pwc-archive/papers-with-abstracts huggingface dataset:
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Mengying Jiang, Wing-Kuen Ling
Date: 2017-09-05 00:00:00
Tasks: Classification, General Classification

As a new machine learning approach, extreme learning machine (ELM) has
received wide attentions due to its good performances. However, when directly
applied to the hyperspectral image (HSI) classification, the recognition rate
is too low. This is because ELM does not use the spatial information which is
very important for HSI classification. In view of this, this paper proposes a
new framework for spectral-spatial classification of HSI by combining ELM with
loopy belief propagation (LBP). The original ELM is linear, and the nonlinear
ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based
on lots of experiments and analysis, we found out that the LELM is a better
choice than nonlinear ELM for spectral-spatial classification of HSI.
Furthermore, we exploit the marginal probability distribution that uses the
whole information in the HSI and learn such distribution using the LBP. The
proposed method not only maintain the fast speed of ELM, but also greatly
improves the accuracy of classification. The experimental results in the
well-known HSI data sets, Indian Pines and Pavia University, demonstrate the
good performances of the proposed method.
Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method
Authors: Elyas Sabeti, Anders Høst-Madsen
Date: 2017-10-12 00:00:00
Tasks: Heart Rate Variability

Heart rate variability (HRV) is a vital measure of the autonomic nervous
system functionality and a key indicator of cardiovascular condition. This
paper proposes a novel method, called pattern tree which is an extension of
Willem's context tree to real-valued data, to investigate HRV via an
atypicality framework. In a previous paper atypicality was developed as method
for mining and discovery in "Big Data," which requires a universal approach.
Using the proposed pattern tree as a universal source coder in this framework
led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring.
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
Authors: Youzuo Lin, Shusen Wang, Jayaraman Thiagarajan, George Guthrie, David Coblentz
Date: 2017-10-11 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, regression

Conventional seismic techniques for detecting the subsurface geologic
features are challenged by limited data coverage, computational inefficiency,
and subjective human factors. We developed a novel data-driven geological
feature detection approach based on pre-stack seismic measurements. Our
detection method employs an efficient and accurate machine-learning detection
approach to extract useful subsurface geologic features automatically.
Specifically, our method is based on kernel ridge regression model. The
conventional kernel ridge regression can be computationally prohibited because
of the large volume of seismic measurements. We employ a data reduction
technique in combination with the conventional kernel ridge regression method
to improve the computational efficiency and reduce memory usage. In particular,
we utilize a randomized numerical linear algebra technique, named Nystr\"om
method, to effectively reduce the dimensionality of the feature space without
compromising the information content required for accurate detection. We
provide thorough computational cost analysis to show efficiency of our new
geological feature detection methods. We further validate the performance of
our new subsurface geologic feature detection method using synthetic surface
seismic data for 2D acoustic and elastic velocity models. Our numerical
examples demonstrate that our new detection method significantly improves the
computational efficiency while maintaining comparable accuracy. Interestingly,
we show that our method yields a speed-up ratio on the order of $\sim10^2$ to
$\sim 10^3$ in a multi-core computational environment.
What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
Authors: Oh-Hyun Kwon, Tarik Crnovrsanin, Kwan-Liu Ma
Date: 2017-10-11 00:00:00

Using different methods for laying out a graph can lead to very different
visual appearances, with which the viewer perceives different information.
Selecting a "good" layout method is thus important for visualizing a graph. The
selection can be highly subjective and dependent on the given task. A common
approach to selecting a good layout is to use aesthetic criteria and visual
inspection. However, fully calculating various layouts and their associated
aesthetic metrics is computationally expensive. In this paper, we present a
machine learning approach to large graph visualization based on computing the
topological similarity of graphs using graph kernels. For a given graph, our
approach can show what the graph would look like in different layouts and
estimate their corresponding aesthetic metrics. An important contribution of
our work is the development of a new framework to design graph kernels. Our
experimental study shows that our estimation calculation is considerably faster
than computing the actual layouts and their aesthetic metrics. Also, our graph
kernels outperform the state-of-the-art ones in both time and accuracy. In
addition, we conducted a user study to demonstrate that the topological
similarity computed with our graph kernel matches perceptual similarity
assessed by human users.
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
Authors: Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Date: 2017-10-04 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

