pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
Authors: Sigurd Løkse, Filippo Maria Bianchi, Arnt-Børre Salberg, Robert Jenssen
Date: 2017-02-23 00:00:00
Tasks: Clustering, Missing Values

In this paper, we propose PCKID, a novel, robust, kernel function for
spectral clustering, specifically designed to handle incomplete data. By
combining posterior distributions of Gaussian Mixture Models for incomplete
data on different scales, we are able to learn a kernel for incomplete data
that does not depend on any critical hyperparameters, unlike the commonly used
RBF kernel. To evaluate our method, we perform experiments on two real
datasets. PCKID outperforms the baseline methods for all fractions of missing
values and in some cases outperforms the baseline methods with up to 25
percentage points.
Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models
Authors: David Malmgren-Hansen, Allan Aasbjerg Nielsen, Rasmus Engholm
Date: 2017-02-23 00:00:00
Tasks: Clustering, Transfer Learning

Convolutional Neural Networks (Convnets) have achieved good results in a
range of computer vision tasks the recent years. Though given a lot of
attention, visualizing the learned representations to interpret Convnets, still
remains a challenging task. The high dimensionality of internal representations
and the high abstractions of deep layers are the main challenges when
visualizing Convnet functionality. We present in this paper a technique based
on clustering internal Convnet representations with a Dirichlet Process
Gaussian Mixture Model, for visualization of learned representations in
Convnets. Our method copes with the high dimensionality of a Convnet by
clustering representations across all nodes of each layer. We will discuss how
this application is useful when considering transfer learning, i.e.\
transferring a model trained on one dataset to solve a task on a different one.
A DIKW Paradigm to Cognitive Engineering
Authors: Amit Kumar Mishra
Date: 2017-02-23 00:00:00

Though the word cognitive has a wide range of meanings we define cognitive
engineering as learning from brain to bolster engineering solutions. However,
giving an achievable framework to the process towards this has been a difficult
task. In this work we take the classic data information knowledge wisdom (DIKW)
framework to set some achievable goals and sub-goals towards cognitive
engineering. A layered framework like DIKW aligns nicely with the layered
structure of pre-frontal cortex. And breaking the task into sub-tasks based on
the layers also makes it easier to start developmental endeavours towards
achieving the final goal of a brain-inspired system.
A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging
Authors: Quercus Hernandez, Diego Gutierrez, Adrian Jarabo
Date: 2017-02-23 00:00:00

Single-Photon Avalanche Diodes (SPAD) are affordable photodetectors, capable
to collect extremely fast low-energy events, due to their single-photon
sensibility. This makes them very suitable for time-of-flight-based range
imaging systems, allowing to reduce costs and power requirements, without
sacrifizing much temporal resolution. In this work we describe a computational
model to simulate the behaviour of SPAD sensors, aiming to provide a realistic
camera model for time-resolved light transport simulation, with applications on
prototyping new reconstructions techniques based on SPAD time-of-flight data.
Our model accounts for the major effects of the sensor on the incoming signal.
We compare our model against real-world measurements, and apply it to a variety
of scenarios, including complex multiply-scattered light transport.
Splitting method for spatio-temporal search efforts planning
Authors: Chouchane Mathieu, Paris Sébastien, Le Gland François, Ouladsine Mustapha
Date: 2011-05-17 00:00:00
Tasks: Stochastic Optimization

This article deals with the spatio-temporal sensors deployment in order to
maximize detection probability of an intelligent and randomly moving target in
an area under surveillance. Our work is based on the rare events simulation
framework. More precisely, we derive a novel stochastic optimization algorithm
based on the generalized splitting method. This new approach offers promising
results without any state-space discretization and can handle various types of
constraints.
Riemannian Tensor Completion with Side Information
Authors: Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu
Date: 2016-11-12 00:00:00
Tasks: Riemannian optimization

