pwc-archive/papers-with-abstracts huggingface dataset:
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Date: 2016-12-18 00:00:00
Tasks: Prognosis

Modeling continuous-time physiological processes that manifest a patient's
evolving clinical states is a key step in approaching many problems in
healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model
(HASMM): a versatile probabilistic model that is capable of capturing the
modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM
accommodates irregularly sampled, temporally correlated, and informatively
censored physiological data, and can describe non-stationary clinical state
transitions. Learning an HASMM from the EHR data is achieved via a novel
forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the
knowledge of the end-point clinical outcomes (informative censoring) in the EHR
data, and implements the E-step by sequentially sampling the patients' clinical
states in the reverse-time direction while conditioning on the future states.
Real-time inferences are drawn via a forward- filtering algorithm that operates
on a virtually constructed discrete-time embedded Markov chain that mirrors the
patient's continuous-time state trajectory. We demonstrate the di- agnostic and
prognostic utility of the HASMM in a critical care prognosis setting using a
real-world dataset for patients admitted to the Ronald Reagan UCLA Medical
Center.
A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
Authors: David I. Inouye, Eunho Yang, Genevera I. Allen, Pradeep Ravikumar
Date: 2016-08-31 00:00:00

The Poisson distribution has been widely studied and used for modeling
univariate count-valued data. Multivariate generalizations of the Poisson
distribution that permit dependencies, however, have been far less popular.
Yet, real-world high-dimensional count-valued data found in word counts,
genomics, and crime statistics, for example, exhibit rich dependencies, and
motivate the need for multivariate distributions that can appropriately model
this data. We review multivariate distributions derived from the univariate
Poisson, categorizing these models into three main classes: 1) where the
marginal distributions are Poisson, 2) where the joint distribution is a
mixture of independent multivariate Poisson distributions, and 3) where the
node-conditional distributions are derived from the Poisson. We discuss the
development of multiple instances of these classes and compare the models in
terms of interpretability and theory. Then, we empirically compare multiple
models from each class on three real-world datasets that have varying data
characteristics from different domains, namely traffic accident data,
biological next generation sequencing data, and text data. These empirical
experiments develop intuition about the comparative advantages and
disadvantages of each class of multivariate distribution that was derived from
the Poisson. Finally, we suggest new research directions as explored in the
subsequent discussion section.
ASAP: Asynchronous Approximate Data-Parallel Computation
Authors: Asim Kadav, Erik Kruus
Date: 2016-12-27 00:00:00
Tasks: BIG-bench Machine Learning

Emerging workloads, such as graph processing and machine learning are
approximate because of the scale of data involved and the stochastic nature of
the underlying algorithms. These algorithms are often distributed over multiple
machines using bulk-synchronous processing (BSP) or other synchronous
processing paradigms such as map-reduce. However, data parallel processing
primitives such as repeated barrier and reduce operations introduce high
synchronization overheads. Hence, many existing data-processing platforms use
asynchrony and staleness to improve data-parallel job performance. Often, these
systems simply change the synchronous communication to asynchronous between the
worker nodes in the cluster. This improves the throughput of data processing
but results in poor accuracy of the final output since different workers may
progress at different speeds and process inconsistent intermediate outputs.
  In this paper, we present ASAP, a model that provides asynchronous and
approximate processing semantics for data-parallel computation. ASAP provides
fine-grained worker synchronization using NOTIFY-ACK semantics that allows
independent workers to run asynchronously. ASAP also provides stochastic reduce
that provides approximate but guaranteed convergence to the same result as an
aggregated all-reduce. In our results, we show that ASAP can reduce
synchronization costs and provides 2-10X speedups in convergence and up to 10X
savings in network costs for distributed machine learning applications and
provides strong convergence guarantees.
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
Authors: Maciej Wielgosz, Marcin Pietroń
Date: 2016-09-10 00:00:00
Tasks: Object Recognition

This paper examines the performance of a Spatial Pooler (SP) of a
Hierarchical Temporal Memory (HTM) in the task of noisy object recognition. To
address this challenge, a dedicated custom-designed system based on the SP,
histogram calculation module and SVM classifier was implemented. In addition to
implementing their own version of HTM, the authors also designed a profiler
which is capable of tracing all of the key parameters of the system. This was
necessary, since an analysis and monitoring of the system performance turned
out to be extremely difficult using conventional testing and debugging tools.
The system was initially trained on artificially prepared videos without noise
and then tested with a set of noisy video streams. This approach was intended
to mimic a real life scenario where an agent or a system trained to deal with
ideal objects faces a task of classifying distorted and noisy ones in its
regular working conditions. The authors conducted a series of experiments for
various macro parameters of HTM SP, as well as for different levels of video
reduction ratios. The experiments allowed them to evaluate the performance of
two different system setups (i.e. 'Multiple HTMs' and 'Single HTM') under
various noise conditions with 32--frame video files. Results of all the tests
were compared to SVM baseline setup. It was determined that the system
featuring SP is capable of achieving approximately 12 times the noise reduction
for a video signal with with distorted bits accounting for 13\% of the total.
Furthermore, the system featuring SP performed better also in the experiments
without a noise component and achieved a max F1 score of 0.96. The experiments
also revealed that a rise of column and synapse number of SP has a substantial
impact on the performance of the system. Consequently, the highest F1 score
values were obtained for 256 and 4096 synapses and columns respectively.
Monte Carlo Sort for unreliable human comparisons
Authors: Samuel L. Smith
Date: 2016-12-27 00:00:00
Tasks: Marketing

