pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic State Warping
Authors: Zhichen Gong, Huanhuan Chen
Date: 2017-03-03 00:00:00
Tasks: Dynamic Time Warping

The ubiquity of sequences in many domains enhances significant recent
interest in sequence learning, for which a basic problem is how to measure the
distance between sequences. Dynamic time warping (DTW) aligns two sequences by
nonlinear local warping and returns a distance value. DTW shows superior
ability in many applications, e.g. video, image, etc. However, in DTW, two
points are paired essentially based on point-to-point Euclidean distance (ED)
without considering the autocorrelation of sequences. Thus, points with
different semantic meanings, e.g. peaks and valleys, may be matched providing
their coordinate values are similar. As a result, DTW is sensitive to noise and
poorly interpretable. This paper proposes an efficient and flexible sequence
alignment algorithm, dynamic state warping (DSW). DSW converts each time point
into a latent state, which endows point-wise autocorrelation information.
Alignment is performed by using the state sequences. Thus DSW is able to yield
alignment that is semantically more interpretable than that of DTW. Using one
nearest neighbor classifier, DSW shows significant improvement on
classification accuracy in comparison to ED (70/85 wins) and DTW (74/85 wins).
We also empirically demonstrate that DSW is more robust and scales better to
long sequences than ED and DTW.
Deep artifact learning for compressed sensing and parallel MRI
Authors: Dongwook Lee, Jaejun Yoo, Jong Chul Ye
Date: 2017-03-03 00:00:00
Tasks: compressed sensing

Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is
one of the powerful ways to reduce the scan time of MR imaging with performance
guarantee. However, the computational costs are usually expensive. This paper
aims to propose a computationally fast and accurate deep learning algorithm for
the reconstruction of MR images from highly down-sampled k-space data.
  Theory: Based on the topological analysis, we show that the data manifold of
the aliasing artifact is easier to learn from a uniform subsampling pattern
with additional low-frequency k-space data. Thus, we develop deep aliasing
artifact learning networks for the magnitude and phase images to estimate and
remove the aliasing artifacts from highly accelerated MR acquisition.
  Methods: The aliasing artifacts are directly estimated from the distorted
magnitude and phase images reconstructed from subsampled k-space data so that
we can get an aliasing-free images by subtracting the estimated aliasing
artifact from corrupted inputs. Moreover, to deal with the globally distributed
aliasing artifact, we develop a multi-scale deep neural network with a large
receptive field.
  Results: The experimental results confirm that the proposed deep artifact
learning network effectively estimates and removes the aliasing artifacts.
Compared to existing CS methods from single and multi-coli data, the proposed
network shows minimal errors by removing the coherent aliasing artifacts.
Furthermore, the computational time is by order of magnitude faster.
  Conclusion: As the proposed deep artifact learning network immediately
generates accurate reconstruction, it has great potential for clinical
applications.
Likelihood-free inference via classification
Authors: Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander
Date: 2014-07-18 00:00:00
Tasks: Bayesian Inference, Classification, General Classification

Increasingly complex generative models are being used across disciplines as
they allow for realistic characterization of data, but a common difficulty with
them is the prohibitively large computational cost to evaluate the likelihood
function and thus to perform likelihood-based statistical inference. A
likelihood-free inference framework has emerged where the parameters are
identified by finding values that yield simulated data resembling the observed
data. While widely applicable, a major difficulty in this framework is how to
measure the discrepancy between the simulated and observed data. Transforming
the original problem into a problem of classifying the data into simulated
versus observed, we find that classification accuracy can be used to assess the
discrepancy. The complete arsenal of classification methods becomes thereby
available for inference of intractable generative models. We validate our
approach using theory and simulations for both point estimation and Bayesian
inference, and demonstrate its use on real data by inferring an
individual-based epidemiological model for bacterial infections in child care
centers.
Adversarial Examples for Semantic Image Segmentation
Authors: Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen, Thomas Brox
Date: 2017-03-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

Machine learning methods in general and Deep Neural Networks in particular
have shown to be vulnerable to adversarial perturbations. So far this
phenomenon has mainly been studied in the context of whole-image
classification. In this contribution, we analyse how adversarial perturbations
can affect the task of semantic segmentation. We show how existing adversarial
attackers can be transferred to this task and that it is possible to create
imperceptible adversarial perturbations that lead a deep network to misclassify
almost all pixels of a chosen class while leaving network prediction nearly
unchanged outside this class.
Texture and Color-based Image Retrieval Using the Local Extrema Features and Riemannian Distance
Authors: Minh-Tan Pham, Grégoire Mercier, Lionel Bombrun, Julien Michel
Date: 2016-11-07 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

