pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge Graph Completion via Complex Tensor Factorization
Authors: Théo Trouillon, Christopher R. Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard
Date: 2017-02-22 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Relational Reasoning

In statistical relational learning, knowledge graph completion deals with
automatically understanding the structure of large knowledge graphs---labeled
directed graphs---and predicting missing relationships---labeled edges.
State-of-the-art embedding models propose different trade-offs between modeling
expressiveness, and time and space complexity. We reconcile both expressiveness
and complexity through the use of complex-valued embeddings and explore the
link between such complex-valued embeddings and unitary diagonalization. We
corroborate our approach theoretically and show that all real square
matrices---thus all possible relation/adjacency matrices---are the real part of
some unitarily diagonalizable matrix. This results opens the door to a lot of
other applications of square matrices factorization. Our approach based on
complex embeddings is arguably simple, as it only involves a Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors, whereas other methods resort to more and more complicated composition
functions to increase their expressiveness. The proposed complex embeddings are
scalable to large data sets as it remains linear in both space and time, while
consistently outperforming alternative approaches on standard link prediction
benchmarks.
Quantum Artificial Life in an IBM Quantum Computer
Authors: U. Alvarez-Rodriguez, M. Sanz, L. Lamata, E. Solano
Date: 2017-11-26 00:00:00
Tasks: Artificial Life, Quantum Machine Learning

We present the first experimental realization of a quantum artificial life
algorithm in a quantum computer. The quantum biomimetic protocol encodes
tailored quantum behaviors belonging to living systems, namely,
self-replication, mutation, interaction between individuals, and death, into
the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads
throughout generations of individuals, where genuine quantum information
features are inherited through genealogical networks. As a pioneering
proof-of-principle, experimental data fits the ideal model with accuracy.
Thereafter, these and other models of quantum artificial life, for which no
classical device may predict its quantum supremacy evolution, can be further
explored in novel generations of quantum computers. Quantum biomimetics,
quantum machine learning, and quantum artificial intelligence will move forward
hand in hand through more elaborate levels of quantum complexity.
User-centric Composable Services: A New Generation of Personal Data Analytics
Authors: Jianxin Zhao, Richard Mortier, Jon Crowcroft, Liang Wang
Date: 2017-10-25 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) techniques, such as Neural Network, are widely used in
today's applications. However, there is still a big gap between the current ML
systems and users' requirements. ML systems focus on improving the performance
of models in training, while individual users cares more about response time
and expressiveness of the tool. Many existing research and product begin to
move computation towards edge devices. Based on the numerical computing system
Owl, we propose to build the Zoo system to support construction, compose, and
deployment of ML models on edge and local devices.
A general unified framework for interval pairwise comparison matrices
Authors: Bice Cavallo, Matteo Brunelli
Date: 2017-11-26 00:00:00

Interval Pairwise Comparison Matrices have been widely used to account for
uncertain statements concerning the preferences of decision makers. Several
approaches have been proposed in the literature, such as multiplicative and
fuzzy interval matrices. In this paper, we propose a general unified approach
to Interval Pairwise Comparison Matrices, based on Abelian linearly ordered
groups. In this framework, we generalize some consistency conditions provided
for multiplicative and/or fuzzy interval pairwise comparison matrices and
provide inclusion relations between them. Then, we provide a concept of
distance between intervals that, together with a notion of mean defined over
real continuous Abelian linearly ordered groups, allows us to provide a
consistency index and an indeterminacy index. In this way, by means of suitable
isomorphisms between Abelian linearly ordered groups, we will be able to
compare the inconsistency and the indeterminacy of different kinds of Interval
Pairwise Comparison Matrices, e.g. multiplicative, additive, and fuzzy, on a
unique Cartesian coordinate system.
Coplanar Repeats by Energy Minimization
Authors: James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
Date: 2017-11-26 00:00:00

