pwc-archive/papers-with-abstracts huggingface dataset:
Collaboratively Learning the Best Option, Using Bounded Memory
Authors: Lili Su, Martin Zubeldia, Nancy Lynch
Date: 2018-02-22 00:00:00

We consider multi-armed bandit problems in social groups wherein each
individual has bounded memory and shares the common goal of learning the best
arm/option. We say an individual learns the best option if eventually (as $t
\to \infty$) it pulls only the arm with the highest average reward. While this
goal is provably impossible for an isolated individual, we show that, in social
groups, this goal can be achieved easily with the aid of social persuasion,
i.e., communication. Specifically, we study the learning dynamics wherein an
individual sequentially decides on which arm to pull next based on not only its
private reward feedback but also the suggestions provided by randomly chosen
peers. Our learning dynamics are hard to analyze via explicit probabilistic
calculations due to the stochastic dependency induced by social interaction.
Instead, we employ the mean-field approximation method from statistical physics
and we show:
  (1) With probability $\to 1$ as the social group size $N \to \infty $, every
individual in the social group learns the best option.
  (2) Over an arbitrary finite time horizon $[0, T]$, with high probability (in
$N$), the fraction of individuals that prefer the best option grows to 1
exponentially fast as $t$ increases ($t\in [0, T]$).
  A major innovation of our mean-filed analysis is a simple yet powerful
technique to deal with absorbing states in the interchange of limits $N \to
\infty$ and $t \to \infty $. The mean-field approximation method allows us to
approximate the probabilistic sample paths of our learning dynamics by a
deterministic and smooth trajectory that corresponds to the unique solution of
a well-behaved system of ordinary differential equations (ODEs). Such an
approximation is desired because the analysis of a system of ODEs is relatively
easier than that of the original stochastic system.
HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing
Authors: Victor Stefan Aldea
Date: 2014-12-08 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Image Compression, regression

Sparse regression methods have been proven effective in a wide range of
signal processing problems such as image compression, speech coding, channel
equalization, linear regression and classification. In this paper a new convex
method of hyperspectral image classification is developed based on the sparse
unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization
term is introduced. To further enhance class separability, the algorithm is
kernelized using an RBF kernel and the final results are improved by a
combination of spatial pre and post-processing operations. It is shown that the
proposed method is competitive with state of the art algorithms such as SVM-CK,
KSOMP-CK and KSSP-CK.
CNN-Based Automatic Urinary Particles Recognition
Authors: Rui Kang, Yixiong Liang, Chunyan Lian, Yuan Mao
Date: 2018-03-06 00:00:00
Tasks: GPU, Object, object-detection, Object Detection

The urine sediment analysis of particles in microscopic images can assist
physicians in evaluating patients with renal and urinary tract diseases. Manual
urine sediment examination is labor-intensive, subjective and time-consuming,
and the traditional automatic algorithms often extract the hand-crafted
features for recognition. Instead of using the hand-crafted features, in this
paper, we exploit CNN to learn features in an end-to-end manner to recognize
the urine particles. We treat the urine particles recognition as object
detection and exploit two state-of-the-art CNN-based object detection methods,
Faster R-CNN and SSD, as well as their variants for urine particles
recognition. We further investigate different factors involving these CNN-based
object detection methods for urine particles recognition. We comprehensively
evaluate these methods on a dataset consisting of 5,376 annotated images
corresponding to 7 categories of urine particles, i.e., erythrocyte, leukocyte,
epithelial cell, crystal, cast, mycete, epithelial nuclei, and obtain a best
mAP (mean average precision) of 84.1% while taking only 72 ms per image on a
NVIDIA Titan X GPU.
Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction
Authors: Hoyeon Lee, Jongha Lee, Hyeongseok Kim, Byungchul Cho, Seungryong Cho
Date: 2018-03-02 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have
been developed and deployed in commercialized CT scanners. Tube current
reduction is perhaps the most actively explored technology with advanced image
reconstruction algorithms. Sparse data sampling is another viable option to the
low-dose CT, and sparse-view CT has been particularly of interest among the
researchers in CT community. Since analytic image reconstruction algorithms
would lead to severe image artifacts, various iterative algorithms have been
developed for reconstructing images from sparsely view-sampled projection data.
However, iterative algorithms take much longer computation time than the
analytic algorithms, and images are usually prone to different types of image
artifacts that heavily depend on the reconstruction parameters. Interpolation
methods have also been utilized to fill the missing data in the sinogram of
sparse-view CT thus providing synthetically full data for analytic image
reconstruction. In this work, we introduce a deep-neural-network-enabled
sinogram synthesis method for sparse-view CT, and show its outperformance to
the existing interpolation methods and also to the iterative image
reconstruction approach.
Nonparametric Estimation of Low Rank Matrix Valued Function
Authors: Fan Zhou
Date: 2018-02-17 00:00:00
Tasks: Matrix Completion, Model Selection