By restricting the iterate on a nonlinear manifold, the recently proposed
Riemannian optimization methods prove to be both efficient and effective in low
rank tensor completion problems. However, existing methods fail to exploit the
easily accessible side information, due to their format mismatch. Consequently,
there is still room for improvement in such methods. To fill the gap, in this
paper, a novel Riemannian model is proposed to organically integrate the
original model and the side information by overcoming their inconsistency. For
this particular model, an efficient Riemannian conjugate gradient descent
solver is devised based on a new metric that captures the curvature of the
objective.Numerical experiments suggest that our solver is more accurate than
the state-of-the-art without compromising the efficiency.
Automatic Representation for Lifetime Value Recommender Systems
Authors: Assaf Hallak, Yishay Mansour, Elad Yom-Tov
Date: 2017-02-23 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning, Reinforcement Learning (RL)

Many modern commercial sites employ recommender systems to propose relevant
content to users. While most systems are focused on maximizing the immediate
gain (clicks, purchases or ratings), a better notion of success would be the
lifetime value (LTV) of the user-system interaction. The LTV approach considers
the future implications of the item recommendation, and seeks to maximize the
cumulative gain over time. The Reinforcement Learning (RL) framework is the
standard formulation for optimizing cumulative successes over time. However, RL
is rarely used in practice due to its associated representation, optimization
and validation techniques which can be complex. In this paper we propose a new
architecture for combining RL with recommendation systems which obviates the
need for hand-tuned features, thus automating the state-space representation
construction process. We analyze the practical difficulties in this formulation
and test our solutions on batch off-line real-world recommendation data.
Consistent On-Line Off-Policy Evaluation
Authors: Assaf Hallak, Shie Mannor
Date: 2017-02-23 00:00:00
Proceeding: ICML 2017 8
Tasks: Off-policy evaluation

The problem of on-line off-policy evaluation (OPE) has been actively studied
in the last decade due to its importance both as a stand-alone problem and as a
module in a policy improvement scheme. However, most Temporal Difference (TD)
based solutions ignore the discrepancy between the stationary distribution of
the behavior and target policies and its effect on the convergence limit when
function approximation is applied. In this paper we propose the Consistent
Off-Policy Temporal Difference (COP-TD($\lambda$, $\beta$)) algorithm that
addresses this issue and reduces this bias at some computational expense. We
show that COP-TD($\lambda$, $\beta$) can be designed to converge to the same
value that would have been obtained by using on-policy TD($\lambda$) with the
target policy. Subsequently, the proposed scheme leads to a related and
promising heuristic we call log-COP-TD($\lambda$, $\beta$). Both algorithms
have favorable empirical results to the current state of the art on-line OPE
algorithms. Finally, our formulation sheds some new light on the recently
proposed Emphatic TD learning.
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations
Authors: Jarvan Law, Hankz Hankui Zhuo, Junhua He, Erhu Rong
Date: 2017-02-23 00:00:00
Tasks: Topic Models, Word Embeddings

Topic models have been widely used in discovering latent topics which are
shared across documents in text mining. Vector representations, word embeddings
and topic embeddings, map words and topics into a low-dimensional and dense
real-value vector space, which have obtained high performance in NLP tasks.
However, most of the existing models assume the result trained by one of them
are perfect correct and used as prior knowledge for improving the other model.
Some other models use the information trained from external large corpus to
help improving smaller corpus. In this paper, we aim to build such an algorithm
framework that makes topic models and vector representations mutually improve
each other within the same corpus. An EM-style algorithm framework is employed
to iteratively optimize both topic model and vector representations.
Experimental results show that our model outperforms state-of-art methods on
various NLP tasks.
Trained Ternary Quantization
Authors: Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally
Date: 2016-12-04 00:00:00
Tasks: Quantization