This paper proposes an automated method to detect, group and rectify
arbitrarily-arranged coplanar repeated elements via energy minimization. The
proposed energy functional combines several features that model how planes with
coplanar repeats are projected into images and captures global interactions
between different coplanar repeat groups and scene planes. An inference
framework based on a recent variant of $\alpha$-expansion is described and fast
convergence is demonstrated. We compare the proposed method to two widely-used
geometric multi-model fitting methods using a new dataset of annotated images
containing multiple scene planes with coplanar repeats in varied arrangements.
The evaluation shows a significant improvement in the accuracy of
rectifications computed from coplanar repeats detected with the proposed method
versus those detected with the baseline methods.
Local Jet Pattern: A Robust Descriptor for Texture Classification
Authors: Swalpa Kumar Roy, Bhabatosh Chanda, Bidyut. B. Chaudhuri, Dipak Kumar Ghosh, Shiv Ram Dubey
Date: 2017-11-26 00:00:00
Tasks: Classification, General Classification, Texture Classification

Methods based on local image features have recently shown promise for texture
classification tasks, especially in the presence of large intra-class variation
due to illumination, scale, and viewpoint changes. Inspired by the theories of
image structure analysis, this paper presents a simple, efficient, yet robust
descriptor namely local jet pattern (LJP) for texture classification. In this
approach, a jet space representation of a texture image is derived from a set
of derivatives of Gaussian (DtGs) filter responses up to second order, so
called local jet vectors (LJV), which also satisfy the Scale Space properties.
The LJP is obtained by utilizing the relationship of center pixel with the
local neighborhood information in jet space. Finally, the feature vector of a
texture region is formed by concatenating the histogram of LJP for all elements
of LJV. All DtGs responses up to second order together preserves the intrinsic
local image structure, and achieves invariance to scale, rotation, and
reflection. This allows us to develop a texture classification framework which
is discriminative and robust. Extensive experiments on five standard texture
image databases, employing nearest subspace classifier (NSC), the proposed
descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for
Outex_TC-00010 (Outex_TC10), and Outex_TC-00012 (Outex_TC12), KTH-TIPS,
Brodatz, CUReT, respectively, which are outperforms the state-of-the-art
methods.
MAVOT: Memory-Augmented Video Object Tracking
Authors: Boyu Liu, Yanzhao Wang, Yu-Wing Tai, Chi-Keung Tang
Date: 2017-11-26 00:00:00
Tasks: Object, Object Tracking, One-Shot Learning, Video Object Tracking, Visual Tracking

We introduce a one-shot learning approach for video object tracking. The
proposed algorithm requires seeing the object to be tracked only once, and
employs an external memory to store and remember the evolving features of the
foreground object as well as backgrounds over time during tracking. With the
relevant memory retrieved and updated in each tracking, our tracking model is
capable of maintaining long-term memory of the object, and thus can naturally
deal with hard tracking scenarios including partial and total occlusion, motion
changes and large scale and shape variations. In our experiments we use the
ImageNet ILSVRC2015 video detection dataset to train and use the VOT-2016
benchmark to test and compare our Memory-Augmented Video Object Tracking
(MAVOT) model. From the results, we conclude that given its oneshot property
and simplicity in design, MAVOT is an attractive approach in visual tracking
because it shows good performance on VOT-2016 benchmark and is among the top 5
performers in accuracy and robustness in occlusion, motion changes and empty
target.
Subspace Clustering via Optimal Direction Search
Authors: Mostafa Rahmani, George Atia
Date: 2017-06-12 00:00:00
Tasks: Clustering, Face Clustering

This letter presents a new spectral-clustering-based approach to the subspace
clustering problem. Underpinning the proposed method is a convex program for
optimal direction search, which for each data point d finds an optimal
direction in the span of the data that has minimum projection on the other data
points and non-vanishing projection on d. The obtained directions are
subsequently leveraged to identify a neighborhood set for each data point. An
alternating direction method of multipliers framework is provided to
efficiently solve for the optimal directions. The proposed method is shown to
notably outperform the existing subspace clustering methods, particularly for
unwieldy scenarios involving high levels of noise and close subspaces, and
yields the state-of-the-art results for the problem of face clustering using
subspace segmentation.
Innovation Pursuit: A New Approach to Subspace Clustering
Authors: Mostafa Rahmani, George Atia
Date: 2015-12-02 00:00:00
Tasks: Clustering, Face Clustering

