pwc-archive/papers-with-abstracts huggingface dataset:
Joint Text Embedding for Personalized Content-based Recommendation
Authors: Ting Chen, Liangjie Hong, Yue Shi, Yizhou Sun
Date: 2017-06-04 00:00:00
Tasks: News Recommendation, Recommendation Systems

Learning a good representation of text is key to many recommendation
applications. Examples include news recommendation where texts to be
recommended are constantly published everyday. However, most existing
recommendation techniques, such as matrix factorization based methods, mainly
rely on interaction histories to learn representations of items. While latent
factors of items can be learned effectively from user interaction data, in many
cases, such data is not available, especially for newly emerged items.
  In this work, we aim to address the problem of personalized recommendation
for completely new items with text information available. We cast the problem
as a personalized text ranking problem and propose a general framework that
combines text embedding with personalized recommendation. Users and textual
content are embedded into latent feature space. The text embedding function can
be learned end-to-end by predicting user interactions with items. To alleviate
sparsity in interaction data, and leverage large amount of text data with
little or no user interactions, we further propose a joint text embedding model
that incorporates unsupervised text embedding with a combination module.
Experimental results show that our model can significantly improve the
effectiveness of recommendation systems on real-world datasets.
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
Authors: Abhilasha Ravichander, Shruti Rijhwani, Rajat Kulshreshtha, Chirag Nagpal, Tadas Baltrušaitis, Louis-Philippe Morency
Date: 2017-06-23 00:00:00
Tasks: Persuasiveness

Hierarchical models are utilized in a wide variety of problems which are
characterized by task hierarchies, where predictions on smaller subtasks are
useful for trying to predict a final task. Typically, neural networks are first
trained for the subtasks, and the predictions of these networks are
subsequently used as additional features when training a model and doing
inference for a final task. In this work, we focus on improving learning for
such hierarchical models and demonstrate our method on the task of speaker
trait prediction. Speaker trait prediction aims to computationally identify
which personality traits a speaker might be perceived to have, and has been of
great interest to both the Artificial Intelligence and Social Science
communities. Persuasiveness prediction in particular has been of interest, as
persuasive speakers have a large amount of influence on our thoughts, opinions
and beliefs. In this work, we examine how leveraging the relationship between
related speaker traits in a hierarchical structure can help improve our ability
to predict how persuasive a speaker is. We present a novel algorithm that
allows us to backpropagate through this hierarchy. This hierarchical model
achieves a 25% relative error reduction in classification accuracy over current
state-of-the art methods on the publicly available POM dataset.
TimeNet: Pre-trained deep recurrent neural network for time series classification
Authors: Pankaj Malhotra, Vishnu Tv, Lovekesh Vig, Puneet Agarwal, Gautam Shroff
Date: 2017-06-23 00:00:00
Tasks: Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

Inspired by the tremendous success of deep Convolutional Neural Networks as
generic feature extractors for images, we propose TimeNet: a deep recurrent
neural network (RNN) trained on diverse time series in an unsupervised manner
using sequence to sequence (seq2seq) models to extract features from time
series. Rather than relying on data from the problem domain, TimeNet attempts
to generalize time series representation across domains by ingesting time
series from several domains simultaneously. Once trained, TimeNet can be used
as a generic off-the-shelf feature extractor for time series. The
representations or embeddings given by a pre-trained TimeNet are found to be
useful for time series classification (TSC). For several publicly available
datasets from UCR TSC Archive and an industrial telematics sensor data from
vehicles, we observe that a classifier learned over the TimeNet embeddings
yields significantly better performance compared to (i) a classifier learned
over the embeddings given by a domain-specific RNN, as well as (ii) a nearest
neighbor classifier based on Dynamic Time Warping.
Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery
Authors: Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies
Date: 2017-06-23 00:00:00
Tasks: compressed sensing, Magnetic Resonance Fingerprinting, Quantitative MRI

We adopt data structure in the form of cover trees and iteratively apply
approximate nearest neighbour (ANN) searches for fast compressed sensing
reconstruction of signals living on discrete smooth manifolds. Levering on the
recent stability results for the inexact Iterative Projected Gradient (IPG)
algorithm and by using the cover tree's ANN searches, we decrease the
projection cost of the IPG algorithm to be logarithmically growing with data
population for low dimensional smooth manifolds. We apply our results to
quantitative MRI compressed sensing and in particular within the Magnetic
Resonance Fingerprinting (MRF) framework. For a similar (or sometimes better)
reconstruction accuracy, we report 2-3 orders of magnitude reduction in
computations compared to the standard iterative method which uses brute-force
searches.
Exact tensor completion with sum-of-squares
Authors: Aaron Potechin, David Steurer
Date: 2017-02-21 00:00:00
Tasks: Matrix Completion

