pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
Authors: Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
Date: 2018-07-15 00:00:00
Tasks: Bayesian Inference, EEG, Electroencephalogram (EEG), object-detection, Object Detection, regression

This paper introduces a new sparse spatio-temporal structured Gaussian
process regression framework for online and offline Bayesian inference. This is
the first framework that gives a time-evolving representation of the
interdependencies between the components of the sparse signal of interest. A
hierarchical Gaussian process describes such structure and the
interdependencies are represented via the covariance matrices of the prior
distributions. The inference is based on the expectation propagation method and
the theoretical derivation of the posterior distribution is provided in the
paper. The inference framework is thoroughly evaluated over synthetic, real
video and electroencephalography (EEG) data where the spatio-temporal evolving
patterns need to be reconstructed with high accuracy. It is shown that it
achieves 15% improvement of the F-measure compared with the alternating
direction method of multipliers, spatio-temporal sparse Bayesian learning
method and one-level Gaussian process model. Additionally, the required memory
for the proposed algorithm is less than in the one-level Gaussian process
model. This structured sparse regression framework is of broad applicability to
source localisation and object detection problems with sparse signals.
DeepInf: Social Influence Prediction with Deep Learning
Authors: Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang
Date: 2018-07-15 00:00:00
Tasks: Deep Learning, Feature Engineering, Graph Neural Network, Prediction, Representation Learning

Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
  Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.
Learning Probabilistic Logic Programs in Continuous Domains
Authors: Stefanie Speichert, Vaishak Belle
Date: 2018-07-15 00:00:00
Tasks: Inductive logic programming, Relational Reasoning

The field of statistical relational learning aims at unifying logic and
probability to reason and learn from data. Perhaps the most successful paradigm
in the field is probabilistic logic programming: the enabling of stochastic
primitives in logic programming, which is now increasingly seen to provide a
declarative background to complex machine learning applications. While many
systems offer inference capabilities, the more significant challenge is that of
learning meaningful and interpretable symbolic representations from data. In
that regard, inductive logic programming and related techniques have paved much
of the way for the last few decades.
  Unfortunately, a major limitation of this exciting landscape is that much of
the work is limited to finite-domain discrete probability distributions.
Recently, a handful of systems have been extended to represent and perform
inference with continuous distributions. The problem, of course, is that
classical solutions for inference are either restricted to well-known
parametric families (e.g., Gaussians) or resort to sampling strategies that
provide correct answers only in the limit. When it comes to learning, moreover,
inducing representations remains entirely open, other than "data-fitting"
solutions that force-fit points to aforementioned parametric families.
  In this paper, we take the first steps towards inducing probabilistic logic
programs for continuous and mixed discrete-continuous data, without being
pigeon-holed to a fixed set of distribution families. Our key insight is to
leverage techniques from piecewise polynomial function approximation theory,
yielding a principled way to learn and compositionally construct density
functions. We test the framework and discuss the learned representations.
Deep Clustering for Unsupervised Learning of Visual Features
Authors: Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze
Date: 2018-07-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: Clustering, Deep Clustering, Image Clustering, Self-Supervised Image Classification, Semantic Segmentation, Unsupervised Semantic Segmentation

Clustering is a class of unsupervised learning methods that has been
extensively applied and studied in computer vision. Little work has been done
to adapt it to the end-to-end training of visual features on large scale
datasets. In this work, we present DeepCluster, a clustering method that
jointly learns the parameters of a neural network and the cluster assignments
of the resulting features. DeepCluster iteratively groups the features with a
standard clustering algorithm, k-means, and uses the subsequent assignments as
supervision to update the weights of the network. We apply DeepCluster to the
unsupervised training of convolutional neural networks on large datasets like
ImageNet and YFCC100M. The resulting model outperforms the current state of the
art by a significant margin on all the standard benchmarks.
Concept-Based Embeddings for Natural Language Processing
Authors: Yukun Ma, Erik Cambria
Date: 2018-07-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, Reranking, Sentiment Analysis, speech-recognition, Speech Recognition