In subspace clustering, a group of data points belonging to a union of
subspaces are assigned membership to their respective subspaces. This paper
presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of
subspace clustering using a new geometrical idea whereby subspaces are
identified based on their relative novelties. We present two frameworks in
which the idea of innovation pursuit is used to distinguish the subspaces.
Underlying the first framework is an iterative method that finds the subspaces
consecutively by solving a series of simple linear optimization problems, each
searching for a direction of innovation in the span of the data potentially
orthogonal to all subspaces except for the one to be identified in one step of
the algorithm. A detailed mathematical analysis is provided establishing
sufficient conditions for iPursuit to correctly cluster the data. The proposed
approach can provably yield exact clustering even when the subspaces have
significant intersections. It is shown that the complexity of the iterative
approach scales only linearly in the number of data points and subspaces, and
quadratically in the dimension of the subspaces. The second framework
integrates iPursuit with spectral clustering to yield a new variant of
spectral-clustering-based algorithms. The numerical simulations with both real
and synthetic data demonstrate that iPursuit can often outperform the
state-of-the-art subspace clustering algorithms, more so for subspaces with
significant intersections, and that it significantly improves the
state-of-the-art result for subspace-segmentation-based face clustering.
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Authors: Lei Liu, Zongxu Pan, Bin Lei
Date: 2017-11-26 00:00:00

Detection of arbitrarily rotated objects is a challenging task due to the
difficulties of locating the multi-angle objects and separating them
effectively from the background. The existing methods are not robust to angle
varies of the objects because of the use of traditional bounding box, which is
a rotation variant structure for locating rotated objects. In this article, a
new detection method is proposed which applies the newly defined rotatable
bounding box (RBox). The proposed detector (DRBox) can effectively handle the
situation where the orientation angles of the objects are arbitrary. The
training of DRBox forces the detection networks to learn the correct
orientation angle of the objects, so that the rotation invariant property can
be achieved. DRBox is tested to detect vehicles, ships and airplanes on
satellite images, compared with Faster R-CNN and SSD, which are chosen as the
benchmark of the traditional bounding box based methods. The results shows that
DRBox performs much better than traditional bounding box based methods do on
the given tasks, and is more robust against rotation of input image and target
objects. Besides, results show that DRBox correctly outputs the orientation
angles of the objects, which is very useful for locating multi-angle objects
efficiently. The code and models are available at
https://github.com/liulei01/DRBox.
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Authors: Andrew Slavin Ross, Finale Doshi-Velez
Date: 2017-11-26 00:00:00
Tasks: Adversarial Robustness

Deep neural networks have proven remarkably effective at solving many
classification problems, but have been criticized recently for two major
weaknesses: the reasons behind their predictions are uninterpretable, and the
predictions themselves can often be fooled by small adversarial perturbations.
These problems pose major obstacles for the adoption of neural networks in
domains that require security or transparency. In this work, we evaluate the
effectiveness of defenses that differentiably penalize the degree to which
small changes in inputs can alter model predictions. Across multiple attacks,
architectures, defenses, and datasets, we find that neural networks trained
with this input gradient regularization exhibit robustness to transferred
adversarial examples generated to fool all of the other models. We also find
that adversarial examples generated to fool gradient-regularized models fool
all other models equally well, and actually lead to more "legitimate,"
interpretable misclassifications as rated by people (which we confirm in a
human subject experiment). Finally, we demonstrate that regularizing input
gradients makes them more naturally interpretable as rationales for model
predictions. We conclude by discussing this relationship between
interpretability and robustness in deep neural networks.
Novel Adaptive Genetic Algorithm Sample Consensus
Authors: Ehsan Shojaedini, Mahshid Majd, Reza Safabakhsh
Date: 2017-11-26 00:00:00