We obtain the first polynomial-time algorithm for exact tensor completion
that improves over the bound implied by reduction to matrix completion. The
algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal
components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed
entries of the tensor. This bound improves over the previous best one of
$r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound
also matches the best known results for the easier problem of approximate
tensor completion (Barak & Moitra, 2015).
  Our algorithm and analysis extends seminal results for exact matrix
completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares
method. The main technical challenge is to show that a small number of randomly
chosen monomials are enough to construct a degree-3 polynomial with precisely
planted orthogonal global optima over the sphere and that this fact can be
certified within the sum-of-squares proof system.
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia
Authors: Ismail Rusli
Date: 2017-06-23 00:00:00
Tasks: Language Modeling, Language Modelling

Smoothing is one technique to overcome data sparsity in statistical language
model. Although in its mathematical definition there is no explicit dependency
upon specific natural language, different natures of natural languages result
in different effects of smoothing techniques. This is true for Russian language
as shown by Whittaker (1998). In this paper, We compared Modified Kneser-Ney
and Witten-Bell smoothing techniques in statistical language model of Bahasa
Indonesia. We used train sets of totally 22M words that we extracted from
Indonesian version of Wikipedia. As far as we know, this is the largest train
set used to build statistical language model for Bahasa Indonesia. The
experiments with 3-gram, 5-gram, and 7-gram showed that Modified Kneser-Ney
consistently outperforms Witten-Bell smoothing technique in term of perplexity
values. It is interesting to note that our experiments showed 5-gram model for
Modified Kneser-Ney smoothing technique outperforms that of 7-gram. Meanwhile,
Witten-Bell smoothing is consistently improving over the increase of n-gram
order.
Query Complexity of Clustering with Side Information
Authors: Arya Mazumdar, Barna Saha
Date: 2017-06-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering, Community Detection, Stochastic Block Model

Suppose, we are given a set of $n$ elements to be clustered into $k$
(unknown) clusters, and an oracle/expert labeler that can interactively answer
pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same
cluster?". The goal is to recover the optimum clustering by asking the minimum
number of queries. In this paper, we initiate a rigorous theoretical study of
this basic problem of query complexity of interactive clustering, and provide
strong information theoretic lower bounds, as well as nearly matching upper
bounds. Most clustering problems come with a similarity matrix, which is used
by an automated process to cluster similar points together. Our main
contribution in this paper is to show the dramatic power of side information
aka similarity matrix on reducing the query complexity of clustering. A
similarity matrix represents noisy pair-wise relationships such as one computed
by some function on attributes of the elements. A natural noisy model is where
similarity values are drawn independently from some arbitrary probability
distribution $f_+$ when the underlying pair of elements belong to the same
cluster, and from some $f_-$ otherwise. We show that given such a similarity
matrix, the query complexity reduces drastically from $\Theta(nk)$ (no
similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$
denotes the squared Hellinger divergence. Moreover, this is also
information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are
all efficient, and parameter free, i.e., they work without any knowledge of $k,
f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our
work also reveals intriguing connection to popular community detection models
such as the {\em stochastic block model}, significantly generalizes them, and
opens up many venues for interesting future research.
The Second Order Linear Model
Authors: Ming Lin, Shuang Qiu, Bin Hong, Jieping Ye
Date: 2017-03-02 00:00:00
Tasks: model, Open-Ended Question Answering

We study a fundamental class of regression models called the second order
linear model (SLM). The SLM extends the linear model to high order functional
space and has attracted considerable research interest recently. Yet how to
efficiently learn the SLM under full generality using nonconvex solver still
remains an open question due to several fundamental limitations of the
conventional gradient descent learning framework. In this study, we try to
attack this problem from a gradient-free approach which we call the
moment-estimation-sequence (MES) method. We show that the conventional gradient
descent heuristic is biased by the skewness of the distribution therefore is no
longer the best practice of learning the SLM. Based on the MES framework, we
design a nonconvex alternating iteration process to train a $d$-dimension
rank-$k$ SLM within $O(kd)$ memory and one-pass of the dataset. The proposed
method converges globally and linearly, achieves $\epsilon$ recovery error
after retrieving $O[k^{2}d\cdot\mathrm{polylog}(kd/\epsilon)]$ samples.
Furthermore, our theoretical analysis reveals that not all SLMs can be learned
on every sub-gaussian distribution. When the instances are sampled from a
so-called $\tau$-MIP distribution, the SLM can be learned by $O(p/\tau^{2})$
samples where $p$ and $\tau$ are positive constants depending on the skewness
and kurtosis of the distribution. For non-MIP distribution, an addition
diagonal-free oracle is necessary and sufficient to guarantee the learnability
of the SLM. Numerical simulations verify the sharpness of our bounds on the
sampling complexity and the linear convergence rate of our algorithm.
The NOESIS Network-Oriented Exploration, Simulation, and Induction System
Authors: Víctor Martínez, Fernando Berzal, Juan-Carlos Cubero
Date: 2016-11-15 00:00:00
Tasks: Community Detection, Link Prediction