Let $A:[0,1]\rightarrow\mathbb{H}_m$ (the space of Hermitian matrices) be a
matrix valued function which is low rank with entries in H\"{o}lder class
$\Sigma(\beta,L)$. The goal of this paper is to study statistical estimation of
$A$ based on the regression model $\mathbb{E}(Y_j|\tau_j,X_j) = \langle
A(\tau_j), X_j \rangle,$ where $\tau_j$ are i.i.d. uniformly distributed in
$[0,1]$, $X_j$ are i.i.d. matrix completion sampling matrices, $Y_j$ are
independent bounded responses. We propose an innovative nuclear norm penalized
local polynomial estimator and establish an upper bound on its point-wise risk
measured by Frobenius norm. Then we extend this estimator globally and prove an
upper bound on its integrated risk measured by $L_2$-norm. We also propose
another new estimator based on bias-reducing kernels to study the case when $A$
is not necessarily low rank and establish an upper bound on its risk measured
by $L_{\infty}$-norm. We show that the obtained rates are all optimal up to
some logarithmic factor in minimax sense. Finally, we propose an adaptive
estimation procedure based on Lepskii's method and model selection with data
splitting which is computationally efficient and can be easily implemented and
parallelized.
Deep Neural Network Compression with Single and Multiple Level Quantization
Authors: Yuhui Xu, Yongzhuang Wang, Aojun Zhou, Weiyao Lin, Hongkai Xiong
Date: 2018-03-06 00:00:00
Tasks: Neural Network Compression, Quantization

Network quantization is an effective solution to compress deep neural
networks for practical usage. Existing network quantization methods cannot
sufficiently exploit the depth information to generate low-bit compressed
network. In this paper, we propose two novel network quantization approaches,
single-level network quantization (SLQ) for high-bit quantization and
multi-level network quantization (MLQ) for extremely low-bit quantization
(ternary).We are the first to consider the network quantization from both width
and depth level. In the width level, parameters are divided into two parts: one
for quantization and the other for re-training to eliminate the quantization
loss. SLQ leverages the distribution of the parameters to improve the width
level. In the depth level, we introduce incremental layer compensation to
quantize layers iteratively which decreases the quantization loss in each
iteration. The proposed approaches are validated with extensive experiments
based on the state-of-the-art neural networks including AlexNet, VGG-16,
GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.
Navigator-free EPI Ghost Correction with Structured Low-Rank Matrix Models: New Theory and Methods
Authors: Rodrigo A. Lobos, Tae Hyung Kim, W. Scott Hoge, Justin P. Haldar
Date: 2017-08-16 00:00:00
Tasks: Image Reconstruction

Structured low-rank matrix models have previously been introduced to enable
calibrationless MR image reconstruction from sub-Nyquist data, and such ideas
have recently been extended to enable navigator-free echo-planar imaging (EPI)
ghost correction. This paper presents novel theoretical analysis which shows
that, because of uniform subsampling, the structured low-rank matrix
optimization problems for EPI data will always have either undesirable or
non-unique solutions in the absence of additional constraints. This theory
leads us to recommend and investigate problem formulations for navigator-free
EPI that incorporate side information from either image-domain or k-space
domain parallel imaging methods. The importance of using nonconvex low-rank
matrix regularization is also identified. We demonstrate using phantom and
\emph{in vivo} data that the proposed methods are able to eliminate ghost
artifacts for several navigator-free EPI acquisition schemes, obtaining better
performance in comparison to state-of-the-art methods across a range of
different scenarios. Results are shown for both single-channel acquisition and
highly accelerated multi-channel acquisition.
Learning Filter Bank Sparsifying Transforms
Authors: Luke Pfister, Yoram Bresler
Date: 2018-03-06 00:00:00
Tasks: Denoising, Image Denoising

