pwc-archive/papers-with-abstracts huggingface dataset:
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Authors: Yan Yang, Jian Sun, Huibin Li, Zongben Xu
Date: 2017-05-19 00:00:00
Tasks: Compressive Sensing, Deep Learning, Image Reconstruction

Compressive sensing (CS) is an effective approach for fast Magnetic Resonance
Imaging (MRI). It aims at reconstructing MR images from a small number of
under-sampled data in k-space, and accelerating the data acquisition in MRI. To
improve the current MRI system in reconstruction accuracy and speed, in this
paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and
generalized versions. ADMM-Nets are defined over data flow graphs, which are
derived from the iterative procedures in Alternating Direction Method of
Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They
take the sampled k-space data as inputs and output reconstructed MR images.
Moreover, we extend our network to cope with complex-valued MR images. In the
training phase, all parameters of the nets, e.g., transforms, shrinkage
functions, etc., are discriminatively trained end-to-end. In the testing phase,
they have computational overhead similar to ADMM algorithm but use optimized
parameters learned from the data for CS-based reconstruction task. We
investigate different configurations in network structures and conduct
extensive experiments on MR image reconstruction under different sampling
rates. Due to the combination of the advantages in model-based approach and
deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction
accuracies with fast computational speed.
CHAM: action recognition using convolutional hierarchical attention model
Authors: Shi-Yang Yan, Jeremy S. Smith, Wenjin Lu, Bai-Ling Zhang
Date: 2017-05-09 00:00:00
Tasks: Action Recognition, Image Captioning, Temporal Action Localization

Recently, the soft attention mechanism, which was originally proposed in
language processing, has been applied in computer vision tasks like image
captioning. This paper presents improvements to the soft attention model by
combining a convolutional LSTM with a hierarchical system architecture to
recognize action categories in videos. We call this model the Convolutional
Hierarchical Attention Model (CHAM). The model applies a convolutional
operation inside the LSTM cell and an attention map generation process to
recognize actions. The hierarchical architecture of this model is able to
explicitly reason on multi-granularities of action categories. The proposed
architecture achieved improved results on three publicly available datasets:
the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.
Co-clustering through Optimal Transport
Authors: Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault
Date: 2017-05-17 00:00:00
Proceeding: ICML 2017 8
Tasks: Clustering, Variational Inference

In this paper, we present a novel method for co-clustering, an unsupervised
learning approach that aims at discovering homogeneous groups of data instances
and features by grouping them simultaneously. The proposed method uses the
entropy regularized optimal transport between empirical measures defined on
data instances and features in order to obtain an estimated joint probability
density function represented by the optimal coupling matrix. This matrix is
further factorized to obtain the induced row and columns partitions using
multiscale representations approach. To justify our method theoretically, we
show how the solution of the regularized optimal transport can be seen from the
variational inference perspective thus motivating its use for co-clustering.
The algorithm derived for the proposed method and its kernelized version based
on the notion of Gromov-Wasserstein distance are fast, accurate and can
determine automatically the number of both row and column clusters. These
features are vividly demonstrated through extensive experimental evaluations.
Prediction of Sea Surface Temperature using Long Short-Term Memory
Authors: Qin Zhang, Hui Wang, Junyu Dong, Guoqiang Zhong, Xin Sun
Date: 2017-05-19 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Regression

This letter adopts long short-term memory(LSTM) to predict sea surface
temperature(SST), which is the first attempt, to our knowledge, to use
recurrent neural network to solve the problem of SST prediction, and to make
one week and one month daily prediction. We formulate the SST prediction
problem as a time series regression problem. LSTM is a special kind of
recurrent neural network, which introduces gate mechanism into vanilla RNN to
prevent the vanished or exploding gradient problem. It has strong ability to
model the temporal relationship of time series data and can handle the
long-term dependency problem well. The proposed network architecture is
composed of two kinds of layers: LSTM layer and full-connected dense layer.
LSTM layer is utilized to model the time series relationship. Full-connected
layer is utilized to map the output of LSTM layer to a final prediction. We
explore the optimal setting of this architecture by experiments and report the
accuracy of coastal seas of China to confirm the effectiveness of the proposed
method. In addition, we also show its online updated characteristics.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Authors: Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer
Date: 2017-02-03 00:00:00
Tasks: Collision Avoidance