A novel efficient method for content-based image retrieval (CBIR) is
developed in this paper using both texture and color features. Our motivation
is to represent and characterize an input image by a set of local descriptors
extracted at characteristic points (i.e. keypoints) within the image. Then,
dissimilarity measure between images is calculated based on the geometric
distance between the topological feature spaces (i.e. manifolds) formed by the
sets of local descriptors generated from these images. In this work, we propose
to extract and use the local extrema pixels as our feature points. Then, the
so-called local extrema-based descriptor (LED) is generated for each keypoint
by integrating all color, spatial as well as gradient information captured by a
set of its nearest local extrema. Hence, each image is encoded by a LED feature
point cloud and riemannian distances between these point clouds enable us to
tackle CBIR. Experiments performed on Vistex, Stex and colored Brodatz texture
databases using the proposed approach provide very efficient and competitive
results compared to the state-of-the-art methods.
Sequential Plan Recognition
Authors: Reuth Mirsky, Roni Stern, Ya'akov, Gal, Meir Kalech
Date: 2017-03-03 00:00:00

Plan recognition algorithms infer agents' plans from their observed actions.
Due to imperfect knowledge about the agent's behavior and the environment, it
is often the case that there are multiple hypotheses about an agent's plans
that are consistent with the observations, though only one of these hypotheses
is correct. This paper addresses the problem of how to disambiguate between
hypotheses, by querying the acting agent about whether a candidate plan in one
of the hypotheses matches its intentions. This process is performed
sequentially and used to update the set of possible hypotheses during the
recognition process. The paper defines the sequential plan recognition process
(SPRP), which seeks to reduce the number of hypotheses using a minimal number
of queries. We propose a number of policies for the SPRP which use maximum
likelihood and information gain to choose which plan to query. We show this
approach works well in practice on two domains from the literature,
significantly reducing the number of hypotheses using fewer queries than a
baseline approach. Our results can inform the design of future plan recognition
systems that interleave the recognition process with intelligent interventions
of their users.
Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables
Authors: Jean Daunizeau
Date: 2017-03-01 00:00:00
Tasks: Clustering, Decision Making

This note is concerned with accurate and computationally efficient
approximations of moments of Gaussian random variables passed through sigmoid
or softmax mappings. These approximations are semi-analytical (i.e. they
involve the numerical adjustment of parametric forms) and highly accurate (they
yield 5% error at most). We also highlight a few niche applications of these
approximations, which arise in the context of, e.g., drift-diffusion models of
decision making or non-parametric data clustering approaches. We provide these
as examples of efficient alternatives to more tedious derivations that would be
needed if one was to approach the underlying mathematical issues in a more
formal way. We hope that this technical note will be helpful to modellers
facing similar mathematical issues, although maybe stemming from different
academic prospects.
Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
Authors: Jia Su, Bin He, Yi Guan, Jingchi Jiang, Jinfeng Yang
Date: 2016-11-28 00:00:00

Cardiovascular disease (CVD) has become the leading cause of death in China,
and most of the cases can be prevented by controlling risk factors. The goal of
this study was to build a corpus of CVD risk factor annotations based on
Chinese electronic medical records (CEMRs). This corpus is intended to be used
to develop a risk factor information extraction system that, in turn, can be
applied as a foundation for the further study of the progress of risk factors
and CVD. We designed a light annotation task to capture CVD risk factors with
indicators, temporal attributes and assertions that were explicitly or
implicitly displayed in the records. The task included: 1) preparing data; 2)
creating guidelines for capturing annotations (these were created with the help
of clinicians); 3) proposing an annotation method including building the
guidelines draft, training the annotators and updating the guidelines, and
corpus construction. Then, a risk factor annotated corpus based on
de-identified discharge summaries and progress notes from 600 patients was
developed. Built with the help of clinicians, this corpus has an
inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high
reliability. To the best of our knowledge, this is the first annotated corpus
concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk
factor annotations from CEMRs were proposed. The obtained document-level
annotations can be applied in future studies to monitor risk factors and CVD
over the long term.
Skin Lesion Classification using Class Activation Map
Authors: Xi Jia, Linlin Shen
Date: 2017-03-03 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Skin Lesion Classification

We proposed a two stage framework with only one network to analyze skin
lesion images, we firstly trained a convolutional network to classify these
images, and cropped the import regions which the network has the maximum
activation value. In the second stage, we retrained this CNN with the image
regions extracted from stage one and output the final probabilities. The two
stage framework achieved a mean AUC of 0.857 in ISIC-2017 skin lesion
validation set and is 0.04 higher than that of the original inputs, 0.821.
PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned Stochastic Neighbor Embedding
Authors: Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, Jaegul Choo
Date: 2016-11-08 00:00:00