Data is said to follow the transform (or analysis) sparsity model if it
becomes sparse when acted on by a linear operator called a sparsifying
transform. Several algorithms have been designed to learn such a transform
directly from data, and data-adaptive sparsifying transforms have demonstrated
excellent performance in signal restoration tasks. Sparsifying transforms are
typically learned using small sub-regions of data called patches, but these
algorithms often ignore redundant information shared between neighboring
patches.
  We show that many existing transform and analysis sparse representations can
be viewed as filter banks, thus linking the local properties of patch-based
model to the global properties of a convolutional model. We propose a new
transform learning framework where the sparsifying transform is an undecimated
perfect reconstruction filter bank. Unlike previous transform learning
algorithms, the filter length can be chosen independently of the number of
filter bank channels. Numerical results indicate filter bank sparsifying
transforms outperform existing patch-based transform learning for image
denoising while benefiting from additional flexibility in the design process.
An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions
Authors: Debjyoti Saharoy, Theja Tulabandhula
Date: 2018-03-06 00:00:00

We propose a new efficient online algorithm to learn the parameters governing
the purchasing behavior of a utility maximizing buyer, who responds to prices,
in a repeated interaction setting. The key feature of our algorithm is that it
can learn even non-linear buyer utility while working with arbitrary price
constraints that the seller may impose. This overcomes a major shortcoming of
previous approaches, which use unrealistic prices to learn these parameters
making them unsuitable in practice.
Thermodynamics of Restricted Boltzmann Machines and related learning dynamics
Authors: Aurélien Decelle, Giancarlo Fissore, Cyril Furtlehner
Date: 2018-03-05 00:00:00

We investigate the thermodynamic properties of a Restricted Boltzmann Machine
(RBM), a simple energy-based generative model used in the context of
unsupervised learning. Assuming the information content of this model to be
mainly reflected by the spectral properties of its weight matrix $W$, we try to
make a realistic analysis by averaging over an appropriate statistical ensemble
of RBMs.
  First, a phase diagram is derived. Otherwise similar to that of the
Sherrington- Kirkpatrick (SK) model with ferromagnetic couplings, the RBM's
phase diagram presents a ferromagnetic phase which may or may not be of
compositional type depending on the kurtosis of the distribution of the
components of the singular vectors of $W$.
  Subsequently, the learning dynamics of the RBM is studied in the
thermodynamic limit. A "typical" learning trajectory is shown to solve an
effective dynamical equation, based on the aforementioned ensemble average and
explicitly involving order parameters obtained from the thermodynamic analysis.
In particular, this let us show how the evolution of the dominant singular
values of $W$, and thus of the unstable modes, is driven by the input data. At
the beginning of the training, in which the RBM is found to operate in the
linear regime, the unstable modes reflect the dominant covariance modes of the
data. In the non-linear regime, instead, the selected modes interact and
eventually impose a matching of the order parameters to their empirical
counterparts estimated from the data.
  Finally, we illustrate our considerations by performing experiments on both
artificial and real data, showing in particular how the RBM operates in the
ferromagnetic compositional phase.
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Authors: Lian Duan, Xi Qin, Yuanhao He, Xialin Sang, Jinda Pan, Tao Xu, Jing Men, Rudolph E. Tanzi, Airong Li, Yutao Ma, Chao Zhou
Date: 2018-03-05 00:00:00
Tasks: General Classification, Heart Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Convolutional neural networks are powerful tools for image segmentation and
classification. Here, we use this method to identify and mark the heart region
of Drosophila at different developmental stages in the cross-sectional images
acquired by a custom optical coherence microscopy (OCM) system. With our
well-trained convolutional neural network model, the heart regions through
multiple heartbeat cycles can be marked with an intersection over union (IOU)
of ~86%. Various morphological and dynamical cardiac parameters can be
quantified accurately with automatically segmented heart regions. This study
demonstrates an efficient heart segmentation method to analyze OCM images of
the beating heart in Drosophila.
M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion
Authors: P. Benedetti, D. Ienco, R. Gaetano, K. Osé, R. Pensa, S. Dupuy
Date: 2018-03-05 00:00:00
Tasks: Earth Observation, Time Series, Time Series Analysis

Modern Earth Observation systems provide sensing data at different temporal
and spatial resolutions. Among optical sensors, today the Sentinel-2 program
supplies high-resolution temporal (every 5 days) and high spatial resolution
(10m) images that can be useful to monitor land cover dynamics. On the other
hand, Very High Spatial Resolution images (VHSR) are still an essential tool to
figure out land cover mapping characterized by fine spatial patterns.
Understand how to efficiently leverage these complementary sources of
information together to deal with land cover mapping is still challenging. With
the aim to tackle land cover mapping through the fusion of multi-temporal High
Spatial Resolution and Very High Spatial Resolution satellite images, we
propose an End-to-End Deep Learning framework, named M3Fusion, able to leverage
simultaneously the temporal knowledge contained in time series data as well as
the fine spatial information available in VHSR information. Experiments carried
out on the Reunion Island study area asses the quality of our proposal
considering both quantitative and qualitative aspects.
ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks
Authors: Kavita Ganesan
Date: 2018-03-05 00:00:00