Deep neural networks have emerged as a widely used and effective means for
tackling complex, real-world problems. However, a major obstacle in applying
them to safety-critical systems is the great difficulty in providing formal
guarantees about their behavior. We present a novel, scalable, and efficient
technique for verifying properties of deep neural networks (or providing
counter-examples). The technique is based on the simplex method, extended to
handle the non-convex Rectified Linear Unit (ReLU) activation function, which
is a crucial ingredient in many modern neural networks. The verification
procedure tackles neural networks as a whole, without making any simplifying
assumptions. We evaluated our technique on a prototype deep neural network
implementation of the next-generation airborne collision avoidance system for
unmanned aircraft (ACAS Xu). Results show that our technique can successfully
prove properties of networks that are an order of magnitude larger than the
largest networks verified using existing methods.
Deep Learning as a Tool to Predict Flow Patterns in Two-Phase Flow
Authors: Mohammadmehdi Ezzatabadipour, Parth Singh, Melvin D. Robinson, Pablo Guillen-Rondon, Carlos Torres
Date: 2017-05-19 00:00:00

In order to better model complex real-world data such as multiphase flow, one
approach is to develop pattern recognition techniques and robust features that
capture the relevant information. In this paper, we use deep learning methods,
and in particular employ the multilayer perceptron, to build an algorithm that
can predict flow pattern in twophase flow from fluid properties and pipe
conditions. The preliminary results show excellent performance when compared
with classical methods of flow pattern prediction.
Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature
Authors: Songxuan Lai, Lianwen Jin, Weixin Yang
Date: 2017-05-19 00:00:00

Inspired by the great success of recurrent neural networks (RNNs) in
sequential modeling, we introduce a novel RNN system to improve the performance
of online signature verification. The training objective is to directly
minimize intra-class variations and to push the distances between skilled
forgeries and genuine samples above a given threshold. By back-propagating the
training signals, our RNN network produced discriminative features with desired
metrics. Additionally, we propose a novel descriptor, called the
length-normalized path signature (LNPS), and apply it to online signature
verification. LNPS has interesting properties, such as scale invariance and
rotation invariance after linear combination, and shows promising results in
online signature verification. Experiments on the publicly available SVC-2004
dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).
The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods
Authors: Jing Wu Lian, Nicholas Mattei, Renee Noble, Toby Walsh
Date: 2017-05-19 00:00:00
Tasks: Decision Making

Motivated by the common academic problem of allocating papers to referees for
conference reviewing we propose a novel mechanism for solving the assignment
problem when we have a two sided matching problem with preferences from one
side (the agents/reviewers) over the other side (the objects/papers) and both
sides have capacity constraints. The assignment problem is a fundamental
problem in both computer science and economics with application in many areas
including task and resource allocation. We draw inspiration from multi-criteria
decision making and voting and use order weighted averages (OWAs) to propose a
novel and flexible class of algorithms for the assignment problem. We show an
algorithm for finding a $\Sigma$-OWA assignment in polynomial time, in contrast
to the NP-hardness of finding an egalitarian assignment. Inspired by this
setting we observe an interesting connection between our model and the classic
proportional multi-winner election problem in social choice.
Toward Open-Set Face Recognition
Authors: Manuel Günther, Steve Cruz, Ethan M. Rudd, Terrance E. Boult
Date: 2017-05-03 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, Known Unknowns

Much research has been conducted on both face identification and face
verification, with greater focus on the latter. Research on face identification
has mostly focused on using closed-set protocols, which assume that all probe
images used in evaluation contain identities of subjects that are enrolled in
the gallery. Real systems, however, where only a fraction of probe sample
identities are enrolled in the gallery, cannot make this closed-set assumption.
Instead, they must assume an open set of probe samples and be able to
reject/ignore those that correspond to unknown identities. In this paper, we
address the widespread misconception that thresholding verification-like scores
is a good way to solve the open-set face identification problem, by formulating
an open-set face identification protocol and evaluating different strategies
for assessing similarity. Our open-set identification protocol is based on the
canonical labeled faces in the wild (LFW) dataset. Additionally to the known
identities, we introduce the concepts of known unknowns (known, but
uninteresting persons) and unknown unknowns (people never seen before) to the
biometric community. We compare three algorithms for assessing similarity in a
deep feature space under an open-set protocol: thresholded verification-like
scores, linear discriminant analysis (LDA) scores, and an extreme value machine
(EVM) probabilities. Our findings suggest that thresholding EVM probabilities,
which are open-set by design, outperforms thresholding verification-like
scores.
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
Authors: Zhengyang Wang, Hao Yuan, Shuiwang Ji
Date: 2017-05-18 00:00:00