Convolutional neural networks have recently demonstrated high-quality
reconstruction for single image super-resolution. However, existing methods
often require a large number of network parameters and entail heavy
computational loads at runtime for generating high-accuracy super-resolution
results. In this paper, we propose the deep Laplacian Pyramid Super-Resolution
Network for fast and accurate image super-resolution. The proposed network
progressively reconstructs the sub-band residuals of high-resolution images at
multiple pyramid levels. In contrast to existing methods that involve the
bicubic interpolation for pre-processing (which results in large feature maps),
the proposed method directly extracts features from the low-resolution input
space and thereby entails low computational loads. We train the proposed
network with deep supervision using the robust Charbonnier loss functions and
achieve high-quality image reconstruction. Furthermore, we utilize the
recursive layers to share parameters across as well as within pyramid levels,
and thus drastically reduce the number of parameters. Extensive quantitative
and qualitative evaluations on benchmark datasets show that the proposed
algorithm performs favorably against the state-of-the-art methods in terms of
run-time and image quality.
Improved Coresets for Kernel Density Estimates
Authors: Jeff M. Phillips, Wai Ming Tai
Date: 2017-10-11 00:00:00

We study the construction of coresets for kernel density estimates. That is
we show how to approximate the kernel density estimate described by a large
point set with another kernel density estimate with a much smaller point set.
For characteristic kernels (including Gaussian and Laplace kernels), our
approximation preserves the $L_\infty$ error between kernel density estimates
within error $\epsilon$, with coreset size $2/\epsilon^2$, but no other aspects
of the data, including the dimension, the diameter of the point set, or the
bandwidth of the kernel common to other approximations. When the dimension is
unrestricted, we show this bound is tight for these kernels as well as a much
broader set.
  This work provides a careful analysis of the iterative Frank-Wolfe algorithm
adapted to this context, an algorithm called \emph{kernel herding}. This
analysis unites a broad line of work that spans statistics, machine learning,
and geometry.
  When the dimension $d$ is constant, we demonstrate much tighter bounds on the
size of the coreset specifically for Gaussian kernels, showing that it is
bounded by the size of the coreset for axis-aligned rectangles. Currently the
best known constructive bound is $O(\frac{1}{\epsilon} \log^d
\frac{1}{\epsilon})$, and non-constructively, this can be improved by
$\sqrt{\log \frac{1}{\epsilon}}$. This improves the best constant dimension
bounds polynomially for $d \geq 3$.
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
Authors: Md. Kamruzzaman Sarker, Ning Xie, Derek Doran, Michael Raymer, Pascal Hitzler
Date: 2017-10-11 00:00:00

The ever increasing prevalence of publicly available structured data on the
World Wide Web enables new applications in a variety of domains. In this paper,
we provide a conceptual approach that leverages such data in order to explain
the input-output behavior of trained artificial neural networks. We apply
existing Semantic Web technologies in order to provide an experimental proof of
concept.
Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science
Authors: Kyle Hundman, Chris A. Mattmann
Date: 2017-10-11 00:00:00
Tasks: Sentence

We propose Marve, a system for extracting measurement values, units, and
related words from natural language text. Marve uses conditional random fields
(CRF) to identify measurement values and units, followed by a rule-based system
to find related entities, descriptors and modifiers within a sentence. Sentence
tokens are represented by an undirected graphical model, and rules are based on
part-of-speech and word dependency patterns connecting values and units to
contextual words. Marve is unique in its focus on measurement context and early
experimentation demonstrates Marve's ability to generate high-precision
extractions with strong recall. We also discuss Marve's role in refining
measurement requirements for NASA's proposed HyspIRI mission, a hyperspectral
infrared imaging satellite that will study the world's ecosystems. In general,
our work with HyspIRI demonstrates the value of semantic measurement
extractions in characterizing quantitative discussion contained in large
corpuses of natural language text. These extractions accelerate broad,
cross-cutting research and expose scientists new algorithmic approaches and
experimental nuances. They also facilitate identification of scientific
opportunities enabled by HyspIRI leading to more efficient scientific
investment and research.
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
Authors: Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi
Date: 2017-10-04 00:00:00
Tasks: Privacy Preserving

