pwc-archive/papers-with-abstracts huggingface dataset:
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification
Authors: Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
Date: 2018-03-07 00:00:00
Tasks: Graph Learning, valid

This paper introduces a novel graph signal processing framework for building
graph-based models from classes of filtered signals. In our framework,
graph-based modeling is formulated as a graph system identification problem,
where the goal is to learn a weighted graph (a graph Laplacian matrix) and a
graph-based filter (a function of graph Laplacian matrices). In order to solve
the proposed problem, an algorithm is developed to jointly identify a graph and
a graph-based filter (GBF) from multiple signal/data observations. Our
algorithm is valid under the assumption that GBFs are one-to-one functions. The
proposed approach can be applied to learn diffusion (heat) kernels, which are
popular in various fields for modeling diffusion processes. In addition, for
specific choices of graph-based filters, the proposed problem reduces to a
graph Laplacian estimation problem. Our experimental results demonstrate that
the proposed algorithm outperforms the current state-of-the-art methods. We
also implement our framework on a real climate dataset for modeling of
temperature signals.
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition
Authors: Wei-Ning Hsu, James Glass
Date: 2018-03-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The performance of automatic speech recognition (ASR) systems can be
significantly compromised by previously unseen conditions, which is typically
due to a mismatch between training and testing distributions. In this paper, we
address robustness by studying domain invariant features, such that domain
information becomes transparent to ASR systems, resolving the mismatch problem.
Specifically, we investigate a recent model, called the Factorized Hierarchical
Variational Autoencoder (FHVAE). FHVAEs learn to factorize sequence-level and
segment-level attributes into different latent variables without supervision.
We argue that the set of latent variables that contain segment-level
information is our desired domain invariant feature for ASR. Experiments are
conducted on Aurora-4 and CHiME-4, which demonstrate 41% and 27% absolute word
error rate reductions respectively on mismatched domains.
Pyramid Person Matching Network for Person Re-identification
Authors: Chaojie Mao, Yingming Li, Zhongfei Zhang, Yaqing Zhang, Xi Li
Date: 2018-03-07 00:00:00
Tasks: Person Re-Identification

In this work, we present a deep convolutional pyramid person matching network
(PPMN) with specially designed Pyramid Matching Module to address the problem
of person re-identification. The architecture takes a pair of RGB images as
input, and outputs a similiarity value indicating whether the two input images
represent the same person or not. Based on deep convolutional neural networks,
our approach first learns the discriminative semantic representation with the
semantic-component-aware features for persons and then employs the Pyramid
Matching Module to match the common semantic-components of persons, which is
robust to the variation of spatial scales and misalignment of locations posed
by viewpoint changes. The above two processes are jointly optimized via a
unified end-to-end deep learning scheme. Extensive experiments on several
benchmark datasets demonstrate the effectiveness of our approach against the
state-of-the-art approaches, especially on the rank-1 recognition rate.
Sparse Adversarial Perturbations for Videos
Authors: Xingxing Wei, Jun Zhu, Hang Su
Date: 2018-03-07 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Although adversarial samples of deep neural networks (DNNs) have been
intensively studied on static images, their extensions in videos are never
explored. Compared with images, attacking a video needs to consider not only
spatial cues but also temporal cues. Moreover, to improve the imperceptibility
as well as reduce the computation cost, perturbations should be added on as
fewer frames as possible, i.e., adversarial perturbations are temporally
sparse. This further motivates the propagation of perturbations, which denotes
that perturbations added on the current frame can transfer to the next frames
via their temporal interactions. Thus, no (or few) extra perturbations are
needed for these frames to misclassify them. To this end, we propose an
l2,1-norm based optimization algorithm to compute the sparse adversarial
perturbations for videos. We choose the action recognition as the targeted
task, and networks with a CNN+RNN architecture as threat models to verify our
method. Thanks to the propagation, we can compute perturbations on a shortened
version video, and then adapt them to the long version video to fool DNNs.
Experimental results on the UCF101 dataset demonstrate that even only one frame
in a video is perturbed, the fooling rate can still reach 59.7%.
A New Algorithmic Decision for Categorical Syllogisms via Caroll's Diagrams
Authors: Necla Kircali Gursoy, Ibrahim Senturk, Tahsin Oner, Arif Gursoy
Date: 2018-02-08 00:00:00
Tasks: Logical Reasoning, valid

In this paper, we deal with a calculus system SLCD (Syllogistic Logic with Carroll Diagrams), which gives a formal approach to logical reasoning with diagrams, for representations of the fundamental Aristotelian categorical propositions and show that they are closed under the syllogistic criterion of inference which is the deletion of middle term. Therefore, it is implemented to let the formalism comprise synchronically bilateral and trilateral diagrammatical appearance and a naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand as well as to use it. Consequently, we give an effective algorithm used to determine whether a syllogistic reasoning valid or not by using SLCD.
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Authors: Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Paul de Figueiredo, Sing-Hoi Sze, Mingyuan Zhou, Xiaoning Qian
Date: 2018-03-07 00:00:00
Tasks: Data Augmentation

