detected when the
sequence is not accepted by the learned model.
Towards Understanding the Invertibility of Convolutional Neural Networks
Authors: Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee
Date: 2017-05-24 00:00:00
Tasks: Compressive Sensing, General Classification

Several recent works have empirically observed that Convolutional Neural Nets
(CNNs) are (approximately) invertible. To understand this approximate
invertibility phenomenon and how to leverage it more effectively, we focus on a
theoretical explanation and develop a mathematical model of sparse signal
recovery that is consistent with CNNs with random weights. We give an exact
connection to a particular model of model-based compressive sensing (and its
recovery algorithms) and random-weight CNNs. We show empirically that several
learned networks are consistent with our mathematical analysis and then
demonstrate that with such a simple theoretical framework, we can obtain
reasonable re- construction results on real images. We also discuss gaps
between our model assumptions and the CNN trained for classification in
practical scenarios.
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
Authors: Lahari Poddar, Wynne Hsu, Mong Li Lee
Date: 2017-05-15 00:00:00
Tasks: Bayesian Inference

User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
Computer vision-based food calorie estimation: dataset, method, and experiment
Authors: Yanchao Liang, Jianhua Li
Date: 2017-05-22 00:00:00

Computer vision has been introduced to estimate calories from food images.
But current food image data sets don't contain volume and mass records of
foods, which leads to an incomplete calorie estimation. In this paper, we
present a novel food image data set with volume and mass records of foods, and
a deep learning method for food detection, to make a complete calorie
estimation. Our data set includes 2978 images, and every image contains
corresponding each food's annotation, volume and mass records, as well as a
certain calibration reference. To estimate calorie of food in the proposed data
set, a deep learning method using Faster R-CNN first is put forward to detect
the food. And the experiment results show our method is effective to estimate
calories and our data set contains adequate information for calorie estimation.
Our data set is the first released food image data set which can be used to
evaluate computer vision-based calorie estimation methods.
Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data
Authors: Jinfeng Yi, Qi Lei, Wesley Gifford, Ji Liu, Junchi Yan
Date: 2017-02-21 00:00:00

Identifying significant location categories visited by mobile users is the
key to a variety of applications. This is an extremely challenging task due to
the possible deviation between the estimated location coordinate and the actual
location, which could be on the order of kilometers. To estimate the actual
location category more precisely, we propose a novel tensor factorization
framework, through several key observations including the intrinsic
correlations between users, to infer the most likely location categories within
the location uncertainty circle. In addition, the proposed algorithm can also
predict where users are even in the absence of location information. In order
to efficiently solve the proposed framework, we propose a parameter-free and
scalable optimization algorithm by effectively exploring the sparse and
low-rank structure of the tensor. Our empirical studies show that the proposed
algorithm is both efficient and effective: it can solve problems with millions
of users and billions of location updates, and also provides superior
prediction accuracies on real-world location updates and check-in data sets.
Self-supervised learning of visual features through embedding images into text topic spaces
Authors: Lluis Gomez, Yash Patel, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
Date: 2017-05-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: image-classification, Image Classification, object-detection, Object Detection, Retrieval, Self-Supervised Learning

End-to-end training from scratch of current deep architectures for new
computer vision problems would require Imagenet-scale datasets, and this is not
always possible. In this paper we present a method that is able to take
advantage of freely available multi-modal content to train computer vision
algorithms without human supervision. We put forward the idea of performing
self-supervised learning of visual features by mining a large scale corpus of
multi-modal (text and image) documents. We show that discriminative visual
features can be learnt efficiently by training a CNN to predict the semantic
context in which a particular image is more probable to appear as an
illustration. For this we leverage the hidden semantic structures discovered in
the text corpus with a well-known topic modeling technique. Our experiments
demonstrate state of the art performance in image classification, object
detection, and multi-modal retrieval compared to recent self-supervised or
natural-supervised approaches.
Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces
Authors: Zhulin Liu, C. L. Philip Chen
Date: 2017-05-24 00:00:00
Tasks: Relation

A new Hardy space Hardy space approach of Dirichlet type problem based on
Tikhonov regularization and Reproducing Hilbert kernel space is discussed in
this paper, which turns out to be a typical extremal problem located on the
upper upper-high complex plane. If considering this in the Hardy space, the
optimization operator of this problem will be highly simplified and an
efficient algorithm is possible. This is mainly realized by the help of
reproducing properties of the functions in the Hardy space of upper-high
complex plane, and the detail algorithm is proposed. Moreover, harmonic
mappings, which is a significant geometric transformation, are commonly used in
many applications such as image processing, since it describes the energy
minimization mappings between individual manifolds. Particularly, when we focus
on the planer mappings between two Euclid planer regions, the harmonic mappings
are exist and unique, which is guaranteed solidly by the existence of harmonic
function. This property is attractive and simulation results are shown in this
paper to ensure the capability of applications such as planer shape distortion
and surface registration.
Sample Efficient Policy Search for Optimal Stopping Domains
Authors: Karan Goel, Christoph Dann, Emma Brunskill
Date: 2017-02-21 00:00:00