Deep neural networks are increasingly being used in a variety of machine
learning applications applied to rich user data on the cloud. However, this
approach introduces a number of privacy and efficiency challenges, as the cloud
operator can perform secondary inferences on the available data. Recently,
advances in edge processing have paved the way for more efficient, and private,
data processing at the source for simple tasks and lighter models, though they
remain a challenge for larger, and more complicated models. In this paper, we
present a hybrid approach for breaking down large, complex deep models for
cooperative, privacy-preserving analytics. We do this by breaking down the
popular deep architectures and fine-tune them in a particular way. We then
evaluate the privacy benefits of this approach based on the information exposed
to the cloud service. We also asses the local inference cost of different
layers on a modern handset for mobile applications. Our evaluations show that
by using certain kind of fine-tuning and embedding techniques and at a small
processing costs, we can greatly reduce the level of information available to
unintended tasks applied to the data feature on the cloud, and hence achieving
the desired tradeoff between privacy and performance.
Solutions of Quadratic First-Order ODEs applied to Computer Vision Problems
Authors: David Casillas-Perez, Daniel Pizarro, Manuel Mazo, Adrien Bartoli
Date: 2017-10-11 00:00:00

This article is a study about the existence and the uniqueness of solutions
of a specific quadratic first-order ODE that frequently appears in multiple
reconstruction problems. It is called the \emph{planar-perspective equation}
due to the duality with the geometric problem of reconstruction of
planar-perspective curves from their modulus. Solutions of the
\emph{planar-perspective equation} are related with planar curves parametrized
with perspective parametrization due to this geometric interpretation. The
article proves the existence of only two local solutions to the \emph{initial
value problem} with \emph{regular initial conditions} and a maximum of two
analytic solutions with \emph{critical initial conditions}. The article also
gives theorems to extend the local definition domain where the existence of
both solutions are guaranteed. It introduces the \emph{maximal depth function}
as a function that upper-bound all possible solutions of the
\emph{planar-perspective equation} and contains all its possible \emph{critical
points}. Finally, the article describes the \emph{maximal-depth solution
problem} that consists of finding the solution of the referred equation that
has maximum the depth and proves its uniqueness. It is an important problem as
it does not need initial conditions to obtain the unique solution and its the
frequent solution that practical algorithms of the state-of-the-art give.
Local Convergence of Proximal Splitting Methods for Rank Constrained Problems
Authors: Christian Grussler, Pontus Giselsson
Date: 2017-10-11 00:00:00

We analyze the local convergence of proximal splitting algorithms to solve
optimization problems that are convex besides a rank constraint. For this, we
show conditions under which the proximal operator of a function involving the
rank constraint is locally identical to the proximal operator of its convex
envelope, hence implying local convergence. The conditions imply that the
non-convex algorithms locally converge to a solution whenever a convex
relaxation involving the convex envelope can be expected to solve the
non-convex problem.
Mining Frequent Patterns in Process Models
Authors: David Chapela-Campa, Manuel Mucientes, Manuel Lama
Date: 2017-10-11 00:00:00

Process mining has emerged as a way to analyze the behavior of an
organization by extracting knowledge from event logs and by offering techniques
to discover, monitor and enhance real processes. In the discovery of process
models, retrieving a complex one, i.e., a hardly readable process model, can
hinder the extraction of information. Even in well-structured process models,
there is information that cannot be obtained with the current techniques. In
this paper, we present WoMine, an algorithm to retrieve frequent behavioural
patterns from the model. Our approach searches in process models extracting
structures with sequences, selections, parallels and loops, which are
frequently executed in the logs. This proposal has been validated with a set of
process models, including some from BPI Challenges, and compared with the state
of the art techniques. Experiments have validated that WoMine can find all
types of patterns, extracting information that cannot be mined with the state
of the art techniques.
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
Authors: Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
Date: 2017-10-11 00:00:00
Proceeding: NeurIPS 2017 12

We prove near-tight concentration of measure for polynomial functions of the
Ising model under high temperature. For any degree $d$, we show that a
degree-$d$ polynomial of a $n$-spin Ising model exhibits exponential tails that
scale as $\exp(-r^{2/d})$ at radius $r=\tilde{\Omega}_d(n^{d/2})$. Our
concentration radius is optimal up to logarithmic factors for constant $d$,
improving known results by polynomial factors in the number of spins. We
demonstrate the efficacy of polynomial functions as statistics for testing the
strength of interactions in social networks in both synthetic and real world
data.
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism
Authors: Adam Stooke, Pieter Abbeel
Date: 2017-10-11 00:00:00
Tasks: GPU

