pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification
Authors: Raphael Prates, William Robson Schwartz
Date: 2016-11-21 00:00:00
Tasks: General Classification, Person Re-Identification

Person re-identification aims at the maintenance of a global identity as a
person moves among non-overlapping surveillance cameras. It is a hard task due
to different illumination conditions, viewpoints and the small number of
annotated individuals from each pair of cameras (small-sample-size problem).
Collaborative Representation based Classification (CRC) has been employed
successfully to address the small-sample-size problem in computer vision.
However, the original CRC formulation is not well-suited for person
re-identification since it does not consider that probe and gallery samples are
from different cameras. Furthermore, it is a linear model, while appearance
changes caused by different camera conditions indicate a strong nonlinear
transition between cameras. To overcome such limitations, we propose the Kernel
Cross-View Collaborative Representation based Classification (Kernel X-CRC)
that represents probe and gallery images by balancing representativeness and
similarity nonlinearly. It assumes that a probe and its corresponding gallery
image are represented with similar coding vectors using individuals from the
training set. Experimental results demonstrate that our assumption is true when
using a high-dimensional feature vector and becomes more compelling when
dealing with a low-dimensional and discriminative representation computed using
a common subspace learning method. We achieve state-of-the-art for rank-1
matching rates in two person re-identification datasets (PRID450S and GRID) and
the second best results on VIPeR and CUHK01 datasets.
Optical Flow Estimation using a Spatial Pyramid Network
Authors: Anurag Ranjan, Michael J. Black
Date: 2016-11-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dense Pixel Correspondence Estimation, Optical Flow Estimation

We learn to compute optical flow by combining a classical spatial-pyramid
formulation with deep learning. This estimates large motions in a
coarse-to-fine approach by warping one image of a pair at each pyramid level by
the current flow estimate and computing an update to the flow. Instead of the
standard minimization of an objective function at each pyramid level, we train
one deep network per level to compute the flow update. Unlike the recent
FlowNet approach, the networks do not need to deal with large motions; these
are dealt with by the pyramid. This has several advantages. First, our Spatial
Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms
of model parameters. This makes it more efficient and appropriate for embedded
applications. Second, since the flow at each pyramid level is small (< 1
pixel), a convolutional approach applied to pairs of warped images is
appropriate. Third, unlike FlowNet, the learned convolution filters appear
similar to classical spatio-temporal filters, giving insight into the method
and how to improve it. Our results are more accurate than FlowNet on most
standard benchmarks, suggesting a new direction of combining classical flow
methods with deep learning.
Statistical Learning for OCR Text Correction
Authors: Jie Mei, Aminul Islam, Yajing Wu, Abidalrahman Moh'd, Evangelos E. Milios
Date: 2016-11-21 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

The accuracy of Optical Character Recognition (OCR) is crucial to the success
of subsequent applications used in text analyzing pipeline. Recent models of
OCR post-processing significantly improve the quality of OCR-generated text,
but are still prone to suggest correction candidates from limited observations
while insufficiently accounting for the characteristics of OCR errors. In this
paper, we show how to enlarge candidate suggestion space by using external
corpus and integrating OCR-specific features in a regression approach to
correct OCR-generated errors. The evaluation results show that our model can
correct 61.5% of the OCR-errors (considering the top 1 suggestion) and 71.5% of
the OCR-errors (considering the top 3 suggestions), for cases where the
theoretical correction upper-bound is 78%.
A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications
Authors: Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer
Date: 2016-11-21 00:00:00
Tasks: Deep Learning, GPU

In recent years, deep neural networks (DNNs), have yielded strong results on
a wide range of applications. Graphics Processing Units (GPUs) have been one
key enabling factor leading to the current popularity of DNNs. However, despite
increasing hardware flexibility and software programming toolchain maturity,
high efficiency GPU programming remains difficult: it suffers from high
complexity, low productivity, and low portability. GPU vendors such as NVIDIA
have spent enormous effort to write special-purpose DNN libraries. However, on
other hardware targets, especially mobile GPUs, such vendor libraries are not
generally available. Thus, the development of portable, open, high-performance,
energy-efficient GPU code for DNN operations would enable broader deployment of
DNN-based algorithms. Toward this end, this work presents a framework to enable
productive, high-efficiency GPU programming for DNN computations across
hardware platforms and programming models. In particular, the framework
provides specific support for metaprogramming, autotuning, and DNN-tailored
data types. Using our framework, we explore implementing DNN operations on
three different hardware targets: NVIDIA, AMD, and Qualcomm GPUs. On NVIDIA
GPUs, we show both portability between OpenCL and CUDA as well competitive
performance compared to the vendor library. On Qualcomm GPUs, we show that our
framework enables productive development of target-specific optimizations, and
achieves reasonable absolute performance. Finally, On AMD GPUs, we show initial
results that indicate our framework can yield reasonable performance on a new
platform with minimal effort.
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning
Authors: Zeynettin Akkus, Issa Ali, Jiri Sedlar, Timothy L. Kline, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, Bradley J. Erickson
Date: 2016-11-21 00:00:00
Tasks: Image Registration, Self-Learning, Specificity, Tumor Segmentation