The key idea of variational auto-encoders (VAEs) resembles that of
traditional auto-encoder models in which spatial information is supposed to be
explicitly encoded in the latent space. However, the latent variables in VAEs
are vectors, which can be interpreted as multiple feature maps of size 1x1.
Such representations can only convey spatial information implicitly when
coupled with powerful decoders. In this work, we propose spatial VAEs that use
feature maps of larger size as latent variables to explicitly capture spatial
information. This is achieved by allowing the latent variables to be sampled
from matrix-variate normal (MVN) distributions whose parameters are computed
from the encoder network. To increase dependencies among locations on latent
feature maps and reduce the number of parameters, we further propose spatial
VAEs via low-rank MVN distributions. Experimental results show that the
proposed spatial VAEs outperform original VAEs in capturing rich structural and
spatial information.
Generalized linear models with low rank effects for network data
Authors: Yun-Jhong Wu, Elizaveta Levina, Ji Zhu
Date: 2017-05-18 00:00:00
Tasks: Denoising, Missing Values

Networks are a useful representation for data on connections between units of
interests, but the observed connections are often noisy and/or include missing
values. One common approach to network analysis is to treat the network as a
realization from a random graph model, and estimate the underlying edge
probability matrix, which is sometimes referred to as network denoising. Here
we propose a generalized linear model with low rank effects to model network
edges. This model can be applied to various types of networks, including
directed and undirected, binary and weighted, and it can naturally utilize
additional information such as node and/or edge covariates. We develop an
efficient projected gradient ascent algorithm to fit the model, establish
asymptotic consistency, and demonstrate empirical performance of the method on
both simulated and real networks.
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
Authors: Christian Bailer, Kiran varanasi, Didier Stricker
Date: 2016-07-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Optical Flow Estimation, Patch Matching

Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a CNN based patch matching approach for optical flow estimation. An important contribution of our approach is a novel thresholded loss for Siamese networks. We demonstrate that our loss performs clearly better than existing losses. It also allows to speed up training by a factor of 2 in our tests. Furthermore, we present a novel way for calculating CNN based features for different image scales, which performs better than existing methods. We also discuss new ways of evaluating the robustness of trained features for the application of patch matching for optical flow. An interesting discovery in our paper is that low-pass filtering of feature maps can increase the robustness of features created by CNNs. We proved the competitive performance of our approach by submitting it to the KITTI 2012, KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art results on all three datasets.
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
Authors: Devinder Kumar, Alexander Wong, Graham W. Taylor
Date: 2017-04-13 00:00:00
Tasks: Decision Making

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an
approach to visualize and understand the decisions made by deep neural networks
(DNNs) given a specific input. CLEAR facilitates the visualization of attentive
regions and levels of interest of DNNs during the decision-making process. It
also enables the visualization of the most dominant classes associated with
these attentive regions of interest. As such, CLEAR can mitigate some of the
shortcomings of heatmap-based methods associated with decision ambiguity, and
allows for better insights into the decision-making process of DNNs.
Quantitative and qualitative experiments across three different datasets
demonstrate the efficacy of CLEAR for gaining a better understanding of the
inner workings of DNNs during the decision-making process.
A General Model for Robust Tensor Factorization with Unknown Noise
Authors: Xi'ai Chen, Zhi Han, Yao Wang, Qian Zhao, Deyu Meng, Lin Lin, Yandong Tang
Date: 2017-05-18 00:00:00

Because of the limitations of matrix factorization, such as losing spatial
structure information, the concept of low-rank tensor factorization (LRTF) has
been applied for the recovery of a low dimensional subspace from high
dimensional visual data. The low-rank tensor recovery is generally achieved by
minimizing the loss function between the observed data and the factorization
representation. The loss function is designed in various forms under different
noise distribution assumptions, like $L_1$ norm for Laplacian distribution and
$L_2$ norm for Gaussian distribution. However, they often fail to tackle the
real data which are corrupted by the noise with unknown distribution. In this
paper, we propose a generalized weighted low-rank tensor factorization method
(GWLRTF) integrated with the idea of noise modelling. This procedure treats the
target data as high-order tensor directly and models the noise by a Mixture of
Gaussians, which is called MoG GWLRTF. The parameters in the model are
estimated under the EM framework and through a new developed algorithm of
weighted low-rank tensor factorization. We provide two versions of the
algorithm with different tensor factorization operations, i.e., CP
factorization and Tucker factorization. Extensive experiments indicate the
respective advantages of this two versions in different applications and also
demonstrate the effectiveness of MoG GWLRTF compared with other competing
methods.
Supervised Learning with Quantum-Inspired Tensor Networks
Authors: E. Miles Stoudenmire, David J. Schwab
Date: 2016-05-18 00:00:00
Tasks: General Classification, Tensor Networks