Network data mining has become an important area of study due to the large
number of problems it can be applied to. This paper presents NOESIS, an open
source framework for network data mining that provides a large collection of
network analysis techniques, including the analysis of network structural
properties, community detection methods, link scoring, and link prediction, as
well as network visualization algorithms. It also features a complete
stand-alone graphical user interface that facilitates the use of all these
techniques. The NOESIS framework has been designed using solid object-oriented
design principles and structured parallel programming. As a lightweight library
with minimal external dependencies and a permissive software license, NOESIS
can be incorporated into other software projects. Released under a BSD license,
it is available from http://noesis.ikor.org.
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
Authors: Kaidong Wang, Yao Wang, Qian Zhao, Deyu Meng, Zongben Xu
Date: 2017-06-20 00:00:00

It is known that Boosting can be interpreted as a gradient descent technique
to minimize an underlying loss function. Specifically, the underlying loss
being minimized by the traditional AdaBoost is the exponential loss, which is
proved to be very sensitive to random noise/outliers. Therefore, several
Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to
improve the robustness of AdaBoost by replacing the exponential loss with some
designed robust loss functions. In this work, we present a new way to robustify
AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning
(SPL) into Boosting framework. Specifically, we design a new robust Boosting
algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented
by slightly modifying off-the-shelf Boosting packages. Extensive experiments
and a theoretical characterization are also carried out to illustrate the
merits of the proposed SPLBoost.
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
Authors: Mahdyar Ravanbakhsh, Enver Sangineto, Moin Nabi, Nicu Sebe
Date: 2017-06-23 00:00:00
Tasks: Anomaly Detection, Event Detection

Abnormal crowd behaviour detection attracts a large interest due to its
importance in video surveillance scenarios. However, the ambiguity and the lack
of sufficient abnormal ground truth data makes end-to-end training of large
deep networks hard in this domain. In this paper we propose to use Generative
Adversarial Nets (GANs), which are trained to generate only the normal
distribution of the data. During the adversarial GAN training, a discriminator
(D) is used as a supervisor for the generator network (G) and vice versa. At
testing time we use D to solve our discriminative task (abnormality detection),
where D has been trained without the need of manually-annotated abnormal data.
Moreover, in order to prevent G learn a trivial identity function, we use a
cross-channel approach, forcing G to transform raw-pixel data in motion
information and vice versa. The quantitative results on standard benchmarks
show that our method outperforms previous state-of-the-art methods in both the
frame-level and the pixel-level evaluation.
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction
Authors: Maham Jahangir, Hammad Afzal, Mehreen Ahmed, Khawar Khurshid, Raheel Nawaz
Date: 2017-06-23 00:00:00
Tasks: Diabetes Prediction, Disease Prediction, Outlier Detection, Prediction

With advanced data analytical techniques, efforts for more accurate decision
support systems for disease prediction are on rise. Surveys by World Health
Organization (WHO) indicate a great increase in number of diabetic patients and
related deaths each year. Early diagnosis of diabetes is a major concern among
researchers and practitioners. The paper presents an application of
\textit{Automatic Multilayer Perceptron }which\textit{ }is combined with an
outlier detection method \textit{Enhanced Class Outlier Detection using
distance based algorithm }to create a prediction framework named as Enhanced
Class Outlier with Automatic Multi layer Perceptron (ECO-AMLP). A series of
experiments are performed on publicly available Pima Indian Diabetes Dataset to
compare ECO-AMLP with other individual classifiers as well as ensemble based
methods. The outlier technique used in our framework gave better results as
compared to other pre-processing and classification techniques. Finally, the
results are compared with other state-of-the-art methods reported in literature
for diabetes prediction on PIDD and achieved accuracy of 88.7\% bests all other
reported studies.
Computer-aided implant design for the restoration of cranial defects
Authors: Xiaojun Chen, Lu Xu, Xing Li, Jan Egger
Date: 2017-06-23 00:00:00