Objective: Several studies have associated codeletion of chromosome arms
1p/19q in low-grade gliomas (LGG) with positive response to treatment and
longer progression free survival. Therefore, predicting 1p/19q status is
crucial for effective treatment planning of LGG. In this study, we predict the
1p/19q status from MR images using convolutional neural networks (CNN), which
could be a noninvasive alternative to surgical biopsy and histopathological
analysis. Method: Our method consists of three main steps: image registration,
tumor segmentation, and classification of 1p/19q status using CNN. We included
a total of 159 LGG with 3 image slices each who had biopsy-proven 1p/19q status
(57 nondeleted and 102 codeleted) and preoperative postcontrast-T1 (T1C) and T2
images. We divided our data into training, validation, and test sets. The
training data was balanced for equal class probability and then augmented with
iterations of random translational shift, rotation, and horizontal and vertical
flips to increase the size of the training set. We shuffled and augmented the
training data to counter overfitting in each epoch. Finally, we evaluated
several configurations of a multi-scale CNN architecture until training and
validation accuracies became consistent. Results: The results of the best
performing configuration on the unseen test set were 93.3% (sensitivity),
82.22% (specificity), and 87.7% (accuracy). Conclusion: Multi-scale CNN with
their self-learning capability provides promising results for predicting 1p/19q
status noninvasively based on T1C and T2 images. Significance: Predicting
1p/19q status noninvasively from MR images would allow selecting effective
treatment strategies for LGG patients without the need for surgical biopsy.
Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks
Authors: Jonathan Y. Suen, Saket Navlakha
Date: 2016-11-21 00:00:00

Controlling the flow and routing of data is a fundamental problem in many
distributed networks, including transportation systems, integrated circuits,
and the Internet. In the brain, synaptic plasticity rules have been discovered
that regulate network activity in response to environmental inputs, which
enable circuits to be stable yet flexible. Here, we develop a new
neuro-inspired model for network flow control that only depends on modifying
edge weights in an activity-dependent manner. We show how two fundamental
plasticity rules (long-term potentiation and long-term depression) can be cast
as a distributed gradient descent algorithm for regulating traffic flow in
engineered networks. We then characterize, both via simulation and
analytically, how different forms of edge-weight update rules affect network
routing efficiency and robustness. We find a close correspondence between
certain classes of synaptic weight update rules derived experimentally in the
brain and rules commonly used in engineering, suggesting common principles to
both.
Unsupervised Learning for Lexicon-Based Classification
Authors: Jacob Eisenstein
Date: 2016-11-21 00:00:00
Tasks: Classification, General Classification

In lexicon-based classification, documents are assigned labels by comparing
the number of words that appear from two opposed lexicons, such as positive and
negative sentiment. Creating such words lists is often easier than labeling
instances, and they can be debugged by non-experts if classification
performance is unsatisfactory. However, there is little analysis or
justification of this classification heuristic. This paper describes a set of
assumptions that can be used to derive a probabilistic justification for
lexicon-based classification, as well as an analysis of its expected accuracy.
One key assumption behind lexicon-based classification is that all words in
each lexicon are equally predictive. This is rarely true in practice, which is
why lexicon-based approaches are usually outperformed by supervised classifiers
that learn distinct weights on each word from labeled instances. This paper
shows that it is possible to learn such weights without labeled data, by
leveraging co-occurrence statistics across the lexicons. This offers the best
of both worlds: light supervision in the form of lexicons, and data-driven
classification with higher accuracy than traditional word-counting heuristics.
Memory Lens: How Much Memory Does an Agent Use?
Authors: Christoph Dann, Katja Hofmann, Sebastian Nowozin
Date: 2016-11-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new method to study the internal memory used by reinforcement
learning policies. We estimate the amount of relevant past information by
estimating mutual information between behavior histories and the current action
of an agent. We perform this estimation in the passive setting, that is, we do
not intervene but merely observe the natural behavior of the agent. Moreover,
we provide a theoretical justification for our approach by showing that it
yields an implementation-independent lower bound on the minimal memory capacity
of any agent that implement the observed policy. We demonstrate our approach by
estimating the use of memory of DQN policies on concatenated Atari frames,
demonstrating sharply different use of memory across 49 games. The study of
memory as information that flows from the past to the current action opens
avenues to understand and improve successful reinforcement learning algorithms.
On the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach
Authors: Masood Tehrani, Mary Ahmadi
Date: 2016-11-21 00:00:00