Optimal stopping problems consider the question of deciding when to stop an
observation-generating process in order to maximize a return. We examine the
problem of simultaneously learning and planning in such domains, when data is
collected directly from the environment. We propose GFSE, a simple and flexible
model-free policy search method that reuses data for sample efficiency by
leveraging problem structure. We bound the sample complexity of our approach to
guarantee uniform convergence of policy value estimates, tightening existing
PAC bounds to achieve logarithmic dependence on horizon length for our setting.
We also examine the benefit of our method against prevalent model-based and
model-free approaches on 3 domains taken from diverse fields.
VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach
Authors: Yassine Maalej, Sameh Sorour, Ahmed Abdel-Rahim, Mohsen Guizani
Date: 2017-05-24 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

In this paper, we design a multimodal framework for object detection,
recognition and mapping based on the fusion of stereo camera frames, point
cloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages
(BSMs) exchanged using Dedicated Short Range Communication (DSRC). We merge the
key features of rich texture descriptions of objects from 2D images, depth and
distance between objects provided by 3D point cloud and awareness of hidden
vehicles from BSMs' 3D information. We present a joint pixel to point cloud and
pixel to V2V correspondences of objects in frames from the Kitti Vision
Benchmark Suite by using a semi-supervised manifold alignment approach to
achieve camera-Lidar and camera-V2V mapping of their recognized objects that
have the same underlying manifold.
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Authors: Lingkun Luo, Xiaofang Wang, Shiqiang Hu, Liming Chen
Date: 2017-05-24 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Transfer Learning

Domain adaptation (DA) is transfer learning which aims to leverage labeled
data in a related source domain to achieve informed knowledge transfer and help
the classification of unlabeled data in a target domain. In this paper, we
propose a novel DA method, namely Robust Data Geometric Structure Aligned,
Close yet Discriminative Domain Adaptation (RSA-CDDA), which brings closer, in
a latent joint subspace, both source and target data distributions, and aligns
inherent hidden source and target data geometric structures while performing
discriminative DA in repulsing both interclass source and target data. The
proposed method performs domain adaptation between source and target in solving
a unified model, which incorporates data distribution constraints, in
particular via a nonparametric distance, i.e., Maximum Mean Discrepancy (MMD),
as well as constraints on inherent hidden data geometric structure segmentation
and alignment between source and target, through low rank and sparse
representation. RSA-CDDA achieves the search of a joint subspace in solving the
proposed unified model through iterative optimization, alternating Rayleigh
quotient algorithm and inexact augmented Lagrange multiplier algorithm.
Extensive experiments carried out on standard DA benchmarks, i.e., 16
cross-domain image classification tasks, verify the effectiveness of the
proposed method, which consistently outperforms the state-of-the-art methods.
Dictionary-based Monitoring of Premature Ventricular Contractions: An Ultra-Low-Cost Point-of-Care Service
Authors: Bollepalli S. Chandra, Challa S. Sastry, Laxminarayana Anumandla, Soumya Jana
Date: 2017-05-24 00:00:00
Tasks: Diagnostic, Management, Sensitivity

While cardiovascular diseases (CVDs) are prevalent across economic strata,
the economically disadvantaged population is disproportionately affected due to
the high cost of traditional CVD management. Accordingly, developing an
ultra-low-cost alternative, affordable even to groups at the bottom of the
economic pyramid, has emerged as a societal imperative. Against this backdrop,
we propose an inexpensive yet accurate home-based electrocardiogram(ECG)
monitoring service. Specifically, we seek to provide point-of-care monitoring
of premature ventricular contractions (PVCs), high frequency of which could
indicate the onset of potentially fatal arrhythmia. Note that a traditional
telecardiology system acquires the ECG, transmits it to a professional
diagnostic centre without processing, and nearly achieves the diagnostic
accuracy of a bedside setup, albeit at high bandwidth cost. In this context, we
aim at reducing cost without significantly sacrificing reliability. To this
end, we develop a dictionary-based algorithm that detects with high sensitivity
the anomalous beats only which are then transmitted. We further compress those
transmitted beats using class-specific dictionaries subject to suitable
reconstruction/diagnostic fidelity. Such a scheme would not only reduce the
overall bandwidth requirement, but also localising anomalous beats, thereby
reducing physicians' burden. Finally, using Monte Carlo cross validation on
MIT/BIH arrhythmia database, we evaluate the performance of the proposed
system. In particular, with a sensitivity target of at most one undetected PVC
in one hundred beats, and a percentage root mean squared difference less than
9% (a clinically acceptable level of fidelity), we achieved about 99.15%
reduction in bandwidth cost, equivalent to 118-fold savings over traditional
telecardiology.
Multi-Task Learning for Contextual Bandits
Authors: Aniket Anand Deshmukh, Urun Dogan, Clayton Scott
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Multi-Armed Bandits, Multi-Task Learning, News Recommendation

