pwc-archive/papers-with-abstracts huggingface dataset:
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Authors: Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, Yong Jae Lee
Date: 2017-05-25 00:00:00

Content popularity prediction has been extensively studied due to its
importance and interest for both users and hosts of social media sites like
Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly
focuses on modeling popularity using a single metric such as the total number
of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based
deep recurrent network that learns to recursively predict the entire diffusion
path of an image through a social network. By combining user social features
and image features, and encoding the diffusion path taken thus far with an
explicit memory cell, our model predicts the diffusion path of an image more
accurately compared to alternate baselines that either encode only image or
social features, or lack memory. By mapping individual users to user
prototypes, our model can generalize to new users not seen during training.
Finally, we demonstrate our model's capability of generating diffusion trees,
and show that the generated trees closely resemble ground-truth trees.
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Authors: Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah
Date: 2017-11-09 00:00:00
Tasks: CPU, GPU

There has been considerable recent activity applying deep convolutional
neural nets (CNNs) to data from particle physics experiments. Current
approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,
and for identifying objects or particular particle types. We explore approaches
that use the entire calorimeter, combined with track information, for directly
conducting physics analyses: i.e. classifying events as known-physics
background or new-physics signals.
  We use an existing RPV-Supersymmetry analysis as a case study and explore
CNNs on multi-channel, high-resolution sparse images: applied on GPU and
multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)
on the Cori supercomputer at NERSC.
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Authors: Arash Akbarinia, Raquel Gil Rodríguez, C. Alejandro Parraga
Date: 2017-11-29 00:00:00

Pooling is a ubiquitous operation in image processing algorithms that allows
for higher-level processes to collect relevant low-level features from a region
of interest. Currently, max-pooling is one of the most commonly used operators
in the computational literature. However, it can lack robustness to outliers
due to the fact that it relies merely on the peak of a function. Pooling
mechanisms are also present in the primate visual cortex where neurons of
higher cortical areas pool signals from lower ones. The receptive fields of
these neurons have been shown to vary according to the contrast by aggregating
signals over a larger region in the presence of low contrast stimuli. We
hypothesise that this contrast-variant-pooling mechanism can address some of
the shortcomings of max-pooling. We modelled this contrast variation through a
histogram clipping in which the percentage of pooled signal is inversely
proportional to the local contrast of an image. We tested our hypothesis by
applying it to the phenomenon of colour constancy where a number of popular
algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and
Double-Opponency). For each of these methods, we investigated the consequences
of replacing their original max-pooling by the proposed
contrast-variant-pooling. Our experiments on three colour constancy benchmark
datasets suggest that previous results can significantly improve by adopting a
contrast-variant-pooling mechanism.
The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks
Authors: Ruthwik R. Junuthula, Maysam Haghdan, Kevin S. Xu, Vijay K. Devabhaktuni
Date: 2017-11-29 00:00:00
Tasks: Clustering, Stochastic Block Model, Variational Inference

We consider the problem of analyzing timestamped relational events between a
set of entities, such as messages between users of an on-line social network.
Such data are often analyzed using static or discrete-time network models,
which discard a significant amount of information by aggregating events over
time to form network snapshots. In this paper, we introduce a block point
process model (BPPM) for continuous-time event-based dynamic networks. The BPPM
is inspired by the well-known stochastic block model (SBM) for static networks.
We show that networks generated by the BPPM follow an SBM in the limit of a
growing number of nodes. We use this property to develop principled and
efficient local search and variational inference procedures initialized by
regularized spectral clustering. We fit BPPMs with exponential Hawkes processes
to analyze several real network data sets, including a Facebook wall post
network with over 3,500 nodes and 130,000 events.
Saccade Sequence Prediction: Beyond Static Saliency Maps
Authors: Calden Wloka, Iuliia Kotseruba, John K. Tsotsos
Date: 2017-11-29 00:00:00
Tasks: Prediction

Visual attention is a field with a considerable history, with eye movement
control and prediction forming an important subfield. Fixation modeling in the
past decades has been largely dominated computationally by a number of highly
influential bottom-up saliency models, such as the Itti-Koch-Niebur model. The
accuracy of such models has dramatically increased recently due to deep
learning. However, on static images the emphasis of these models has largely
been based on non-ordered prediction of fixations through a saliency map. Very
few implemented models can generate temporally ordered human-like sequences of
saccades beyond an initial fixation point. Towards addressing these
shortcomings we present STAR-FC, a novel multi-saccade generator based on a
central/peripheral integration of deep learning-based saliency and lower-level
feature-based saliency. We have evaluated our model using the CAT2000 database,
successfully predicting human patterns of fixation with equivalent accuracy and
quality compared to what can be achieved by using one human sequence to predict
another. This is a significant improvement over fixation sequences predicted by
state-of-the-art saliency algorithms.
Now Playing: Continuous low-power music recognition
Authors: Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirović
Date: 2017-11-29 00:00:00