Evaluation of summarization tasks is extremely crucial to determining the
quality of machine generated summaries. Over the last decade, ROUGE has become
the standard automatic evaluation measure for evaluating summarization tasks.
While ROUGE has been shown to be effective in capturing n-gram overlap between
system and human composed summaries, there are several limitations with the
existing ROUGE measures in terms of capturing synonymous concepts and coverage
of topics. Thus, often times ROUGE scores do not reflect the true quality of
summaries and prevents multi-faceted evaluation of summaries (i.e. by topics,
by overall content coverage and etc). In this paper, we introduce ROUGE 2.0,
which has several updated measures of ROUGE: ROUGE-N+Synonyms, ROUGE-Topic,
ROUGE-Topic+Synonyms, ROUGE-TopicUniq and ROUGE-TopicUniq+Synonyms; all of
which are improvements over the core ROUGE measures.
The morphospace of language networks
Authors: Luís F. Seoane, Ricard Solé
Date: 2018-03-05 00:00:00

Language can be described as a network of interacting objects with different
qualitative properties and complexity. These networks include semantic,
syntactic, or phonological levels and have been found to provide a new picture
of language complexity and its evolution. A general approach considers language
from an information theory perspective that incorporates a speaker, a hearer,
and a noisy channel. The later is often encoded in a matrix connecting the
signals used for communication with meanings to be found in the real world.
Most studies of language evolution deal in a way or another with such
theoretical contraption and explore the outcome of diverse forms of selection
on the communication matrix that somewhat optimizes communication. This
framework naturally introduces networks mediating the communicating agents, but
no systematic analysis of the underlying landscape of possible language graphs
has been developed. Here we present a detailed analysis of network properties
on a generic model of a communication code, which reveals a rather complex and
heterogeneous morphospace of language networks. Additionally, we use curated
data of English words to locate and evaluate real languages within this
language morphospace. Our findings indicate a surprisingly simple structure in
human language unless particles are introduced in the vocabulary, with the
ability of naming any other concept. These results refine and for the first
time complement with empirical data a lasting theoretical tradition around the
framework of \emph{least effort language}.
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
Authors: Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee
Date: 2018-03-05 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification

Finding parameters that minimise a loss function is at the core of many
machine learning methods. The Stochastic Gradient Descent algorithm is widely
used and delivers state of the art results for many problems. Nonetheless,
Stochastic Gradient Descent typically cannot find the global minimum, thus its
empirical effectiveness is hitherto mysterious. We derive a correspondence
between parameter inference and free energy minimisation in statistical
physics. The degree of undersampling plays the role of temperature. Analogous
to the energy-entropy competition in statistical physics, wide but shallow
minima can be optimal if the system is undersampled, as is typical in many
applications. Moreover, we show that the stochasticity in the algorithm has a
non-trivial correlation structure which systematically biases it towards wide
minima. We illustrate our argument with two prototypical models: image
classification using deep learning, and a linear neural network where we can
analytically reveal the relationship between entropy and out-of-sample error.
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Authors: Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon Kleinberg
Date: 2017-11-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has achieved many recent successes, but our
understanding of its strengths and limitations is hampered by the lack of rich
environments in which we can fully characterize optimal behavior, and
correspondingly diagnose individual actions against such a characterization.
Here we consider a family of combinatorial games, arising from work of Erdos,
Selfridge, and Spencer, and we propose their use as environments for evaluating
and comparing different approaches to reinforcement learning. These games have
a number of appealing features: they are challenging for current learning
approaches, but they form (i) a low-dimensional, simply parametrized
environment where (ii) there is a linear closed form solution for optimal
behavior from any state, and (iii) the difficulty of the game can be tuned by
changing environment parameters in an interpretable way. We use these
Erdos-Selfridge-Spencer games not only to compare different algorithms, but
test for generalization, make comparisons to supervised learning, analyse
multiagent play, and even develop a self play algorithm. Code can be found at:
https://github.com/rubai5/ESS_Game
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Authors: Pengcheng Xi, Chang Shu, Rafik Goubran
Date: 2018-03-05 00:00:00
Tasks: Anomaly Detection

Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.