Random sample consensus (RANSAC) is a successful algorithm in model fitting
applications. It is vital to have strong exploration phase when there are an
enormous amount of outliers within the dataset. Achieving a proper model is
guaranteed by pure exploration strategy of RANSAC. However, finding the optimum
result requires exploitation. GASAC is an evolutionary paradigm to add
exploitation capability to the algorithm. Although GASAC improves the results
of RANSAC, it has a fixed strategy for balancing between exploration and
exploitation. In this paper, a new paradigm is proposed based on genetic
algorithm with an adaptive strategy. We utilize an adaptive genetic operator to
select high fitness individuals as parents and mutate low fitness ones. In the
mutation phase, a training method is used to gradually learn which gene is the
best replacement for the mutated gene. The proposed method adaptively balance
between exploration and exploitation by learning about genes. During the final
Iterations, the algorithm draws on this information to improve the final
results. The proposed method is extensively evaluated on two set of
experiments. In all tests, our method outperformed the other methods in terms
of both the number of inliers found and the speed of the algorithm.
Whale Optimization Based Energy-Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks
Authors: Ashwin R. Jadhav, T. Shankar
Date: 2017-11-26 00:00:00
Tasks: Clustering

Wireless Sensor Network (WSN) consists of many individual sensors that are
deployed in the area of interest. These sensor nodes have major energy
constraints as they are small and their battery can't be replaced. They
collaborate together in order to gather, transmit and forward the sensed data
to the base station. Consequently, data transmission is one of the biggest
reasons for energy depletion in WSN. Clustering is one of the most effective
techniques for energy efficient data transmission in WSN. In this paper, an
energy efficient cluster head selection algorithm which is based on Whale
Optimization Algorithm (WOA) called WOA-Clustering (WOA-C) is proposed.
Accordingly, the proposed algorithm helps in selection of energy aware cluster
heads based on a fitness function which considers the residual energy of the
node and the sum of energy of adjacent nodes. The proposed algorithm is
evaluated for network lifetime, energy efficiency, throughput and overall
stability. Furthermore, the performance of WOA-C is evaluated against other
standard contemporary routing protocols such as LEACH. Extensive simulations
show the superior performance of the proposed algorithm in terms of residual
energy, network lifetime and longer stability period.
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary
Authors: Khanh Quoc Dinh, Thuong Nguyen Canh, Byeungwoo Jeon
Date: 2017-11-26 00:00:00
Tasks: Compressive Sensing

This paper addresses an ill-posed problem of recovering a color image from
its compressively sensed measurement data. Differently from the typical 1D
vector-based approach of the state-of-the-art methods, we exploit the nonlocal
similarities inherently existing in images by treating each patch of a color
image as a 3D tensor consisting of not only horizontal and vertical but also
spectral dimensions. A group of nonlocal similar patches form a 4D tensor for
which a nonlocal higher order dictionary is learned via higher order singular
value decomposition. The multiple sub-dictionaries contained in the higher
order dictionary decorrelate the group in each corresponding dimension, thus
help the detail of color images to be reconstructed better. Furthermore, we
promote sparsity of the final solution using a sparsity regularization based on
a weight tensor. It can distinguish those coefficients of the sparse
representation generated by the higher order dictionary which are expected to
have large magnitude from the others in the optimization. Accordingly, in the
iterative solution, it acts like a weighting process which is designed by
approximating the minimum mean squared error filter for more faithful recovery.
Experimental results confirm improvement by the proposed method over the
state-of-the-art ones.
SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis
Authors: Dongjin Choi, Lee Sael
Date: 2017-11-22 00:00:00