In this work, we focus on effectively leveraging and integrating information
from concept-level as well as word-level via projecting concepts and words into
a lower dimensional space while retaining most critical semantics. In a broad
context of opinion understanding system, we investigate the use of the fused
embedding for several core NLP tasks: named entity detection and
classification, automatic speech recognition reranking, and targeted sentiment
analysis.
Syllabification by Phone Categorization
Authors: Jacob Krantz, Maxwell Dulin, Paul De Palma, Mark VanDam
Date: 2018-07-15 00:00:00
Tasks: Retrieval, speech-recognition, Speech Recognition, Speech Synthesis

Syllables play an important role in speech synthesis, speech recognition, and
spoken document retrieval. A novel, low cost, and language agnostic approach to
dividing words into their corresponding syllables is presented. A hybrid
genetic algorithm constructs a categorization of phones optimized for
syllabification. This categorization is used on top of a hidden Markov model
sequence classifier to find syllable boundaries. The technique shows promising
preliminary results when trained and tested on English words.
Boosting Combinatorial Problem Modeling with Machine Learning
Authors: Michele Lombardi, Michela Milano
Date: 2018-07-15 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization, Survey

In the past few years, the area of Machine Learning (ML) has witnessed
tremendous advancements, becoming a pervasive technology in a wide range of
applications. One area that can significantly benefit from the use of ML is
Combinatorial Optimization. The three pillars of constraint satisfaction and
optimization problem solving, i.e., modeling, search, and optimization, can
exploit ML techniques to boost their accuracy, efficiency and effectiveness. In
this survey we focus on the modeling component, whose effectiveness is crucial
for solving the problem. The modeling activity has been traditionally shaped by
optimization and domain experts, interacting to provide realistic results.
Machine Learning techniques can tremendously ease the process, and exploit the
available data to either create models or refine expert-designed ones. In this
survey we cover approaches that have been recently proposed to enhance the
modeling process by learning either single constraints, objective functions, or
the whole model. We highlight common themes to multiple approaches and draw
connections with related fields of research.
Magnitude Bounded Matrix Factorisation for Recommender Systems
Authors: Shuai Jiang, Kan Li, Richard Yi Da Xu
Date: 2018-07-15 00:00:00
Tasks: Recommendation Systems

Low rank matrix factorisation is often used in recommender systems as a way
of extracting latent features. When dealing with large and sparse datasets,
traditional recommendation algorithms face the problem of acquiring large,
unrestrained, fluctuating values over predictions especially for users/items
with very few corresponding observations. Although the problem has been
somewhat solved by imposing bounding constraints over its objectives, and/or
over all entries to be within a fixed range, in terms of gaining better
recommendations, these approaches have two major shortcomings that we aim to
mitigate in this work: one is they can only deal with one pair of fixed bounds
for all entries, and the other one is they are very time-consuming when applied
on large scale recommender systems. In this paper, we propose a novel algorithm
named Magnitude Bounded Matrix Factorisation (MBMF), which allows different
bounds for individual users/items and performs very fast on large scale
datasets. The key idea of our algorithm is to construct a model by constraining
the magnitudes of each individual user/item feature vector. We achieve this by
converting from the Cartesian to Spherical coordinate system with radii set as
the corresponding magnitudes, which allows the above constrained optimisation
problem to become an unconstrained one. The Stochastic Gradient Descent (SGD)
method is then applied to solve the unconstrained task efficiently. Experiments
on synthetic and real datasets demonstrate that in most cases the proposed MBMF
is superior over all existing algorithms in terms of accuracy and time
complexity.
Object Detection with Deep Learning: A Review
Authors: Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, Xindong Wu
Date: 2018-07-15 00:00:00
Tasks: Deep Learning, Face Detection, Object, object-detection, Object Detection, Pedestrian Detection, RGB Salient Object Detection, Salient Object Detection