Existing music recognition applications require a connection to a server that
performs the actual recognition. In this paper we present a low-power music
recognizer that runs entirely on a mobile device and automatically recognizes
music without user interaction. To reduce battery consumption, a small music
detector runs continuously on the mobile device's DSP chip and wakes up the
main application processor only when it is confident that music is present.
Once woken, the recognizer on the application processor is provided with a few
seconds of audio which is fingerprinted and compared to the stored fingerprints
in the on-device fingerprint database of tens of thousands of songs. Our
presented system, Now Playing, has a daily battery usage of less than 1% on
average, respects user privacy by running entirely on-device and can passively
recognize a wide range of music.
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
Authors: Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
Date: 2017-11-07 00:00:00
Tasks: Attribute

Deep Neural Networks (DNNs) have emerged as the method of choice for solving
a wide range of machine learning tasks. The enormous computational demands
posed by DNNs have most commonly been addressed through the design of custom
accelerators. However, these accelerators are prohibitive in many design
scenarios (e.g., wearable devices and IoT sensors), due to stringent area/cost
constraints. Accelerating DNNs on these low-power systems, comprising of mainly
the general-purpose processor (GPP) cores, requires new approaches. We improve
the performance of DNNs on GPPs by exploiting a key attribute of DNNs, i.e.,
sparsity. We propose Sparsity aware Core Extensions (SparCE)- a set of
micro-architectural and ISA extensions that leverage sparsity and are minimally
intrusive and low-overhead. We dynamically detect zero operands and skip a set
of future instructions that use it. Our design ensures that the instructions to
be skipped are prevented from even being fetched, as squashing instructions
comes with a penalty. SparCE consists of 2 key micro-architectural
enhancements- a Sparsity Register File (SpRF) that tracks zero registers and a
Sparsity aware Skip Address (SASA) table that indicates instructions to be
skipped. When an instruction is fetched, SparCE dynamically pre-identifies
whether the following instruction(s) can be skipped and appropriately modifies
the program counter, thereby skipping the redundant instructions and improving
performance. We model SparCE using the gem5 architectural simulator, and
evaluate our approach on 6 image-recognition DNNs in the context of both
training and inference using the Caffe framework. On a scalar microprocessor,
SparCE achieves 19%-31% reduction in application-level. We also evaluate SparCE
on a 4-way SIMD ARMv8 processor using the OpenBLAS library, and demonstrate
that SparCE achieves 8%-15% reduction in the application-level execution time.
Automatic Generation of Constrained Furniture Layouts
Authors: Paul Henderson, Kartic Subr, Vittorio Ferrari
Date: 2017-11-29 00:00:00

Efficient authoring of vast virtual environments hinges on algorithms that
are able to automatically generate content while also being controllable. We
propose a method to automatically generate furniture layouts for indoor
environments. Our method is simple, efficient, human-interpretable and amenable
to a wide variety of constraints. We model the composition of rooms into
classes of objects and learn joint (co-occurrence) statistics from a database
of training layouts. We generate new layouts by performing a sequence of
conditional sampling steps, exploiting the statistics learned from the
database. The generated layouts are specified as 3D object models, along with
their positions and orientations. We show they are of equivalent perceived
quality to the training layouts, and compare favorably to a state-of-the-art
method. We incorporate constraints using a general mechanism -- rejection
sampling -- which provides great flexibility at the cost of extra computation.
We demonstrate the versatility of our method by applying a wide variety of
constraints relevant to real-world applications.
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling
Authors: Malinda Vania, Dawit Mureja, Deukhee Lee
Date: 2017-11-29 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Image Segmentation, Segmentation, Semantic Segmentation, Specificity

There has been a significant increase from 2010 to 2016 in the number of
people suffering from spine problems. The automatic image segmentation of the
spine obtained from a computed tomography (CT) image is important for
diagnosing spine conditions and for performing surgery with computer-assisted
surgery systems. The spine has a complex anatomy that consists of 33 vertebrae,
23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the
spinal surgeon is faced with the challenge of needing a robust algorithm to
segment and create a model of the spine. In this study, we developed an
automatic segmentation method to segment the spine, and we compared our
segmentation results with reference segmentations obtained by experts. We
developed a fully automatic approach for spine segmentation from CT based on a
hybrid method. This method combines the convolutional neural network (CNN) and
fully convolutional network (FCN), and utilizes class redundancy as a soft
constraint to greatly improve the segmentation results. The proposed method was
found to significantly enhance the accuracy of the segmentation results and the
system processing time. Our comparison was based on 12 measurements: the Dice
coefficient (94%), Jaccard index (93%), volumetric similarity (96%),
sensitivity (97%), specificity (99%), precision (over segmentation; 8.3 and
under segmentation 2.6), accuracy (99%), Matthews correlation coefficient
(0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and
global consistency error (0.02). We experimented with CT images from 32
patients, and the experimental results demonstrated the efficiency of the
proposed method.
Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting
Authors: Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Olivier Mestre
Date: 2017-11-29 00:00:00
Tasks: quantile regression