Deep neural networks are widely used in machine learning applications.
However, the deployment of large neural networks models can be difficult to
deploy on mobile devices with limited power budgets. To solve this problem, we
propose Trained Ternary Quantization (TTQ), a method that can reduce the
precision of weights in neural networks to ternary values. This method has very
little accuracy degradation and can even improve the accuracy of some models
(32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet
model is trained from scratch, which means it's as easy as to train normal full
precision model. We highlight our trained quantization method that can learn
both ternary values and ternary assignment. During inference, only ternary
values (2-bit weights) and scaling factors are needed, therefore our models are
nearly 16x smaller than full-precision models. Our ternary models can also be
viewed as sparse binary weight networks, which can potentially be accelerated
with custom circuit. Experiments on CIFAR-10 show that the ternary models
obtained by trained quantization method outperform full-precision models of
ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model
outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and
outperforms previous ternary models by 3%.
Discriminating Traces with Time
Authors: Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi
Date: 2017-02-23 00:00:00

What properties about the internals of a program explain the possible
differences in its overall running time for different inputs? In this paper, we
propose a formal framework for considering this question we dub trace-set
discrimination. We show that even though the algorithmic problem of computing
maximum likelihood discriminants is NP-hard, approaches based on integer linear
programming (ILP) and decision tree learning can be useful in zeroing-in on the
program internals. On a set of Java benchmarks, we find that
compactly-represented decision trees scalably discriminate with high
accuracy---more scalably than maximum likelihood discriminants and with
comparable accuracy. We demonstrate on three larger case studies how
decision-tree discriminants produced by our tool are useful for debugging
timing side-channel vulnerabilities (i.e., where a malicious observer infers
secrets simply from passively watching execution times) and availability
vulnerabilities.
Field-aware Factorization Machines in a Real-world Online Advertising System
Authors: Yuchin Juan, Damien Lefortier, Olivier Chapelle
Date: 2017-01-15 00:00:00

Predicting user response is one of the core machine learning tasks in
computational advertising. Field-aware Factorization Machines (FFM) have
recently been established as a state-of-the-art method for that problem and in
particular won two Kaggle challenges. This paper presents some results from
implementing this method in a production system that predicts click-through and
conversion rates for display advertising and shows that this method it is not
only effective to win challenges but is also valuable in a real-world
prediction system. We also discuss some specific challenges and solutions to
reduce the training time, namely the use of an innovative seeding algorithm and
a distributed learning mechanism.
A Neural Attention Model for Categorizing Patient Safety Events
Authors: Arman Cohan, Allan Fong, Nazli Goharian, Raj Ratwani
Date: 2017-02-23 00:00:00

Medical errors are leading causes of death in the US and as such, prevention
of these errors is paramount to promoting health care. Patient Safety Event
reports are narratives describing potential adverse events to the patients and
are important in identifying and preventing medical errors. We present a neural
network architecture for identifying the type of safety events which is the
first step in understanding these narratives. Our proposed model is based on a
soft neural attention model to improve the effectiveness of encoding long
sequences. Empirical results on two large-scale real-world datasets of patient
safety reports demonstrate the effectiveness of our method with significant
improvements over existing methods.
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Authors: Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu
Date: 2017-02-23 00:00:00
Tasks: Topic Models, Variational Inference

Nested Chinese Restaurant Process (nCRP) topic models are powerful
nonparametric Bayesian methods to extract a topic hierarchy from a given text
corpus, where the hierarchical structure is automatically determined by the
data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of
nCRP topic models. However, hLDA has only been evaluated at small scale,
because the existing collapsed Gibbs sampling and instantiated weight
variational inference algorithms either are not scalable or sacrifice inference
quality with mean-field assumptions. Moreover, an efficient distributed
implementation of the data structures, such as dynamically growing count
matrices and trees, is challenging.
  In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS)
algorithm, which combines the advantages of collapsed and instantiated weight
algorithms to achieve good scalability as well as high model quality. An
initialization strategy is presented to further improve the model quality.
Finally, we propose an efficient distributed implementation of PCGS through
vectorization, pre-processing, and a careful design of the concurrent data
structures and communication strategy.
  Empirical studies show that our algorithm is 111 times more efficient than
the previous open-source implementation for hLDA, with comparable or even
better model quality. Our distributed implementation can extract 1,722 topics
from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders
of magnitude larger than the previous largest corpus, with 50 machines in 7
hours.
Pronunciation recognition of English phonemes /\textipa{@}/, /æ/, /\textipa{A}:/ and /\textipa{2}/ using Formants and Mel Frequency Cepstral Coefficients
Authors: Keith Y. Patarroyo, Vladimir Vargas-Calderón
Date: 2017-02-23 00:00:00
Tasks: speech-recognition, Speech Recognition