Due to object detection's close relationship with video analysis and image
understanding, it has attracted much research attention in recent years.
Traditional object detection methods are built on handcrafted features and
shallow trainable architectures. Their performance easily stagnates by
constructing complex ensembles which combine multiple low-level image features
with high-level context from object detectors and scene classifiers. With the
rapid development in deep learning, more powerful tools, which are able to
learn semantic, high-level, deeper features, are introduced to address the
problems existing in traditional architectures. These models behave differently
in network architecture, training strategy and optimization function, etc. In
this paper, we provide a review on deep learning based object detection
frameworks. Our review begins with a brief introduction on the history of deep
learning and its representative tool, namely Convolutional Neural Network
(CNN). Then we focus on typical generic object detection architectures along
with some modifications and useful tricks to improve detection performance
further. As distinct specific detection tasks exhibit different
characteristics, we also briefly survey several specific tasks, including
salient object detection, face detection and pedestrian detection. Experimental
analyses are also provided to compare various methods and draw some meaningful
conclusions. Finally, several promising directions and tasks are provided to
serve as guidelines for future work in both object detection and relevant
neural network based learning systems.
Deep neural network ensemble by data augmentation and bagging for skin lesion classification
Authors: Manik Goyal, Jagath C. Rajapakse
Date: 2018-07-15 00:00:00
Tasks: Data Augmentation, General Classification, Lesion Classification, Meta-Learning, Skin Lesion Classification

This work summarizes our submission for the Task 3: Disease Classification of
ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection. We use
a novel deep neural network (DNN) ensemble architecture introduced by us that
can effectively classify skin lesions by using data-augmentation and bagging to
address paucity of data and prevent over-fitting. The ensemble is composed of
two DNN architectures: Inception-v4 and Inception-Resnet-v2. The DNN
architectures are combined in to an ensemble by using a $1\times1$ convolution
for fusion in a meta-learning layer.
Semi-supervised Feature Learning For Improving Writer Identification
Authors: Shiming Chen, Yisong Wang, Chin-Teng Lin, Weiping Ding, Zehong Cao
Date: 2018-07-15 00:00:00
Tasks: Data Augmentation

Data augmentation is usually used by supervised learning approaches for
offline writer identification, but such approaches require extra training data
and potentially lead to overfitting errors. In this study, a semi-supervised
feature learning pipeline was proposed to improve the performance of writer
identification by training with extra unlabeled data and the original labeled
data simultaneously. Specifically, we proposed a weighted label smoothing
regularization (WLSR) method for data augmentation, which assigned the weighted
uniform label distribution to the extra unlabeled data. The WLSR method could
regularize the convolutional neural network (CNN) baseline to allow more
discriminative features to be learned to represent the properties of different
writing styles. The experimental results on well-known benchmark datasets
(ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning
approach could significantly improve the baseline measurement and perform
competitively with existing writer identification approaches. Our findings
provide new insights into offline write identification.
The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences
Authors: Thomas Mitchel, Sipu Ruan, Yixin Gao, Gregory S. Chirikjian
Date: 2018-07-15 00:00:00
Tasks: Action Recognition, Computational Efficiency, Dynamic Time Warping, Temporal Action Localization

Signal alignment has become a popular problem in robotics due in part to its
fundamental role in action recognition. Currently, the most successful
algorithms for signal alignment are Dynamic Time Warping (DTW) and its variant
'Fast' Dynamic Time Warping (FastDTW). Here we introduce a new framework for
signal alignment, namely the Globally Optimal Reparameterization Algorithm
(GORA). We review the algorithm's mathematical foundation and provide a
numerical verification of its theoretical basis. We compare the performance of
GORA with that of the DTW and FastDTW algorithms, in terms of computational
efficiency and accuracy in matching signals. Our results show a significant
improvement in both speed and accuracy over the DTW and FastDTW algorithms and
suggest that GORA has the potential to provide a highly effective framework for
signal alignment and action recognition.
Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network
Authors: Zhongliu Xie, Duncan Gillies
Date: 2018-07-15 00:00:00
Tasks: Hippocampus, image-classification, Image Classification, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Over the past decades, state-of-the-art medical image segmentation has
heavily rested on signal processing paradigms, most notably registration-based
label propagation and pair-wise patch comparison, which are generally slow
despite a high segmentation accuracy. In recent years, deep learning has
revolutionalized computer vision with many practices outperforming prior art,
in particular the convolutional neural network (CNN) studies on image
classification. Deep CNN has also started being applied to medical image
segmentation lately, but generally involves long training and demanding memory
requirements, achieving limited success. We propose a patch-based deep learning
framework based on a revisit to the classic neural network model with
substantial modernization, including the use of Rectified Linear Unit (ReLU)
activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a
test application to hippocampus segmentation using 100 brain MR images from the
ADNI database, our approach significantly outperformed prior art in terms of
both segmentation accuracy and speed: scoring a median Dice score up to 90.98%
on a near real-time performance (<1s).
A salt and pepper noise image denoising method based on the generative classification
Authors: Bo Fu, Xiao-Yang Zhao, Yong-Gong Ren, Xi-Ming Li, Xiang-Hai Wang
Date: 2018-07-15 00:00:00
Tasks: Clustering, Denoising, General Classification, Image Denoising