Rainfall ensemble forecasts have to be skillful for both low precipitation
and extreme events. We present statistical post-processing methods based on
Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric
extension for heavy-tailed distributions. Our goal is to improve ensemble
quality for all types of precipitation events, heavy-tailed included, subject
to a good overall performance. Our hybrid proposed methods are applied to daily
51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France
using the M{\'e}t{\'e}o-France ensemble prediction system called PEARP. They
provide calibrated pre-dictive distributions and compete favourably with
state-of-the-art methods like Analogs method or Ensemble Model Output
Statistics. In particular, hybrid forest-based procedures appear to bring an
added value to the forecast of heavy rainfall.
NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets
Authors: Soroush Saryazdi, Bahareh Nikpour, Hossein Nezamabadi-pour
Date: 2017-11-29 00:00:00
Tasks: Decision Making, General Classification

Learning from many real-world datasets is limited by a problem called the
class imbalance problem. A dataset is imbalanced when one class (the majority
class) has significantly more samples than the other class (the minority
class). Such datasets cause typical machine learning algorithms to perform
poorly on the classification task. To overcome this issue, this paper proposes
a new approach Neighbors Progressive Competition (NPC) for classification of
imbalanced datasets. Whilst the proposed algorithm is inspired by weighted
k-Nearest Neighbor (k-NN) algorithms, it has major differences from them.
Unlike k- NN, NPC does not limit its decision criteria to a preset number of
nearest neighbors. In contrast, NPC considers progressively more neighbors of
the query sample in its decision making until the sum of grades for one class
is much higher than the other classes. Furthermore, NPC uses a novel method for
grading the training samples to compensate for the imbalance issue. The grades
are calculated using both local and global information. In brief, the
contribution of this paper is an entirely new classifier for handling the
imbalance issue effectively without any manually-set parameters or any need for
expert knowledge. Experimental results compare the proposed approach with five
representative algorithms applied to fifteen imbalanced datasets and illustrate
this algorithms effectiveness.
Investigating how well contextual features are captured by bi-directional recurrent neural network models
Authors: Kushal Chawla, Sunil Kumar Sahu, Ashish Anand
Date: 2017-09-03 00:00:00
Proceeding: WS 2017 12
Tasks: Feature Engineering

Learning algorithms for natural language processing (NLP) tasks traditionally
rely on manually defined relevant contextual features. On the other hand,
neural network models using an only distributional representation of words have
been successfully applied for several NLP tasks. Such models learn features
automatically and avoid explicit feature engineering. Across several domains,
neural models become a natural choice specifically when limited characteristics
of data are known. However, this flexibility comes at the cost of
interpretability. In this paper, we define three different methods to
investigate ability of bi-directional recurrent neural networks (RNNs) in
capturing contextual features. In particular, we analyze RNNs for sequence
tagging tasks. We perform a comprehensive analysis on general as well as
biomedical domain datasets. Our experiments focus on important contextual words
as features, which can easily be extended to analyze various other feature
types. We also investigate positional effects of context words and show how the
developed methods can be used for error analysis.
Particle Optimization in Stochastic Gradient MCMC
Authors: Changyou Chen, Ruiyi Zhang
Date: 2017-11-29 00:00:00

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has been increasingly
popular in Bayesian learning due to its ability to deal with large data. A
standard SG-MCMC algorithm simulates samples from a discretized-time Markov
chain to approximate a target distribution. However, the samples are typically
highly correlated due to the sequential generation process, an undesired
property in SG-MCMC. In contrary, Stein variational gradient descent (SVGD)
directly optimizes a set of particles, and it is able to approximate a target
distribution with much fewer samples. In this paper, we propose a novel method
to directly optimize particles (or samples) in SG-MCMC from scratch.
Specifically, we propose efficient methods to solve the corresponding
Fokker-Planck equation on the space of probability distributions, whose
solution (i.e., a distribution) is approximated by particles. Through our
framework, we are able to show connections of SG-MCMC to SVGD, as well as the
seemly unrelated generative-adversarial-net framework. Under certain
relaxations, particle optimization in SG-MCMC can be interpreted as an
extension of standard SVGD with momentum.
Causality Refined Diagnostic Prediction
Authors: Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström
Date: 2017-11-29 00:00:00
Tasks: Causal Identification, Decision Making, Diagnostic, Prediction