In this study, an Artificial Neural Network (ANN) approach is utilized to
perform a parametric study on the process of extraction of lubricants from
heavy petroleum cuts. To train the model, we used field data collected from an
industrial plant. Operational conditions of feed and solvent flow rate,
Temperature of streams and mixing rate were considered as the input to the
model, whereas the flow rate of the main product was considered as the output
of the ANN model. A feed-forward Multi-Layer Perceptron Neural Network was
successfully applied to capture the relationship between inputs and output
parameters.
Learning From Graph Neighborhoods Using LSTMs
Authors: Rakshit Agrawal, Luca de Alfaro, Vassilis Polychronopoulos
Date: 2016-11-21 00:00:00

Many prediction problems can be phrased as inferences over local
neighborhoods of graphs. The graph represents the interaction between entities,
and the neighborhood of each entity contains information that allows the
inferences or predictions. We present an approach for applying machine learning
directly to such graph neighborhoods, yielding predicitons for graph nodes on
the basis of the structure of their local neighborhood and the features of the
nodes in it. Our approach allows predictions to be learned directly from
examples, bypassing the step of creating and tuning an inference model or
summarizing the neighborhoods via a fixed set of hand-crafted features. The
approach is based on a multi-level architecture built from Long Short-Term
Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood
from data. We demonstrate the effectiveness of the proposed technique on a
synthetic example and on real-world data related to crowdsourced grading,
Bitcoin transactions, and Wikipedia edit reversions.
The subset-matched Jaccard index for evaluation of Segmentation for Plant Images
Authors: Jonathan Bell, Hannah M. Dee
Date: 2016-11-21 00:00:00
Tasks: Segmentation

We describe a new measure for the evaluation of region level segmentation of
objects, as applied to evaluating the accuracy of leaf-level segmentation of
plant images. The proposed approach enforces the rule that a region (e.g. a
leaf) in either the image being evaluated or the ground truth image evaluated
against can be mapped to no more than one region in the other image. We call
this measure the subset-matched Jaccard index.
Probabilistic structure discovery in time series data
Authors: David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank Wood
Date: 2016-11-21 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Existing methods for structure discovery in time series data construct
interpretable, compositional kernels for Gaussian process regression models.
While the learned Gaussian process model provides posterior mean and variance
estimates, typically the structure is learned via a greedy optimization
procedure. This restricts the space of possible solutions and leads to
over-confident uncertainty estimates. We introduce a fully Bayesian approach,
inferring a full posterior over structures, which more reliably captures the
uncertainty of the model.
Exponential Family Embeddings
Authors: Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
Date: 2016-08-02 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Dimensionality Reduction, Movie Recommendation, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Word embeddings are a powerful approach for capturing semantic similarity
among terms in a vocabulary. In this paper, we develop exponential family
embeddings, a class of methods that extends the idea of word embeddings to
other types of high-dimensional data. As examples, we studied neural data with
real-valued observations, count data from a market basket analysis, and ratings
data from a movie recommendation system. The main idea is to model each
observation conditioned on a set of other observations. This set is called the
context, and the way the context is defined is a modeling choice that depends
on the problem. In language the context is the surrounding words; in
neuroscience the context is close-by neurons; in market basket data the context
is other items in the shopping cart. Each type of embedding model defines the
context, the exponential family of conditional distributions, and how the
latent embedding vectors are shared across data. We infer the embeddings with a
scalable algorithm based on stochastic gradient descent. On all three
applications - neural activity of zebrafish, users' shopping behavior, and
movie ratings - we found exponential family embedding models to be more
effective than other types of dimension reduction. They better reconstruct
held-out data and find interesting qualitative structure.
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography
Authors: Kezhi Li, Daniel Holland
Date: 2016-03-02 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Sensitivity

A new iterative image reconstruction algorithm for electrical capacitance
tomography (ECT) is proposed that is based on iterative soft thresholding of a
total variation penalty and adaptive reweighted compressive sensing. This
algorithm encourages sharp changes in the ECT image and overcomes the
disadvantage of the $l_1$ minimization by equipping the total variation with an
adaptive weighting depending on the reconstructed image. Moreover, the
non-linear effect is also partially reduced due to the adoption of an updated
sensitivity matrix. Simulation results show that the proposed algorithm
recovers ECT images more precisely than existing state-of-the-art algorithms
and therefore is suitable for the imaging of multiphase systems in industrial
or medical applications.
A Surrogate-based Generic Classifier for Chinese TV Series Reviews
Authors: Yu-feng Ma, Long Xia, Wenqi Shen, Mi Zhou, Weiguo Fan
Date: 2016-11-08 00:00:00