In this paper, an image denoising algorithm is proposed for salt and pepper
noise. First, a generative model is built on a patch as a basic unit and then
the algorithm locates the image noise within that patch in order to better
describe the patch and obtain better subsequent clustering. Second, the
algorithm classifies patches using a generative clustering method, thus
providing additional similarity information for noise repair and suppressing
the interference of noise, abandoning those categories that consist of a
smaller number of patches. Finally, the algorithm builds a non-local switching
filter to remove the salt and pepper noise. Simulation results show that the
proposed algorithm effectively denoises salt and pepper noise of various
densities. It obtains a better visual quality and higher peak signal-to-noise
ratio score than several state-of-the-art algorithms. In short, our algorithm
uses a noisy patch as the basic unit, a patch clustering method to optimize the
repair data set as well as obtain a better denoising effect, and provides a
guideline for future denoising and repair methods.
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
Authors: Andreas Bueff, Stefanie Speichert, Vaishak Belle
Date: 2018-07-14 00:00:00

Probabilistic representations, such as Bayesian and Markov networks, are
fundamental to much of statistical machine learning. Thus, learning
probabilistic representations directly from data is a deep challenge, the main
computational bottleneck being inference that is intractable. Tractable
learning is a powerful new paradigm that attempts to learn distributions that
support efficient probabilistic querying. By leveraging local structure,
representations such as sum-product networks (SPNs) can capture high tree-width
models with many hidden layers, essentially a deep architecture, while still
admitting a range of probabilistic queries to be computable in time polynomial
in the network size. The leaf nodes in SPNs, from which more intricate mixtures
are formed, are tractable univariate distributions, and so the literature has
focused on Bernoulli and Gaussian random variables. This is clearly a
restriction for handling mixed discrete-continuous data, especially if the
continuous features are generated from non-parametric and non-Gaussian
distribution families. In this work, we present a framework that systematically
integrates SPN structure learning with weighted model integration, a recently
introduced computational abstraction for performing inference in hybrid
domains, by means of piecewise polynomial approximations of density functions
of arbitrary shape. Our framework is instantiated by exploiting the notion of
propositional abstractions, thus minimally interfering with the SPN structure
learning module, and supports a powerful query interface for conditioning on
interval constraints. Our empirical results show that our approach is
effective, and allows a study of the trade off between the granularity of the
learned model and its predictive power.
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
Authors: Sakshi Mishra, Praveen Palanisamy
Date: 2018-07-14 00:00:00
Tasks: 3D Anomaly Detection and Segmentation, Prediction

The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid.
Specular-to-Diffuse Translation for Multi-View Reconstruction
Authors: Shihao Wu, Hui Huang, Tiziano Portenier, Matan Sela, Danny Cohen-Or, Ron Kimmel, Matthias Zwicker
Date: 2018-07-14 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Reconstruction, Generative Adversarial Network, Image-to-Image Translation, Lighting Estimation, Multi-View 3D Reconstruction, Translation, Unsupervised Image-To-Image Translation

Most multi-view 3D reconstruction algorithms, especially when
shape-from-shading cues are used, assume that object appearance is
predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a
generative adversarial network for transferring multiple views of objects with
specular reflection into diffuse ones, so that multi-view reconstruction
methods can be applied more effectively. Our network extends unsupervised
image-to-image translation to multi-view "specular to diffuse" translation. To
preserve object appearance across multiple views, we introduce a Multi-View
Coherence loss (MVC) that evaluates the similarity and faithfulness of local
patches after the view-transformation. Our MVC loss ensures that the similarity
of local correspondences among multi-view images is preserved under the
image-to-image translation. As a result, our network yields significantly
better results than several single-view baseline techniques. In addition, we
carefully design and generate a large synthetic training data set using
physically-based rendering. During testing, our network takes only the raw
glossy images as input, without extra information such as segmentation masks or
lighting estimation. Results demonstrate that multi-view reconstruction can be
significantly improved using the images filtered by our network. We also show
promising performance on real world training and testing data.