Tensor networks are efficient representations of high-dimensional tensors
which have been very successful for physics and mathematics applications. We
demonstrate how algorithms for optimizing such networks can be adapted to
supervised learning tasks by using matrix product states (tensor trains) to
parameterize models for classifying images. For the MNIST data set we obtain
less than 1% test set classification error. We discuss how the tensor network
form imparts additional structure to the learned model and suggest a possible
generative interpretation.
Discovering the Graph Structure in the Clustering Results
Authors: Evgeny Bauman, Konstantin Bauman
Date: 2017-05-18 00:00:00
Tasks: Clustering

In a standard cluster analysis, such as k-means, in addition to clusters
locations and distances between them, it's important to know if they are
connected or well separated from each other. The main focus of this paper is
discovering the relations between the resulting clusters. We propose a new
method which is based on pairwise overlapping k-means clustering, that in
addition to means of clusters provides the graph structure of their relations.
The proposed method has a set of parameters that can be tuned in order to
control the sensitivity of the model and the desired relative size of the
pairwise overlapping interval between means of two adjacent clusters, i.e.,
level of overlapping. We present the exact formula for calculating that
parameter. The empirical study presented in the paper demonstrates that our
approach works well not only on toy data but also compliments standard
clustering results with a reasonable graph structure on real datasets, such as
financial indices and restaurants.
Bandit Regret Scaling with the Effective Loss Range
Authors: Nicolò Cesa-Bianchi, Ohad Shamir
Date: 2017-05-15 00:00:00
Tasks: Multi-Armed Bandits

We study how the regret guarantees of nonstochastic multi-armed bandits can be improved, if the effective range of the losses in each round is small (e.g. the maximal difference between two losses in a given round). Despite a recent impossibility result, we show how this can be made possible under certain mild additional assumptions, such as availability of rough estimates of the losses, or advance knowledge of the loss of a single, possibly unspecified arm. Along the way, we develop a novel technique which might be of independent interest, to convert any multi-armed bandit algorithm with regret depending on the loss range, to an algorithm with regret depending only on the effective range, while avoiding predictably bad arms altogether.
Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System
Authors: Feng Yao, Suleiman Y. Yerima, BooJoong Kang, Sakir Sezer
Date: 2017-05-18 00:00:00
Tasks: Mobile Security

As mobile devices have become indispensable in modern life, mobile security
is becoming much more important. Traditional password or PIN-like
point-of-entry security measures score low on usability and are vulnerable to
brute force and other types of attacks. In order to improve mobile security, an
adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication
system is proposed in this paper to provide authentication in a continuous and
transparent manner.To illustrate the applicability and capability of ANFIS in
our implicit authentication system, experiments were conducted on behavioural
data collected for up to 12 weeks from different Android users. The ability of
the ANFIS-based system to detect an adversary is also tested with scenarios
involving an attacker with varying levels of knowledge. The results demonstrate
that ANFIS is a feasible and efficient approach for implicit authentication
with an average of 95% user recognition rate. Moreover, the use of ANFIS-based
system for implicit authentication significantly reduces manual tuning and
configuration tasks due to its selflearning capability.
Model-based Catheter Segmentation in MRI-images
Authors: Andre Mastmeyer, Guillaume Pernelle, Lauren Barber, Steve Pieper, Dirk Fortmeier, Sandy Wells, Heinz Handels, Tina Kapur
Date: 2017-05-18 00:00:00
Tasks: model, Segmentation

Accurate and reliable segmentation of catheters in MR-guided interventions remains a challenge, and a step of critical importance in clinical workflows. In this work, under reasonable assumptions, mechanical model based heuristics guide the segmentation process allows correct catheter identification rates greater than 98% (error 2.88 mm), and reduction in outliers to one-fourth compared to the state of the art. Given distal tips, searching towards the proximal ends of the catheters is guided by mechanical models that are estimated on a per-catheter basis. Their bending characteristics are used to constrain the image feature based candidate points. The final catheter trajectories are hybrid sequences of individual points, each derived from model and image features. We evaluate the method on a database of 10 patient MRI scans including 101 manually segmented catheters. The mean errors were 1.40 mm and the median errors were 1.05 mm. The number of outliers deviating more than 2 mm from the gold standard is 7, and the number of outliers deviating more than 3 mm from the gold standard is just 2.