Motivation: How do we integratively analyze large-scale multi-platform
genomic data that are high dimensional and sparse? Furthermore, how can we
incorporate prior knowledge, such as the association between genes, in the
analysis systematically? Method: To solve this problem, we propose a Scalable
Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts
parallel stochastic gradient descent approach on the proposed parallelizable
network constrained optimization function. SNeCT decomposition is applied to
tensor constructed from large scale multi-platform multi-cohort cancer data,
PanCan12, constrained on a network built from PathwayCommons database. Results:
The decomposed factor matrices are applied to stratify cancers, to search for
top-k similar patients, and to illustrate how the matrices can be used for
personalized interpretation. In the stratification test, combined twelve-cohort
data is clustered to form thirteen subclasses. The thirteen subclasses have a
high correlation to tissue of origin in addition to other interesting
observations, such as clear separation of OV cancers to two groups, and high
clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the
top-k search, a new patient's genomic profile is generated and searched against
existing patients based on the factor matrices. The similarity of the top-k
patient to the query is high for 23 clinical features, including
estrogen/progesterone receptor statuses of BRCA patients with average precision
value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also
provide an illustration of how the factor matrices can be used for
interpretable personalized analysis of each patient.
Learning to Remember Translation History with a Continuous Cache
Authors: Zhaopeng Tu, Yang Liu, Shuming Shi, Tong Zhang
Date: 2017-11-26 00:00:00
Proceeding: TACL 2018 1
Tasks: Machine Translation, NMT, Translation

Existing neural machine translation (NMT) models generally translate
sentences in isolation, missing the opportunity to take advantage of
document-level information. In this work, we propose to augment NMT models with
a very light-weight cache-like memory network, which stores recent hidden
representations as translation history. The probability distribution over
generated words is updated online depending on the translation history
retrieved from the memory, endowing NMT models with the capability to
dynamically adapt over time. Experiments on multiple domains with different
topics and styles show the effectiveness of the proposed approach with
negligible impact on the computational cost.
Scalable Demand-Aware Recommendation
Authors: Jinfeng Yi, Cho-Jui Hsieh, Kush Varshney, Lijun Zhang, Yao Li
Date: 2017-02-21 00:00:00
Proceeding: NeurIPS 2017 12

Recommendation for e-commerce with a mix of durable and nondurable goods has
characteristics that distinguish it from the well-studied media recommendation
problem. The demand for items is a combined effect of form utility and time
utility, i.e., a product must both be intrinsically appealing to a consumer and
the time must be right for purchase. In particular for durable goods, time
utility is a function of inter-purchase duration within product category
because consumers are unlikely to purchase two items in the same category in
close temporal succession. Moreover, purchase data, in contrast to ratings
data, is implicit with non-purchases not necessarily indicating dislike.
Together, these issues give rise to the positive-unlabeled demand-aware
recommendation problem that we pose via joint low-rank tensor completion and
product category inter-purchase duration vector estimation. We further relax
this problem and propose a highly scalable alternating minimization approach
with which we can solve problems with millions of users and millions of items
in a single thread. We also show superior prediction accuracies on multiple
real-world data sets.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
Authors: Qiang Chen, Yunhong Wang, Zheng Liu, Qingjie Liu, Di Huang
Date: 2017-11-26 00:00:00
Tasks: Gait Recognition

In this paper, we develop a novel convolutional neural network based approach
to extract and aggregate useful information from gait silhouette sequence
images instead of simply representing the gait process by averaging silhouette
images. The network takes a pair of arbitrary length sequence images as inputs
and extracts features for each silhouette independently. Then a feature map
pooling strategy is adopted to aggregate sequence features. Subsequently, a
network which is similar to Siamese network is designed to perform recognition.
The proposed network is simple and easy to implement and can be trained in an
end-to-end manner. Cross-view gait recognition experiments are conducted on
OU-ISIR large population dataset. The results demonstrate that our network can
extract and aggregate features from silhouette sequence effectively. It also
achieves significant equal error rates and comparable identification rates when
compared with the state of the art.