Embedding and visualizing large-scale high-dimensional data in a
two-dimensional space is an important problem since such visualization can
reveal deep insights out of complex data. Most of the existing embedding
approaches, however, run on an excessively high precision, ignoring the fact
that at the end, embedding outputs are converted into coarse-grained discrete
pixel coordinates in a screen space. Motivated by such an observation and
directly considering pixel coordinates in an embedding optimization process, we
accelerate Barnes-Hut tree-based t-distributed stochastic neighbor embedding
(BH-SNE), known as a state-of-the-art 2D embedding method, and propose a novel
method called PixelSNE, a highly-efficient, screen resolution-driven 2D
embedding method with a linear computational complexity in terms of the number
of data items. Our experimental results show the significantly fast running
time of PixelSNE by a large margin against BH-SNE, while maintaining the
minimal degradation in the embedding quality. Finally, the source code of our
method is publicly available at https://github.com/awesome-davian/PixelSNE
Expectile Matrix Factorization for Skewed Data Analysis
Authors: Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li
Date: 2016-06-07 00:00:00

Matrix factorization is a popular approach to solving matrix estimation
problems based on partial observations. Existing matrix factorization is based
on least squares and aims to yield a low-rank matrix to interpret the
conditional sample means given the observations. However, in many real
applications with skewed and extreme data, least squares cannot explain their
central tendency or tail distributions, yielding undesired estimates. In this
paper, we propose \emph{expectile matrix factorization} by introducing
asymmetric least squares, a key concept in expectile regression analysis, into
the matrix factorization framework. We propose an efficient algorithm to solve
the new problem based on alternating minimization and quadratic programming. We
prove that our algorithm converges to a global optimum and exactly recovers the
true underlying low-rank matrices when noise is zero. For synthetic data with
skewed noise and a real-world dataset containing web service response times,
the proposed scheme achieves lower recovery errors than the existing matrix
factorization method based on least squares in a wide range of settings.
Information-theoretic limits of Bayesian network structure learning
Authors: Asish Ghoshal, Jean Honorio
Date: 2016-01-27 00:00:00
Tasks: regression, Variable Selection

In this paper, we study the information-theoretic limits of learning the
structure of Bayesian networks (BNs), on discrete as well as continuous random
variables, from a finite number of samples. We show that the minimum number of
samples required by any procedure to recover the correct structure grows as
$\Omega(m)$ and $\Omega(k \log m + (k^2/m))$ for non-sparse and sparse BNs
respectively, where $m$ is the number of variables and $k$ is the maximum
number of parents per node. We provide a simple recipe, based on an extension
of the Fano's inequality, to obtain information-theoretic limits of structure
recovery for any exponential family BN. We instantiate our result for specific
conditional distributions in the exponential family to characterize the
fundamental limits of learning various commonly used BNs, such as conditional
probability table based networks, gaussian BNs, noisy-OR networks, and logistic
regression networks. En route to obtaining our main results, we obtain tight
bounds on the number of sparse and non-sparse essential-DAGs. Finally, as a
byproduct, we recover the information-theoretic limits of sparse variable
selection for logistic regression.
Diverse Neural Network Learns True Target Functions
Authors: Bo Xie, YIngyu Liang, Le Song
Date: 2016-11-09 00:00:00
Tasks: Diversity, Relation Linking