We present Synkhronos, an extension to Theano for multi-GPU computations
leveraging data parallelism. Our framework provides automated execution and
synchronization across devices, allowing users to continue to write serial
programs without risk of race conditions. The NVIDIA Collective Communication
Library is used for high-bandwidth inter-GPU communication. Further
enhancements to the Theano function interface include input slicing (with
aggregation) and input indexing, which perform common data-parallel computation
patterns efficiently. One example use case is synchronous SGD, which has
recently been shown to scale well for a growing set of deep learning problems.
When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA
DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in
isolation. Yet Synkhronos remains general to any data-parallel computation
programmable in Theano. By implementing parallelism at the level of individual
Theano functions, our framework uniquely addresses a niche between manual
multi-device programming and prescribed multi-GPU training routines.
Neural Program Meta-Induction
Authors: Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli
Date: 2017-10-11 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Program induction, Transfer Learning

Most recently proposed methods for Neural Program Induction work under the
assumption of having a large set of input/output (I/O) examples for learning
any underlying input-output mapping. This paper aims to address the problem of
data and computation efficiency of program induction by leveraging information
from related tasks. Specifically, we propose two approaches for cross-task
knowledge transfer to improve program induction in limited-data scenarios. In
our first proposal, portfolio adaptation, a set of induction models is
pretrained on a set of related tasks, and the best model is adapted towards the
new task using transfer learning. In our second approach, meta program
induction, a $k$-shot learning approach is used to make a model generalize to
new tasks without additional training. To test the efficacy of our methods, we
constructed a new benchmark of programs written in the Karel programming
language. Using an extensive experimental evaluation on the Karel benchmark, we
demonstrate that our proposals dramatically outperform the baseline induction
method that does not use knowledge transfer. We also analyze the relative
performance of the two approaches and study conditions in which they perform
best. In particular, meta induction outperforms all existing approaches under
extreme data sparsity (when a very small number of examples are available),
i.e., fewer than ten. As the number of available I/O examples increase (i.e. a
thousand or more), portfolio adapted program induction becomes the best
approach. For intermediate data sizes, we demonstrate that the combined method
of adapted meta program induction has the strongest performance.
Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey
Authors: Feiyun Zhu
Date: 2017-08-17 00:00:00
Tasks: Hyperspectral Unmixing, Survey

Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground truths of most hyperspectral images are not shared on the web,
which may cause lots of unnecessary troubles for researchers to evaluate their
algorithms; (c) the codes of most state-of-the-art methods are not shared,
which may also delay the testing of new methods.
  Accordingly, this paper deals with the above issues from the following three
perspectives: (1) as a profound contribution, we provide a general labeling
method for the HU. With it, we labeled up to 15 hyperspectral images, providing
18 versions of ground truths. To the best of our knowledge, this is the first
paper to summarize and share up to 15 hyperspectral images and their 18
versions of ground truths for the HU. Observing that the hyperspectral
classification (HyC) has much more standard datasets (whose ground truths are
generally publicly shared) than the HU, we propose an interesting method to
transform the HyC datasets for the HU research. (2) To further facilitate the
evaluation of HU methods under different conditions, we reviewed and
implemented the algorithm to generate a complex synthetic hyperspectral image.
By tuning the hyper-parameters in the code, we may verify the HU methods from
four perspectives. The code would also be shared on the web. (3) To provide a
standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then
selected the 5 most benchmark HU algorithms, and compared them on the 15 real
hyperspectral datasets. The experiment results are surely reproducible; the
implemented codes would be shared on the web.
Bollywood Movie Corpus for Text, Images and Videos
Authors: Nishtha Madaan, Sameep Mehta, Mayank Saxena, Aditi Aggarwal, Taneea S Agrawaal, Vrinda Malhotra
Date: 2017-10-11 00:00:00

In past few years, several data-sets have been released for text and images.
We present an approach to create the data-set for use in detecting and removing
gender bias from text. We also include a set of challenges we have faced while
creating this corpora. In this work, we have worked with movie data from
Wikipedia plots and movie trailers from YouTube. Our Bollywood Movie corpus
contains 4000 movies extracted from Wikipedia and 880 trailers extracted from
YouTube which were released from 1970-2017. The corpus contains csv files with
the following data about each movie - Wikipedia title of movie, cast, plot
text, co-referenced plot text, soundtrack information, link to movie poster,
caption of movie poster, number of males in poster, number of females in
poster. In addition to that, corresponding to each cast member the following
data is available - cast name, cast gender, cast verbs, cast adjectives, cast
relations, cast centrality, cast mentions. We present some preliminary results
on the task of bias removal which suggest that the data-set is quite useful for
performing such tasks.