Next-generation sequencing (NGS) to profile temporal changes in living
systems is gaining more attention for deriving better insights into the
underlying biological mechanisms compared to traditional static sequencing
experiments. Nonetheless, the majority of existing statistical tools for
analyzing NGS data lack the capability of exploiting the richer information
embedded in temporal data. Several recent tools have been developed to analyze
such data but they typically impose strict model assumptions, such as
smoothness on gene expression dynamic changes. To capture a broader range of
gene expression dynamic patterns, we develop the gamma Markov negative binomial
(GMNB) model that integrates a gamma Markov chain into a negative binomial
distribution model, allowing flexible temporal variation in NGS count data.
Using Bayes factors, GMNB enables more powerful temporal gene differential
expression analysis across different phenotypes or treatment conditions. In
addition, it naturally handles the heterogeneity of sequencing depth in
different samples, removing the need for ad-hoc normalization. Efficient Gibbs
sampling inference of the GMNB model parameters is achieved by exploiting novel
data augmentation techniques. Extensive experiments on both simulated and
real-world RNA-seq data show that GMNB outperforms existing methods in both
receiver operating characteristic (ROC) and precision-recall (PR) curves of
differential expression analysis results.
Fast Robust Methods for Singular State-Space Models
Authors: Jonathan Jonker, Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto, Sarah Webster
Date: 2018-03-07 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

State-space models are used in a wide range of time series analysis
formulations. Kalman filtering and smoothing are work-horse algorithms in these
settings. While classic algorithms assume Gaussian errors to simplify
estimation, recent advances use a broader range of optimization formulations to
allow outlier-robust estimation, as well as constraints to capture prior
information.
  Here we develop methods on state-space models where either innovations or
error covariances may be singular. These models frequently arise in navigation
(e.g. for `colored noise' models or deterministic integrals) and are ubiquitous
in auto-correlated time series models such as ARMA. We reformulate all
state-space models (singular as well as nonsinguar) as constrained convex
optimization problems, and develop an efficient algorithm for this
reformulation. The convergence rate is {\it locally linear}, with constants
that do not depend on the conditioning of the problem.
  Numerical comparisons show that the new approach outperforms competing
approaches for {\it nonsingular} models, including state of the art interior
point (IP) methods. IP methods converge at superlinear rates; we expect them to
dominate. However, the steep rate of the proposed approach (independent of
problem conditioning) combined with cheap iterations wins against IP in a
run-time comparison. We therefore suggest that the proposed approach be the
{\it default choice} for estimating state space models outside of the Gaussian
context, regardless of whether the error covariances are singular or not.
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Authors: Jiang Liu, Chenqiang Gao, Deyu Meng, Alexander G. Hauptmann
Date: 2017-12-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: Crowd Counting, Density Estimation, regression

In real-world crowd counting applications, the crowd densities vary greatly
in spatial and temporal domains. A detection based counting method will
estimate crowds accurately in low density scenes, while its reliability in
congested areas is downgraded. A regression based approach, on the other hand,
captures the general density information in crowded regions. Without knowing
the location of each person, it tends to overestimate the count in low density
areas. Thus, exclusively using either one of them is not sufficient to handle
all kinds of scenes with varying densities. To address this issue, a novel
end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density
Estimation Network) is proposed. It can adaptively decide the appropriate
counting mode for different locations on the image based on its real density
conditions. DecideNet starts with estimating the crowd density by generating
detection and regression based density maps separately. To capture inevitable
variation in densities, it incorporates an attention module, meant to
adaptively assess the reliability of the two types of estimations. The final
crowd counts are obtained with the guidance of the attention module to adopt
suitable estimations from the two kinds of density maps. Experimental results
show that our method achieves state-of-the-art performance on three challenging
crowd counting datasets.
Rigid Point Registration with Expectation Conditional Maximization
Authors: Jing Wu
Date: 2018-03-07 00:00:00
Tasks: Translation

This paper addresses the issue of matching rigid 3D object points with 2D
image points through point registration based on maximum likelihood principle
in computer simulated images. Perspective projection is necessary when
transforming 3D coordinate into 2D. The problem then recasts into a missing
data framework where unknown correspondences are handled via mixture models.
Adopting the Expectation Conditional Maximization for Point Registration
(ECMPR), two different rotation and translation optimization algorithms are
compared in this paper. We analyze in detail the associated consequences in
terms of estimation of the registration parameters theoretically and
experimentally.
Sequential Maximum Margin Classifiers for Partially Labeled Data
Authors: Elizabeth Hou, Alfred O. Hero
Date: 2018-03-07 00:00:00