Contextual bandits are a form of multi-armed bandit in which the agent has
access to predictive side information (known as the context) for each arm at
each time step, and have been used to model personalized news recommendation,
ad placement, and other applications. In this work, we propose a multi-task
learning framework for contextual bandit problems. Like multi-task learning in
the batch setting, the goal is to leverage similarities in contexts for
different arms so as to improve the agent's ability to predict rewards from
contexts. We propose an upper confidence bound-based multi-task learning
algorithm for contextual bandits, establish a corresponding regret bound, and
interpret this bound to quantify the advantages of learning in the presence of
high task (arm) similarity. We also describe an effective scheme for estimating
task similarity from data, and demonstrate our algorithm's performance on
several data sets.
Depth Creates No Bad Local Minima
Authors: Haihao Lu, Kenji Kawaguchi
Date: 2017-02-27 00:00:00

In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create
non-convex loss surfaces. Then, does depth alone create bad local minima? In
this paper, we prove that without nonlinearity, depth alone does not create bad
local minima, although it induces non-convex loss surface. Using this insight,
we greatly simplify a recently proposed proof to show that all of the local
minima of feedforward deep linear neural networks are global minima. Our
theoretical results generalize previous results with fewer assumptions, and
this analysis provides a method to show similar results beyond square loss in
deep linear models.
Deep Learning Improves Template Matching by Normalized Cross Correlation
Authors: Davit Buniatyan, Thomas Macrina, Dodam Ih, Jonathan Zung, H. Sebastian Seung
Date: 2017-05-24 00:00:00
Tasks: Deep Learning, Template Matching

Template matching by normalized cross correlation (NCC) is widely used for
finding image correspondences. We improve the robustness of this algorithm by
preprocessing images with "siamese" convolutional networks trained to maximize
the contrast between NCC values of true and false matches. The improvement is
quantified using patches of brain images from serial section electron
microscopy. Relative to a parameter-tuned bandpass filter, siamese
convolutional networks significantly reduce false matches. Furthermore, all
false matches can be eliminated by removing a tiny fraction of all matches
based on NCC values. The improved accuracy of our method could be essential for
connectomics, because emerging petascale datasets may require billions of
template matches to assemble 2D images of serial sections into a 3D image
stack. Our method is also expected to generalize to many other computer vision
applications that use NCC template matching to find image correspondences.
Towards Instance Optimal Bounds for Best Arm Identification
Authors: Lijie Chen, Jian Li, Mingda Qiao
Date: 2016-08-22 00:00:00

In the classical best arm identification (Best-$1$-Arm) problem, we are given
$n$ stochastic bandit arms, each associated with a reward distribution with an
unknown mean. We would like to identify the arm with the largest mean with
probability at least $1-\delta$, using as few samples as possible.
Understanding the sample complexity of Best-$1$-Arm has attracted significant
attention since the last decade. However, the exact sample complexity of the
problem is still unknown.
  Recently, Chen and Li made the gap-entropy conjecture concerning the instance
sample complexity of Best-$1$-Arm. Given an instance $I$, let $\mu_{[i]}$ be
the $i$th largest mean and $\Delta_{[i]}=\mu_{[1]}-\mu_{[i]}$ be the
corresponding gap. $H(I)=\sum_{i=2}^n\Delta_{[i]}^{-2}$ is the complexity of
the instance. The gap-entropy conjecture states that
$\Omega\left(H(I)\cdot\left(\ln\delta^{-1}+\mathsf{Ent}(I)\right)\right)$ is an
instance lower bound, where $\mathsf{Ent}(I)$ is an entropy-like term
determined by the gaps, and there is a $\delta$-correct algorithm for
Best-$1$-Arm with sample complexity
$O\left(H(I)\cdot\left(\ln\delta^{-1}+\mathsf{Ent}(I)\right)+\Delta_{[2]}^{-2}\ln\ln\Delta_{[2]}^{-1}\right)$.
If the conjecture is true, we would have a complete understanding of the
instance-wise sample complexity of Best-$1$-Arm.
  We make significant progress towards the resolution of the gap-entropy
conjecture. For the upper bound, we provide a highly nontrivial algorithm which
requires \[O\left(H(I)\cdot\left(\ln\delta^{-1}
+\mathsf{Ent}(I)\right)+\Delta_{[2]}^{-2}\ln\ln\Delta_{[2]}^{-1}\mathrm{polylog}(n,\delta^{-1})\right)\]
samples in expectation. For the lower bound, we show that for any Gaussian
Best-$1$-Arm instance with gaps of the form $2^{-k}$, any $\delta$-correct
monotone algorithm requires $\Omega\left(H(I)\cdot\left(\ln\delta^{-1} +
\mathsf{Ent}(I)\right)\right)$ samples in expectation.
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces
Authors: Anoop Cherian, Suvrit Sra, Richard Hartley
Date: 2017-05-24 00:00:00
Tasks: Action Recognition, Riemannian optimization, Temporal Action Localization, Time Series, Time Series Analysis