Patient-specific cranial implants are important and necessary in the surgery
of cranial defect restoration. However, traditional methods of manual design of
cranial implants are complicated and time-consuming. Our purpose is to develop
a novel software named EasyCrania to design the cranial implants conveniently
and efficiently. The process can be divided into five steps, which are
mirroring model, clipping surface, surface fitting, the generation of the
initial implant and the generation of the final implant. The main concept of
our method is to use the geometry information of the mirrored model as the base
to generate the final implant. The comparative studies demonstrated that the
EasyCrania can improve the efficiency of cranial implant design significantly.
And, the intra- and inter-rater reliability of the software were stable, which
were 87.07+/-1.6% and 87.73+/-1.4% respectively.
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
Authors: Wenshuo Wang, Chang Liu, Ding Zhao
Date: 2017-06-23 00:00:00
Tasks: Density Estimation

Big data has shown its uniquely powerful ability to reveal, model, and
understand driver behaviors. The amount of data affects the experiment cost and
conclusions in the analysis. Insufficient data may lead to inaccurate models
while excessive data waste resources. For projects that cost millions of
dollars, it is critical to determine the right amount of data needed. However,
how to decide the appropriate amount has not been fully studied in the realm of
driver behaviors. This paper systematically investigates this issue to estimate
how much naturalistic driving data (NDD) is needed for understanding driver
behaviors from a statistical point of view. A general assessment method is
proposed using a Gaussian kernel density estimation to catch the underlying
characteristics of driver behaviors. We then apply the Kullback-Liebler
divergence method to measure the similarity between density functions with
differing amounts of NDD. A max-minimum approach is used to compute the
appropriate amount of NDD. To validate our proposed method, we investigated the
car-following case using NDD collected from the University of Michigan Safety
Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical
perspective, the proposed approach can provide an appropriate amount of NDD
capable of capturing most features of the normal car-following behavior, which
is consistent with the experiment settings in many literatures.
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
Authors: Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang
Date: 2016-07-24 00:00:00
Proceeding: ICML 2017 8

Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and ultra-high dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the inactive features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in the computational cost without sacrificing the accuracy. Moreover, we show that our method can be extended to multi-class sparse support vector machines. To the best of our knowledge, the proposed method is the \emph{first} \emph{static} feature and sample reduction method for sparse SVMs and multi-class sparse SVMs. Experiments on both synthetic and real data sets demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.
Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
Authors: Thanuja Dharmasiri, Andrew Spek, Tom Drummond
Date: 2017-06-23 00:00:00

Understanding the 3D structure of a scene is of vital importance, when it
comes to developing fully autonomous robots. To this end, we present a novel
deep learning based framework that estimates depth, surface normals and surface
curvature by only using a single RGB image. To the best of our knowledge this
is the first work to estimate surface curvature from colour using a machine
learning approach. Additionally, we demonstrate that by tuning the network to
infer well designed features, such as surface curvature, we can achieve
improved performance at estimating depth and normals.This indicates that
network guidance is still a useful aspect of designing and training a neural
network. We run extensive experiments where the network is trained to infer
different tasks while the model capacity is kept constant resulting in
different feature maps based on the tasks at hand. We outperform the previous
state-of-the-art benchmarks which jointly estimate depths and surface normals
while predicting surface curvature in parallel.
4d isip: 4d implicit surface interest point detection
Authors: Shirui Li, Alper Yilmaz, Changlin Xiao, Hua Li
Date: 2017-05-10 00:00:00
Tasks: Interest Point Detection, Object

In this paper, we propose a new method to detect 4D spatiotemporal interest
points though an implicit surface, we refer to as the 4D-ISIP. We use a 3D
volume which has a truncated signed distance function(TSDF) for every voxel to
represent our 3D object model. The TSDF represents the distance between the
spatial points and object surface points which is an implicit surface
representation. Our novelty is to detect the points where the local
neighborhood has significant variations along both spatial and temporal
directions. We established a system to acquire 3D human motion dataset using
only one Kinect. Experimental results show that our method can detect 4D-ISIP
for different human actions.
Cross-validation failure: small sample sizes lead to large error bars
Authors: Gaël Varoquaux
Date: 2017-06-23 00:00:00

Predictive models ground many state-of-the-art developments in statistical
brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers.
The principled approach to establish their validity and usefulness is
cross-validation, testing prediction on unseen data. Here, I would like to
raise awareness on error bars of cross-validation, which are often
underestimated. Simple experiments show that sample sizes of many neuroimaging
studies inherently lead to large error bars, eg $\pm$10% for 100 samples. The
standard error across folds strongly underestimates them. These large error
bars compromise the reliability of conclusions drawn with predictive models,
such as biomarkers or methods developments where, unlike with cognitive
neuroimaging MVPA approaches, more samples cannot be acquired by repeating the
experiment across many subjects. Solutions to increase sample size must be
investigated, tackling possible increases in heterogeneity of the data.