Algorithms which sort lists of real numbers into ascending order have been
studied for decades. They are typically based on a series of pairwise
comparisons and run entirely on chip. However people routinely sort lists which
depend on subjective or complex judgements that cannot be automated. Examples
include marketing research; where surveys are used to learn about customer
preferences for products, the recruiting process; where interviewers attempt to
rank potential employees, and sporting tournaments; where we infer team
rankings from a series of one on one matches. We develop a novel sorting
algorithm, where each pairwise comparison reflects a subjective human judgement
about which element is bigger or better. We introduce a finite and large error
rate to each judgement, and we take the cost of each comparison to
significantly exceed the cost of other computational steps. The algorithm must
request the most informative sequence of comparisons from the user; in order to
identify the correct sorted list with minimum human input. Our Discrete
Adiabatic Monte Carlo approach exploits the gradual acquisition of information
by tracking a set of plausible hypotheses which are updated after each
additional comparison.
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
Authors: Amir Hossein Akhavan Rahnama
Date: 2016-12-27 00:00:00
Tasks: Distributed Computing, Sentiment Analysis

Big data trend has enforced the data-centric systems to have continuous fast
data streams. In recent years, real-time analytics on stream data has formed
into a new research field, which aims to answer queries about
what-is-happening-now with a negligible delay. The real challenge with
real-time stream data processing is that it is impossible to store instances of
data, and therefore online analytical algorithms are utilized. To perform
real-time analytics, pre-processing of data should be performed in a way that
only a short summary of stream is stored in main memory. In addition, due to
high speed of arrival, average processing time for each instance of data should
be in such a way that incoming instances are not lost without being captured.
Lastly, the learner needs to provide high analytical accuracy measures.
Sentinel is a distributed system written in Java that aims to solve this
challenge by enforcing both the processing and learning process to be done in
distributed form. Sentinel is built on top of Apache Storm, a distributed
computing platform. Sentinels learner, Vertical Hoeffding Tree, is a parallel
decision tree-learning algorithm based on the VFDT, with ability of enabling
parallel classification in distributed environments. Sentinel also uses
SpaceSaving to keep a summary of the data stream and stores its summary in a
synopsis data structure. Application of Sentinel on Twitter Public Stream API
is shown and the results are discussed.
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild
Authors: Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan
Date: 2016-12-27 00:00:00
Tasks: Decoder, Face Recognition

Face recognition techniques have been developed significantly in recent
years. However, recognizing faces with partial occlusion is still challenging
for existing face recognizers which is heavily desired in real-world
applications concerning surveillance and security. Although much research
effort has been devoted to developing face de-occlusion methods, most of them
can only work well under constrained conditions, such as all the faces are from
a pre-defined closed set. In this paper, we propose a robust LSTM-Autoencoders
(RLA) model to effectively restore partially occluded faces even in the wild.
The RLA model consists of two LSTM components, which aims at occlusion-robust
face encoding and recurrent occlusion removal respectively. The first one,
named multi-scale spatial LSTM encoder, reads facial patches of various scales
sequentially to output a latent representation, and occlusion-robustness is
achieved owing to the fact that the influence of occlusion is only upon some of
the patches. Receiving the representation learned by the encoder, the LSTM
decoder with a dual channel architecture reconstructs the overall face and
detects occlusion simultaneously, and by feat of LSTM, the decoder breaks down
the task of face de-occlusion into restoring the occluded part step by step.
Moreover, to minimize identify information loss and guarantee face recognition
accuracy over recovered faces, we introduce an identity-preserving adversarial
training scheme to further improve RLA. Extensive experiments on both synthetic
and real datasets of faces with occlusion clearly demonstrate the effectiveness
of our proposed RLA in removing different types of facial occlusion at various
locations. The proposed method also provides significantly larger performance
gain than other de-occlusion methods in promoting recognition performance over
partially-occluded faces.
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Authors: Nathanael Perraudin, Vassilis Kalofolias, David Shuman, Pierre Vandergheynst
Date: 2014-02-04 00:00:00