Representations that can compactly and effectively capture temporal evolution
of semantic content are important to machine learning algorithms that operate
on multi-variate time-series data. We investigate such representations
motivated by the task of human action recognition. Here each data instance is
encoded by a multivariate feature (such as via a deep CNN) where action
dynamics are characterized by their variations in time. As these features are
often non-linear, we propose a novel pooling method, kernelized rank pooling,
that represents a given sequence compactly as the pre-image of the parameters
of a hyperplane in an RKHS, projections of data onto which captures their
temporal order. We develop this idea further and show that such a pooling
scheme can be cast as an order-constrained kernelized PCA objective; we then
propose to use the parameters of a kernelized low-rank feature subspace as the
representation of the sequences. We cast our formulation as an optimization
problem on generalized Grassmann manifolds and then solve it efficiently using
Riemannian optimization techniques. We present experiments on several action
recognition datasets using diverse feature modalities and demonstrate
state-of-the-art results.
XOR-Sampling for Network Design with Correlated Stochastic Events
Authors: Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes
Date: 2017-05-23 00:00:00

Many network optimization problems can be formulated as stochastic network
design problems in which edges are present or absent stochastically.
Furthermore, protective actions can guarantee that edges will remain present.
We consider the problem of finding the optimal protection strategy under a
budget limit in order to maximize some connectivity measurements of the
network. Previous approaches rely on the assumption that edges are independent.
In this paper, we consider a more realistic setting where multiple edges are
not independent due to natural disasters or regional events that make the
states of multiple edges stochastically correlated. We use Markov Random Fields
to model the correlation and define a new stochastic network design framework.
We provide a novel algorithm based on Sample Average Approximation (SAA)
coupled with a Gibbs or XOR sampler. The experimental results on real road
network data show that the policies produced by SAA with the XOR sampler have
higher quality and lower variance compared to SAA with Gibbs sampler.
Towards Interrogating Discriminative Machine Learning Models
Authors: Wenbo Guo, Kaixuan Zhang, Lin Lin, Sui Huang, Xinyu Xing
Date: 2017-05-23 00:00:00
Tasks: BIG-bench Machine Learning

It is oftentimes impossible to understand how machine learning models reach a
decision. While recent research has proposed various technical approaches to
provide some clues as to how a learning model makes individual decisions, they
cannot provide users with ability to inspect a learning model as a complete
entity. In this work, we propose a new technical approach that augments a
Bayesian regression mixture model with multiple elastic nets. Using the
enhanced mixture model, we extract explanations for a target model through
global approximation. To demonstrate the utility of our approach, we evaluate
it on different learning models covering the tasks of text mining and image
recognition. Our results indicate that the proposed approach not only
outperforms the state-of-the-art technique in explaining individual decisions
but also provides users with an ability to discover the vulnerabilities of a
learning model.
Accelerated Hierarchical Density Clustering
Authors: Leland McInnes, John Healy
Date: 2017-05-20 00:00:00
Tasks: Clustering

We present an accelerated algorithm for hierarchical density based
clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a
significant qualitative improvement over the popular DBSCAN algorithm. The
accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while
supporting variable density clusters, and eliminating the need for the
difficult to tune distance scale parameter. This makes accelerated HDBSCAN* the
default choice for density based clustering.
  Library available at: https://github.com/scikit-learn-contrib/hdbscan
Grounded Recurrent Neural Networks
Authors: Ankit Vani, Yacine Jernite, David Sontag
Date: 2017-05-23 00:00:00

In this work, we present the Grounded Recurrent Neural Network (GRNN), a
recurrent neural network architecture for multi-label prediction which
explicitly ties labels to specific dimensions of the recurrent hidden state (we
call this process "grounding"). The approach is particularly well-suited for
extracting large numbers of concepts from text. We apply the new model to
address an important problem in healthcare of understanding what medical
concepts are discussed in clinical text. Using a publicly available dataset
derived from Intensive Care Units, we learn to label a patient's diagnoses and
procedures from their discharge summary. Our evaluation shows a clear advantage
to using our proposed architecture over a variety of strong baselines.