The Vocal Joystick Vowel Corpus, by Washington University, was used to study
monophthongs pronounced by native English speakers. The objective of this study
was to quantitatively measure the extent at which speech recognition methods
can distinguish between similar sounding vowels. In particular, the phonemes
/\textipa{@}/, /{\ae}/, /\textipa{A}:/ and /\textipa{2}/ were analysed. 748
sound files from the corpus were used and subjected to Linear Predictive Coding
(LPC) to compute their formants, and to Mel Frequency Cepstral Coefficients
(MFCC) algorithm, to compute the cepstral coefficients. A Decision Tree
Classifier was used to build a predictive model that learnt the patterns of the
two first formants measured in the data set, as well as the patterns of the 13
cepstral coefficients. An accuracy of 70\% was achieved using formants for the
mentioned phonemes. For the MFCC analysis an accuracy of 52 \% was achieved and
an accuracy of 71\% when /\textipa{@}/ was ignored. The results obtained show
that the studied algorithms are far from mimicking the ability of
distinguishing subtle differences in sounds like human hearing does.
A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data
Authors: Pierre Lafaye de Micheaux, Benoit Liquet, Matthew Sutton
Date: 2017-02-23 00:00:00
Tasks: Mathematical Proofs

Partial Least Squares (PLS) methods have been heavily exploited to analyse
the association between two blocs of data. These powerful approaches can be
applied to data sets where the number of variables is greater than the number
of observations and in presence of high collinearity between variables.
Different sparse versions of PLS have been developed to integrate multiple data
sets while simultaneously selecting the contributing variables. Sparse
modelling is a key factor in obtaining better estimators and identifying
associations between multiple data sets. The cornerstone of the sparsity
version of PLS methods is the link between the SVD of a matrix (constructed
from deflated versions of the original matrices of data) and least squares
minimisation in linear regression. We present here an accurate description of
the most popular PLS methods, alongside their mathematical proofs. A unified
algorithm is proposed to perform all four types of PLS including their
regularised versions. Various approaches to decrease the computation time are
offered, and we show how the whole procedure can be scalable to big data sets.
Robust and fully automated segmentation of mandible from CT scans
Authors: Neslisah Torosdagli, Denise K. Liberton, Payal Verma, Murat Sincan Janice Lee, Sumanta Pattanaik, Ulas Bagci
Date: 2017-02-23 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Segmentation, Semantic Segmentation

Mandible bone segmentation from computed tomography (CT) scans is challenging
due to mandible's structural irregularities, complex shape patterns, and lack
of contrast in joints. Furthermore, connections of teeth to mandible and
mandible to remaining parts of the skull make it extremely difficult to
identify mandible boundary automatically. This study addresses these challenges
by proposing a novel framework where we define the segmentation as two
complementary tasks: recognition and delineation. For recognition, we use
random forest regression to localize mandible in 3D. For delineation, we
propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation
algorithm, operating on the recognized mandible sub-volume. Despite heavy CT
artifacts and dental fillings, consisting half of the CT image data in our
experiments, we have achieved highly accurate detection and delineation
results. Specifically, detection accuracy more than 96% (measured by union of
intersection (UoI)), the delineation accuracy of 91% (measured by dice
similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff
Distance) were found.
Solving DCOPs with Distributed Large Neighborhood Search
Authors: Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli, William Yeoh, Roie Zivan
Date: 2017-02-22 00:00:00