Neural networks are a powerful class of functions that can be trained with
simple gradient descent to achieve state-of-the-art performance on a variety of
applications. Despite their practical success, there is a paucity of results
that provide theoretical guarantees on why they are so effective. Lying in the
center of the problem is the difficulty of analyzing the non-convex loss
function with potentially numerous local minima and saddle points. Can neural
networks corresponding to the stationary points of the loss function learn the
true target function? If yes, what are the key factors contributing to such
nice optimization properties?
  In this paper, we answer these questions by analyzing one-hidden-layer neural
networks with ReLU activation, and show that despite the non-convexity, neural
networks with diverse units have no spurious local minima. We bypass the
non-convexity issue by directly analyzing the first order optimality condition,
and show that the loss can be made arbitrarily small if the minimum singular
value of the "extended feature matrix" is large enough. We make novel use of
techniques from kernel methods and geometric discrepancy, and identify a new
relation linking the smallest singular value to the spectrum of a kernel
function associated with the activation function and to the diversity of the
units. Our results also suggest a novel regularization function to promote unit
diversity for potentially better generalization.
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Authors: Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
Date: 2017-03-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Decision Making, Dependency Parsing, Imitation Learning, Prediction, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Researchers have demonstrated state-of-the-art performance in sequential
decision making problems (e.g., robotics control, sequential prediction) with
deep neural network models. One often has access to near-optimal oracles that
achieve good performance on the task during training. We demonstrate that
AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL)
approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve
faster and better solutions with less training data than a less-informed
Reinforcement Learning (RL) technique. Using both feedforward and recurrent
neural network predictors, we present stochastic gradient procedures on a
sequential prediction task, dependency-parsing from raw image data, as well as
on various high dimensional robotics control problems. We also provide a
comprehensive theoretical study of IL that demonstrates we can expect up to
exponentially lower sample complexity for learning with AggreVaTeD than with RL
algorithms, which backs our empirical findings. Our results and theory indicate
that the proposed approach can achieve superior performance with respect to the
oracle when the demonstrator is sub-optimal.
Outlier Cluster Formation in Spectral Clustering
Authors: Takuro Ina, Atsushi Hashimoto, Masaaki Iiyama, Hidekazu Kasahara, Mikihiko Mori, Michihiko Minoh
Date: 2017-03-03 00:00:00
Tasks: Clustering, Face Clustering, Outlier Detection, Person Re-Identification, valid

Outlier detection and cluster number estimation is an important issue for
clustering real data. This paper focuses on spectral clustering, a time-tested
clustering method, and reveals its important properties related to outliers.
The highlights of this paper are the following two mathematical observations:
first, spectral clustering's intrinsic property of an outlier cluster
formation, and second, the singularity of an outlier cluster with a valid
cluster number. Based on these observations, we designed a function that
evaluates clustering and outlier detection results. In experiments, we prepared
two scenarios, face clustering in photo album and person re-identification in a
camera network. We confirmed that the proposed method detects outliers and
estimates the number of clusters properly in both problems. Our method
outperforms state-of-the-art methods in both the 128-dimensional sparse space
for face clustering and the 4,096-dimensional non-sparse space for person
re-identification.
Unsupervised Basis Function Adaptation for Reinforcement Learning
Authors: Edward W. Barker, Charl J. Ras
Date: 2017-03-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining a suitable approximation architecture can often be a highly complex
task. Consequently there is a large amount of interest in the potential for
allowing RL algorithms to adaptively generate approximation architectures.
  We investigate a method of adapting approximation architectures which uses
feedback regarding the frequency with which an agent has visited certain states
to guide which areas of the state space to approximate with greater detail.
This method is "unsupervised" in the sense that it makes no direct reference to
reward or the VF estimate. We introduce an algorithm based upon this idea which
adapts a state aggregation approximation architecture on-line.
  A common method of scoring a VF estimate is to weight the squared Bellman
error of each state-action by the probability of that state-action occurring.
Adopting this scoring method, and assuming $S$ states, we demonstrate
theoretically that - provided (1) the number of cells $X$ in the state
aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2)
the policy and transition function are close to deterministic, and (3) the
prior for the transition function is uniformly distributed - our algorithm,
used in conjunction with a suitable RL algorithm, can guarantee a score which
is arbitrarily close to zero as $S$ becomes large. It is able to do this
despite having only $O(X \log_2S)$ space complexity and negligible time
complexity. The results take advantage of certain properties of the stationary
distributions of Markov chains.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
Authors: Xulei Yang, Zeng Zeng, Si Yong Yeo, Colin Tan, Hong Liang Tey, Yi Su
Date: 2017-03-03 00:00:00
Tasks: General Classification, Lesion Segmentation, Multi-Task Learning, Segmentation, Skin Lesion Segmentation

In this study, a multi-task deep neural network is proposed for skin lesion
analysis. The proposed multi-task learning model solves different tasks (e.g.,
lesion segmentation and two independent binary lesion classifications) at the
same time by exploiting commonalities and differences across tasks. This
results in improved learning efficiency and potential prediction accuracy for
the task-specific models, when compared to training the individual models
separately. The proposed multi-task deep learning model is trained and
evaluated on the dermoscopic image sets from the International Skin Imaging
Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma
Detection, which consists of 2000 training samples and 150 evaluation samples.
The experimental results show that the proposed multi-task deep learning model
achieves promising performances on skin lesion segmentation and classification.
The average value of Jaccard index for lesion segmentation is 0.724, while the
average values of area under the receiver operating characteristic curve (AUC)
on two individual lesion classifications are 0.880 and 0.972, respectively.