With the emerging of various online video platforms like Youtube, Youku and
LeTV, online TV series' reviews become more and more important both for viewers
and producers. Customers rely heavily on these reviews before selecting TV
series, while producers use them to improve the quality. As a result,
automatically classifying reviews according to different requirements evolves
as a popular research topic and is essential in our daily life. In this paper,
we focused on reviews of hot TV series in China and successfully trained
generic classifiers based on eight predefined categories. The experimental
results showed promising performance and effectiveness of its generalization to
different TV series.
Privacy-Preserved Big Data Analysis Based on Asymmetric Imputation Kernels and Multiside Similarities
Authors: Bo-Wei Chen
Date: 2016-03-25 00:00:00
Tasks: Clustering, Imputation

This study presents an efficient approach for incomplete data classification,
where the entries of samples are missing or masked due to privacy preservation.
To deal with these incomplete data, a new kernel function with asymmetric
intrinsic mappings is proposed in this study. Such a new kernel uses three-side
similarities for kernel matrix formation. The similarity between a testing
instance and a training sample relies not only on their distance but also on
the relation between the testing sample and the centroid of the class, where
the training sample belongs. This reduces biased estimation compared with
typical methods when only one training sample is used for kernel matrix
formation. Furthermore, centroid generation does not involve any clustering
algorithms. The proposed kernel is capable of performing data imputation by
using class-dependent averages. This enhances Fisher Discriminant Ratios and
data discriminability. Experiments on two open databases were carried out for
evaluating the proposed method. The result indicated that the accuracy of the
proposed method was higher than that of the baseline. These findings thereby
demonstrated the effectiveness of the proposed idea.
MDL-motivated compression of GLM ensembles increases interpretability and retains predictive power
Authors: Boris Hayete, Matthew Valko, Alex Greenfield, Raymond Yan
Date: 2016-11-21 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Over the years, ensemble methods have become a staple of machine learning.
Similarly, generalized linear models (GLMs) have become very popular for a wide
variety of statistical inference tasks. The former have been shown to enhance
out- of-sample predictive power and the latter possess easy interpretability.
Recently, ensembles of GLMs have been proposed as a possibility. On the
downside, this approach loses the interpretability that GLMs possess. We show
that minimum description length (MDL)-motivated compression of the inferred
ensembles can be used to recover interpretability without much, if any,
downside to performance and illustrate on a number of standard classification
data sets.
Generalized Dropout
Authors: Suraj Srinivas, R. Venkatesh Babu
Date: 2016-11-21 00:00:00
Tasks: Bayesian Inference

Deep Neural Networks often require good regularizers to generalize well.
Dropout is one such regularizer that is widely used among Deep Learning
practitioners. Recent work has shown that Dropout can also be viewed as
performing Approximate Bayesian Inference over the network parameters. In this
work, we generalize this notion and introduce a rich family of regularizers
which we call Generalized Dropout. One set of methods in this family, called
Dropout++, is a version of Dropout with trainable parameters. Classical Dropout
emerges as a special case of this method. Another member of this family selects
the width of neural network layers. Experiments show that these methods help in
improving generalization performance over Dropout.
Bidirectional Tree-Structured LSTM with Head Lexicalization
Authors: Zhiyang Teng, Yue Zhang
Date: 2016-11-21 00:00:00
Tasks: General Classification

Sequential LSTM has been extended to model tree structures, giving
competitive results for a number of tasks. Existing methods model constituent
trees by bottom-up combinations of constituent nodes, making direct use of
input word information only for leaf nodes. This is different from sequential
LSTMs, which contain reference to input words for each node. In this paper, we
propose a method for automatic head-lexicalization for tree-structure LSTMs,
propagating head words from leaf nodes to every constituent node. In addition,
enabled by head lexicalization, we build a tree LSTM in the top-down direction,
which corresponds to bidirectional sequential LSTM structurally. Experiments
show that both extensions give better representations of tree structures. Our
final model gives the best results on the Standford Sentiment Treebank and
highly competitive results on the TREC question type classification task.
ConfocalGN : a minimalistic confocal image simulator
Authors: Serge Dmitrieff, François Nédélec
Date: 2016-10-31 00:00:00

SUMMARY : We developed a user-friendly software to generate synthetic
confocal microscopy images from a ground truth specified as a 3D bitmap with
pixels of arbitrary size. The software can analyze a real confocal stack to
derivate noise parameters and will use them directly to generate new images
with similar noise characteristics. Such synthetic images can then be used to
assert the quality and robustness of an image analysis pipeline, as well as be
used to train machine-learning image analysis procedures. We illustrate the
approach with closed curves corresponding to the microtubule ring present in
blood platelet. AVAILABILITY AND IMPLEMENTATION: ConfocalGN is written in
Matlab but does not require any toolbox. The source code is distributed under
the GPL 3.0 licence on https://github.com/SergeDmi/ConfocalGN.