The field of Distributed Constraint Optimization has gained momentum in
recent years, thanks to its ability to address various applications related to
multi-agent cooperation. Nevertheless, solving Distributed Constraint
Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale,
complex applications, incomplete DCOP algorithms are necessary. Current
incomplete DCOP algorithms suffer of one or more of the following limitations:
they (a) find local minima without providing quality guarantees; (b) provide
loose quality assessment; or (c) are unable to benefit from the structure of
the problem, such as domain-dependent knowledge and hard constraints.
Therefore, capitalizing on strategies from the centralized constraint solving
community, we propose a Distributed Large Neighborhood Search (D-LNS) framework
to solve DCOPs. The proposed framework (with its novel repair phase) provides
guarantees on solution quality, refining upper and lower bounds during the
iterative process, and can exploit domain-dependent structures. Our
experimental results show that D-LNS outperforms other incomplete DCOP
algorithms on both structured and unstructured problem instances.
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
Authors: Wanli Ouyang, Ku Wang, Xin Zhu, Xiaogang Wang
Date: 2017-02-23 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

Cascade is a widely used approach that rejects obvious negative samples at
early stages for learning better classifier and faster inference. This paper
presents chained cascade network (CC-Net). In this CC-Net, the cascaded
classifier at a stage is aided by the classification scores in previous stages.
Feature chaining is further proposed so that the feature learning for the
current cascade stage uses the features in previous stages as the prior
information. The chained ConvNet features and classifiers of multiple stages
are jointly learned in an end-to-end network. In this way, features and
classifiers at latter stages handle more difficult samples with the help of
features and classifiers in previous stages. It yields consistent boost in
detection performance on benchmarks like PASCAL VOC 2007 and ImageNet. Combined
with better region proposal, CC-Net leads to state-of-the-art result of 81.1%
mAP on PASCAL VOC 2007.
Feature Generation for Robust Semantic Role Labeling
Authors: Travis Wolfe, Mark Dredze, Benjamin Van Durme
Date: 2017-02-22 00:00:00
Tasks: Semantic Role Labeling

Hand-engineered feature sets are a well understood method for creating robust
NLP models, but they require a lot of expertise and effort to create. In this
work we describe how to automatically generate rich feature sets from simple
units called featlets, requiring less engineering. Using information gain to
guide the generation process, we train models which rival the state of the art
on two standard Semantic Role Labeling datasets with almost no task or
linguistic insight.
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective
Authors: Ursula Challita, Li Dong, Walid Saad
Date: 2017-02-22 00:00:00
Tasks: channel selection, Fairness, Management

LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a
promising approach to overcome the wireless spectrum scarcity. However, to reap
the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi
deployments is required. In this paper, a novel deep learning approach is
proposed for modeling the resource allocation problem of LTE-LAA small base
stations (SBSs). The proposed approach enables multiple SBSs to proactively
perform dynamic channel selection, carrier aggregation, and fractional spectrum
access while guaranteeing fairness with existing WiFi networks and other
LTE-LAA operators. Adopting a proactive coexistence mechanism enables future
delay-tolerant LTE-LAA data demands to be served within a given prediction
window ahead of their actual arrival time thus avoiding the underutilization of
the unlicensed spectrum during off-peak hours while maximizing the total served
LTE-LAA traffic load. To this end, a noncooperative game model is formulated in
which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence
of future actions and thus achieving long-term equal weighted fairness with
WLAN and other LTE-LAA operators over a given time horizon. The proposed deep
learning algorithm is then shown to reach a mixed-strategy Nash equilibrium
(NE), when it converges. Simulation results using real data traces show that
the proposed scheme can yield up to 28% and 11% gains over a conventional
reactive approach and a proportional fair coexistence mechanism, respectively.
The results also show that the proposed framework prevents WiFi performance
degradation for a densely deployed LTE-LAA network.