Convex optimization is an essential tool for machine learning, as many of its
problems can be formulated as minimization problems of specific objective
functions. While there is a large variety of algorithms available to solve
convex problems, we can argue that it becomes more and more important to focus
on efficient, scalable methods that can deal with big data. When the objective
function can be written as a sum of "simple" terms, proximal splitting methods
are a good choice. UNLocBoX is a MATLAB library that implements many of these
methods, designed to solve convex optimization problems of the form $\min_{x
\in \mathbb{R}^N} \sum_{n=1}^K f_n(x).$ It contains the most recent solvers
such as FISTA, Douglas-Rachford, SDMM as well a primal dual techniques such as
Chambolle-Pock and forward-backward-forward. It also includes an extensive list
of common proximal operators that can be combined, allowing for a quick
implementation of a large variety of convex problems.
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Authors: Jian Shi, Yue Dong, Hao Su, Stella X. Yu
Date: 2016-12-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Object

We consider the non-Lambertian object intrinsic problem of recovering diffuse
albedo, shading, and specular highlights from a single image of an object.
  We build a large-scale object intrinsics database based on existing 3D models
in the ShapeNet database. Rendered with realistic environment maps, millions of
synthetic images of objects and their corresponding albedo, shading, and
specular ground-truth images are used to train an encoder-decoder CNN. Once
trained, the network can decompose an image into the product of albedo and
shading components, along with an additive specular component.
  Our CNN delivers accurate and sharp results in this classical inverse problem
of computer vision, sharp details attributed to skip layer connections at
corresponding resolutions from the encoder to the decoder. Benchmarked on our
ShapeNet and MIT intrinsics datasets, our model consistently outperforms the
state-of-the-art by a large margin.
  We train and test our CNN on different object categories. Perhaps surprising
especially from the CNN classification perspective, our intrinsics CNN
generalizes very well across categories. Our analysis shows that feature
learning at the encoder stage is more crucial for developing a universal
representation across categories.
  We apply our synthetic data trained model to images and videos downloaded
from the internet, and observe robust and realistic intrinsics results. Quality
non-Lambertian intrinsics could open up many interesting applications such as
image-based albedo and specular editing.
Classifying Patents Based on their Semantic Content
Authors: Antonin Bergeaud, Yoann Potiron, Juste Raimbault
Date: 2016-12-27 00:00:00
Tasks: General Classification

In this paper, we extend some usual techniques of classification resulting
from a large-scale data-mining and network approach. This new technology, which
in particular is designed to be suitable to big data, is used to construct an
open consolidated database from raw data on 4 million patents taken from the US
patent office from 1976 onward. To build the pattern network, not only do we
look at each patent title, but we also examine their full abstract and extract
the relevant keywords accordingly. We refer to this classification as semantic
approach in contrast with the more common technological approach which consists
in taking the topology when considering US Patent office technological classes.
Moreover, we document that both approaches have highly different topological
measures and strong statistical evidence that they feature a different model.
This suggests that our method is a useful tool to extract endogenous
information.
End-to-End Data Visualization by Metric Learning and Coordinate Transformation
Authors: Lilei Zheng, Ying Zhang, Stefan Duffner, Khalid Idrissi, Christophe Garcia, Atilla Baskurt
Date: 2016-12-27 00:00:00
Tasks: Data Visualization, General Classification, Metric Learning

This paper presents a deep nonlinear metric learning framework for data
visualization on an image dataset. We propose the Triangular Similarity and
prove its equivalence to the Cosine Similarity in measuring a data pair. Based
on this novel similarity, a geometrically motivated loss function - the
triangular loss - is then developed for optimizing a metric learning system
comprising two identical CNNs. It is shown that this deep nonlinear system can
be efficiently trained by a hybrid algorithm based on the conventional
backpropagation algorithm. More interestingly, benefiting from classical
manifold learning theories, the proposed system offers two different views to
visualize the outputs, the second of which provides better classification
results than the state-of-the-art methods in the visualizable spaces.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
Authors: Zewang Zhang, Zheng Sun, Jiaqi Liu, Jingwen Chen, Zhao Huo, Xiao Zhang
Date: 2016-11-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