Applying machine learning in the health care domain has shown promising
results in recent years. Interpretable outputs from learning algorithms are
desirable for decision making by health care personnel. In this work, we
explore the possibility of utilizing causal relationships to refine diagnostic
prediction. We focus on the task of diagnostic prediction using discomfort
drawings, and explore two ways to employ causal identification to improve the
diagnostic results. Firstly, we use causal identification to infer the causal
relationships among diagnostic labels which, by itself, provides interpretable
results to aid the decision making and training of health care personnel.
Secondly, we suggest a post-processing approach where the inferred causal
relationships are used to refine the prediction accuracy of a multi-view
probabilistic model. Experimental results show firstly that causal
identification is capable of detecting the causal relationships among
diagnostic labels correctly, and secondly that there is potential for improving
pain diagnostics prediction accuracy using the causal relationships.
Learning Spatio-temporal Features with Partial Expression Sequences for on-the-Fly Prediction
Authors: Wissam J. Baddar, Yong Man Ro
Date: 2017-11-29 00:00:00
Tasks: Prediction

Spatio-temporal feature encoding is essential for encoding facial expression
dynamics in video sequences. At test time, most spatio-temporal encoding
methods assume that a temporally segmented sequence is fed to a learned model,
which could require the prediction to wait until the full sequence is available
to an auxiliary task that performs the temporal segmentation. This causes a
delay in predicting the expression. In an interactive setting, such as
affective interactive agents, such delay in the prediction could not be
tolerated. Therefore, training a model that can accurately predict the facial
expression "on-the-fly" (as they are fed to the system) is essential. In this
paper, we propose a new spatio-temporal feature learning method, which would
allow prediction with partial sequences. As such, the prediction could be
performed on-the-fly. The proposed method utilizes an estimated expression
intensity to generate dense labels, which are used to regulate the prediction
model training with a novel objective function. As results, the learned
spatio-temporal features can robustly predict the expression with partial
(incomplete) expression sequences, on-the-fly. Experimental results showed that
the proposed method achieved higher recognition rates compared to the
state-of-the-art methods on both datasets. More importantly, the results
verified that the proposed method improved the prediction frames with partial
expression sequence inputs.
Regularization, sparse recovery, and median-of-means tournaments
Authors: Gábor Lugosi, Shahar Mendelson
Date: 2017-01-15 00:00:00
Tasks: regression

A regularized risk minimization procedure for regression function estimation
is introduced that achieves near optimal accuracy and confidence under general
conditions, including heavy-tailed predictor and response variables. The
procedure is based on median-of-means tournaments, introduced by the authors in
[8]. It is shown that the new procedure outperforms standard regularized
empirical risk minimization procedures such as lasso or slope in heavy-tailed
problems.
Improved Regularization of Convolutional Neural Networks with Cutout
Authors: Terrance DeVries, Graham W. Taylor
Date: 2017-08-15 00:00:00
Tasks: Data Augmentation, Domain Generalization, Image Augmentation, Image Classification, Out-of-Distribution Generalization, Semi-Supervised Image Classification

Convolutional neural networks are capable of learning powerful
representational spaces, which are necessary for tackling complex learning
tasks. However, due to the model capacity required to capture such
representations, they are often susceptible to overfitting and therefore
require proper regularization in order to generalize well. In this paper, we
show that the simple regularization technique of randomly masking out square
regions of input during training, which we call cutout, can be used to improve
the robustness and overall performance of convolutional neural networks. Not
only is this method extremely easy to implement, but we also demonstrate that
it can be used in conjunction with existing forms of data augmentation and
other regularizers to further improve model performance. We evaluate this
method by applying it to current state-of-the-art architectures on the
CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results
of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at
https://github.com/uoguelph-mlrg/Cutout
Faster ICA under orthogonal constraint
Authors: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Date: 2017-11-29 00:00:00

Independent Component Analysis (ICA) is a technique for unsupervised
exploration of multi-channel data widely used in observational sciences. In its
classical form, ICA relies on modeling the data as a linear mixture of
non-Gaussian independent sources. The problem can be seen as a likelihood
maximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy
over the set of orthogonal matrices, which can quickly separate both super- and
sub-Gaussian signals. It returns the same set of sources as the widely used
FastICA algorithm. Through numerical experiments, we show that our method is
faster and more robust than FastICA on real data.