In many real-world applications, data is not collected as one batch, but
sequentially over time, and often it is not possible or desirable to wait until
the data is completely gathered before analyzing it. Thus, we propose a
framework to sequentially update a maximum margin classifier by taking
advantage of the Maximum Entropy Discrimination principle. Our maximum margin
classifier allows for a kernel representation to represent large numbers of
features and can also be regularized with respect to a smooth sub-manifold,
allowing it to incorporate unlabeled observations. We compare the performance
of our classifier to its non-sequential equivalents in both simulated and real
datasets.
An Application of HodgeRank to Online Peer Assessment
Authors: Tse-Yu Lin, Yen-Lung Tsai
Date: 2018-03-07 00:00:00

Bias and heterogeneity in peer assessment can lead to the issue of unfair
scoring in the educational field. To deal with this problem, we propose a
reference ranking method for an online peer assessment system using HodgeRank.
Such a scheme provides instructors with an objective scoring reference based on
mathematics.
Action Classification via Concepts and Attributes
Authors: Amir Rosenfeld, Shimon Ullman
Date: 2016-05-25 00:00:00
Tasks: Action Classification, Action Recognition, Classification, General Classification, Temporal Action Localization

Classes in natural images tend to follow long tail distributions. This is
problematic when there are insufficient training examples for rare classes.
This effect is emphasized in compound classes, involving the conjunction of
several concepts, such as those appearing in action-recognition datasets. In
this paper, we propose to address this issue by learning how to utilize common
visual concepts which are readily available. We detect the presence of
prominent concepts in images and use them to infer the target labels instead of
using visual features directly, combining tools from vision and
natural-language processing. We validate our method on the recently introduced
HICO dataset reaching a mAP of 31.54\% and on the Stanford-40 Actions dataset,
where the proposed method outperforms that obtained by direct visual features,
obtaining an accuracy 83.12\%. Moreover, the method provides for each class a
semantically meaningful list of keywords and relevant image regions relating it
to its constituent concepts.
Exponential Discriminative Metric Embedding in Deep Learning
Authors: Bowen Wu, Zhangling Chen, Jun Wang, Huaming Wu
Date: 2018-03-07 00:00:00
Tasks: Deep Learning, Face Recognition, Face Verification, Metric Learning, Object, Object Recognition

With the remarkable success achieved by the Convolutional Neural Networks
(CNNs) in object recognition recently, deep learning is being widely used in
the computer vision community. Deep Metric Learning (DML), integrating deep
learning with conventional metric learning, has set new records in many fields,
especially in classification task. In this paper, we propose a replicable DML
method, called Include and Exclude (IE) loss, to force the distance between a
sample and its designated class center away from the mean distance of this
sample to other class centers with a large margin in the exponential feature
projection space. With the supervision of IE loss, we can train CNNs to enhance
the intra-class compactness and inter-class separability, leading to great
improvements on several public datasets ranging from object recognition to face
verification. We conduct a comparative study of our algorithm with several
typical DML methods on three kinds of networks with different capacity.
Extensive experiments on three object recognition datasets and two face
recognition datasets demonstrate that IE loss is always superior to other
mainstream DML methods and approach the state-of-the-art results.
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Authors: Dan Ma, Bin Liu, Zhao Kang, Jiayu Zhou, Jianke Zhu, Zenglin Xu
Date: 2017-11-16 00:00:00
Tasks: Image Generation

Generating high fidelity identity-preserving faces with different facial
attributes has a wide range of applications. Although a number of generative
models have been developed to tackle this problem, there is still much room for
further improvement.In paticular, the current solutions usually ignore the
perceptual information of images, which we argue that it benefits the output of
a high-quality image while preserving the identity information, especially in
facial attributes learning area.To this end, we propose to train GAN
iteratively via regularizing the min-max process with an integrated loss, which
includes not only the per-pixel loss but also the perceptual loss. In contrast
to the existing methods only deal with either image generation or
transformation, our proposed iterative architecture can achieve both of them.
Experiments on the multi-label facial dataset CelebA demonstrate that the
proposed model has excellent performance on recognizing multiple attributes,
generating a high-quality image, and transforming image with controllable
attributes.
Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts
Authors: Gao Tang, Kris Hauser
Date: 2018-03-07 00:00:00
Tasks: Mixture-of-Experts, Model Predictive Control, Reinforcement Learning