A deep learning approach has been widely applied in sequence modeling
problems. In terms of automatic speech recognition (ASR), its performance has
significantly been improved by increasing large speech corpus and deeper neural
network. Especially, recurrent neural network and deep convolutional neural
network have been applied in ASR successfully. Given the arising problem of
training speed, we build a novel deep recurrent convolutional network for
acoustic modeling and then apply deep residual learning to it. Our experiments
show that it has not only faster convergence speed but better recognition
accuracy over traditional deep convolutional recurrent network. In the
experiments, we compare the convergence speed of our novel deep recurrent
convolutional networks and traditional deep convolutional recurrent networks.
With faster convergence speed, our novel deep recurrent convolutional networks
can reach the comparable performance. We further show that applying deep
residual learning can boost the convergence speed of our novel deep recurret
convolutional networks. Finally, we evaluate all our experimental networks by
phoneme error rate (PER) with our proposed bidirectional statistical n-gram
language model. Our evaluation results show that our newly proposed deep
recurrent convolutional network applied with deep residual learning can reach
the best PER of 17.33\% with the fastest convergence speed on TIMIT database.
The outstanding performance of our novel deep recurrent convolutional neural
network with deep residual learning indicates that it can be potentially
adopted in other sequential problems.
Steerable CNNs
Authors: Taco S. Cohen, Max Welling
Date: 2016-12-27 00:00:00
Tasks: General Classification, image-classification, Image Classification

It has long been recognized that the invariance and equivariance properties
of a representation are critically important for success in many vision tasks.
In this paper we present Steerable Convolutional Neural Networks, an efficient
and flexible class of equivariant convolutional networks. We show that
steerable CNNs achieve state of the art results on the CIFAR image
classification benchmark. The mathematical theory of steerable representations
reveals a type system in which any steerable representation is a composition of
elementary feature types, each one associated with a particular kind of
symmetry. We show how the parameter cost of a steerable filter bank depends on
the types of the input and output features, and show how to use this knowledge
to construct CNNs that utilize parameters effectively.
An Automated CNN Recommendation System for Image Classification Tasks
Authors: Song Wang, Li Sun, Wei Fan, Jun Sun, Satoshi Naoi, Koichi Shirahata, Takuya Fukagai, Yasumoto Tomita, Atsushi Ike
Date: 2016-12-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Nowadays the CNN is widely used in practical applications for image
classification task. However the design of the CNN model is very professional
work and which is very difficult for ordinary users. Besides, even for experts
of CNN, to select an optimal model for specific task may still need a lot of
time (to train many different models). In order to solve this problem, we
proposed an automated CNN recommendation system for image classification task.
Our system is able to evaluate the complexity of the classification task and
the classification ability of the CNN model precisely. By using the evaluation
results, the system can recommend the optimal CNN model and which can match the
task perfectly. The recommendation process of the system is very fast since we
don't need any model training. The experiment results proved that the
evaluation methods are very accurate and reliable.
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
Authors: Keke Tang, Peng Song, Xiaoping Chen
Date: 2016-12-26 00:00:00
Tasks: 3D Object Recognition, Denoising, Object Recognition

Depth scans acquired from different views may contain nuisances such as
noise, occlusion, and varying point density. We propose a novel Signature of
Geometric Centroids descriptor, supporting direct shape matching on the scans,
without requiring any preprocessing such as scan denoising or converting into a
mesh. First, we construct the descriptor by voxelizing the local shape within a
uniquely defined local reference frame and concatenating geometric centroid and
point density features extracted from each voxel. Second, we compare two
descriptors by employing only corresponding voxels that are both non-empty,
thus supporting matching incomplete local shape such as those close to scan
boundary. Third, we propose a descriptor saliency measure and compute it from a
descriptor-graph to improve shape matching performance. We demonstrate the
descriptor's robustness and effectiveness for shape matching by comparing it
with three state-of-the-art descriptors, and applying it to object/scene
reconstruction and 3D object recognition.
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains
Authors: Muhammad Yousefnezhad, Daoqiang Zhang
Date: 2016-12-26 00:00:00
Tasks: Time Series, Time Series Analysis

Multivariate Pattern (MVP) classification holds enormous potential for
decoding visual stimuli in the human brain by employing task-based fMRI data
sets. There is a wide range of challenges in the MVP techniques, i.e.
decreasing noise and sparsity, defining effective regions of interest (ROIs),
visualizing results, and the cost of brain studies. In overcoming these
challenges, this paper proposes a novel model of neural representation, which
can automatically detect the active regions for each visual stimulus and then
utilize these anatomical regions for visualizing and analyzing the functional
activities. Therefore, this model provides an opportunity for neuroscientists
to ask this question: what is the effect of a stimulus on each of the detected
regions instead of just study the fluctuation of voxels in the manually
selected ROIs. Moreover, our method introduces analyzing snapshots of brain
image for decreasing sparsity rather than using the whole of fMRI time series.
Further, a new Gaussian smoothing method is proposed for removing noise of
voxels in the level of ROIs. The proposed method enables us to combine
different fMRI data sets for reducing the cost of brain studies. Experimental
studies on 4 visual categories (words, consonants, objects and nonsense photos)
confirm that the proposed method achieves superior performance to
state-of-the-art methods.