This paper proposes a discontinuity-sensitive approach to learn the solutions of parametric optimal control problems with high accuracy. Many tasks, ranging from model predictive control to reinforcement learning, may be solved by learning optimal solutions as a function of problem parameters. However, nonconvexity, discrete homotopy classes, and control switching cause discontinuity in the parameter-solution mapping, thus making learning difficult for traditional continuous function approximators. A mixture of experts (MoE) model composed of a classifier and several regressors is proposed to address such an issue. The optimal trajectories of different parameters are clustered such that in each cluster the trajectories are continuous function of problem parameters. Numerical examples on benchmark problems show that training the classifier and regressors individually outperforms joint training of MoE. With suitably chosen clusters, this approach not only achieves lower prediction error with less training data and fewer model parameters, but also leads to dramatic improvements in the reliability of trajectory tracking compared to traditional universal function approximation models (e.g., neural networks).
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
Authors: Ke Yan, Le Lu, Ronald M. Summers
Date: 2017-07-12 00:00:00
Tasks: Anomaly Detection, regression

Automatic body part recognition for CT slices can benefit various medical
image applications. Recent deep learning methods demonstrate promising
performance, with the requirement of large amounts of labeled images for
training. The intrinsic structural or superior-inferior slice ordering
information in CT volumes is not fully exploited. In this paper, we propose a
convolutional neural network (CNN) based Unsupervised Body part Regression
(UBR) algorithm to address this problem. A novel unsupervised learning method
and two inter-sample CNN loss functions are presented. Distinct from previous
work, UBR builds a coordinate system for the human body and outputs a
continuous score for each axial slice, representing the normalized position of
the body part in the slice. The training process of UBR resembles a
self-organization process: slice scores are learned from inter-slice
relationships. The training samples are unlabeled CT volumes that are abundant,
thus no extra annotation effort is needed. UBR is simple, fast, and accurate.
Quantitative and qualitative experiments validate its effectiveness. In
addition, we show two applications of UBR in network initialization and anomaly
detection.
An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics
Authors: Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig
Date: 2017-09-13 00:00:00

This paper presents a learning-based approach for impromptu trajectory
tracking for non-minimum phase systems, i.e., systems with unstable inverse
dynamics. Inversion-based feedforward approaches are commonly used for
improving tracking performance; however, these approaches are not directly
applicable to non-minimum phase systems due to their inherent instability. In
order to resolve the instability issue, existing methods have assumed that the
system model is known and used pre-actuation or inverse approximation
techniques. In this work, we propose an approach for learning a stable,
approximate inverse of a non-minimum phase baseline system directly from its
input-output data. Through theoretical discussions, simulations, and
experiments on two different platforms, we show the stability of our proposed
approach and its effectiveness for high-accuracy, impromptu tracking. Our
approach also shows that including more information in the training, as is
commonly assumed to be useful, does not lead to better performance but may
trigger instability and impact the effectiveness of the overall approach.
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification
Authors: Alexandr A. Kalinin, Gerald A. Higgins, Narathip Reamaroon, S. M. Reza Soroushmehr, Ari Allyn-Feuer, Ivo D. Dinov, Kayvan Najarian, Brian D. Athey
Date: 2018-01-25 00:00:00
Tasks: Deep Learning

This Perspective provides examples of current and future applications of deep
learning in pharmacogenomics, including: (1) identification of novel regulatory
variants located in noncoding domains and their function as applied to
pharmacoepigenomics; (2) patient stratification from medical records; and (3)
prediction of drugs, targets, and their interactions. Deep learning
encapsulates a family of machine learning algorithms that over the last decade
has transformed many important subfields of artificial intelligence (AI) and
has demonstrated breakthrough performance improvements on a wide range of tasks
in biomedicine. We anticipate that in the future deep learning will be widely
used to predict personalized drug response and optimize medication selection
and dosing, using knowledge extracted from large and complex molecular,
epidemiological, clinical, and demographic datasets.
Decision-making processes in the Cognitive Theory of True Conditions
Authors: Sergio Miguel-Tomé
Date: 2018-03-06 00:00:00
Tasks: Decision Making

The Cognitive Theory of True Conditions (CTTC) is a proposal to design the
implementation of cognitive abilities and to describe the model-theoretic
semantics of symbolic cognitive architectures. The CTTC is formulated
mathematically using the multi-optional many-sorted past present future(MMPPF)
structures. This article discussed how decision-making processes are described
in the CTTC.
Dynamic Coattention Networks For Question Answering
Authors: Caiming Xiong, Victor Zhong, Richard Socher
Date: 2016-11-05 00:00:00
Tasks: Decoder, Question Answering

Several deep learning models have been proposed for question answering.
However, due to their single-pass nature, they have no way to recover from
local maxima corresponding to incorrect answers. To address this problem, we
introduce the Dynamic Coattention Network (DCN) for question answering. The DCN
first fuses co-dependent representations of the question and the document in
order to focus on relevant parts of both. Then a dynamic pointing decoder
iterates over potential answer spans. This iterative procedure enables the
model to recover from initial local maxima corresponding to incorrect answers.
On the Stanford question answering dataset, a single DCN model improves the
previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains
80.4% F1.