pwc-archive/papers-with-abstracts huggingface dataset:
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Authors: Wen Zhang, Bibek Paudel, Wei zhang, Abraham Bernstein, Huajun Chen
Date: 2019-03-12 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Prediction

Knowledge graph embedding aims to learn distributed representations for
entities and relations, and is proven to be effective in many applications.
Crossover interactions --- bi-directional effects between entities and
relations --- help select related information when predicting a new triple, but
haven't been formally discussed before. In this paper, we propose CrossE, a
novel knowledge graph embedding which explicitly simulates crossover
interactions. It not only learns one general embedding for each entity and
relation as most previous methods do, but also generates multiple triple
specific embeddings for both of them, named interaction embeddings. We evaluate
embeddings on typical link prediction tasks and find that CrossE achieves
state-of-the-art results on complex and more challenging datasets. Furthermore,
we evaluate embeddings from a new perspective --- giving explanations for
predicted triples, which is important for real applications. In this work, an
explanation for a triple is regarded as a reliable closed-path between the head
and the tail entity. Compared to other baselines, we show experimentally that
CrossE, benefiting from interaction embeddings, is more capable of generating
reliable explanations to support its predictions.
Generating and Sampling Orbits for Lifted Probabilistic Inference
Authors: Steven Holtzen, Todd Millstein, Guy Van Den Broeck
Date: 2019-03-12 00:00:00

A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non-relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.
Iterated two-phase local search for the Set-Union Knapsack Problem
Authors: Zequn Wei, Jin-Kao Hao
Date: 2019-03-12 00:00:00
Tasks: Combinatorial Optimization, Vocal Bursts Valence Prediction

The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1
knapsack problem. Given a set of weighted elements and a set of items with
profits where each item is composed of a subset of elements, the SUKP involves
packing a subset of items in a capacity-constrained knapsack such that the
total profit of the selected items is maximized while their weights do not
exceed the knapsack capacity. In this work, we present an effective iterated
two-phase local search algorithm for this NP-hard combinatorial optimization
problem. The proposed algorithm iterates through two search phases: a local
optima exploration phase that alternates between a variable neighborhood
descent search and a tabu search to explore local optimal solutions, and a
local optima escaping phase to drive the search to unexplored regions. We show
the competitiveness of the algorithm compared to the state-of-the-art methods
in the literature. Specifically, the algorithm discovers 18 improved best
results (new lower bounds) for the 30 benchmark instances and matches the
best-known results for the 12 remaining instances. We also report the first
computational results with the general CPLEX solver, including 6 proven optimal
solutions. Finally, we investigate the effectiveness of the key ingredients of
the algorithm on its performance.
Temporal Logics Over Finite Traces with Uncertainty (Technical Report)
Authors: Fabrizio M. Maggi, Marco Montali, Rafael Peñaloza
Date: 2019-03-12 00:00:00
Tasks: Decision Making

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.
Challenges in Search on Streaming Services: Netflix Case Study
Authors: Sudarshan Lamkhede, Sudeep Das
Date: 2019-03-11 00:00:00

We discuss salient challenges of building a search experience for a streaming
media service such as Netflix. We provide an overview of the role of
recommendations within the search context to aid content discovery and support
searches for unavailable (out-of-catalog) entities. We also stress the
importance of keystroke-level instant search experience, and the technical
challenges associated with implementing it across different devices and
languages for a global audience.
Extracting localized information from a Twitter corpus for flood prevention
Authors: Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet, Renaud Hostache, Patrick Matgen, Marco Chini, Thomas Tamisier
Date: 2019-03-12 00:00:00

In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial perspective, our goal here is to get a first estimation of the quality and precision of the geographical information featured in the collected corpus. From a topical perspective, we discuss the representation of Twitter posts, and strategies to process an initially unlabeled corpus of tweets.
Generalized Sparse Additive Models
Authors: Asad Haris, Noah Simon, Ali Shojaie
Date: 2019-03-11 00:00:00
Tasks: Additive models

We present a unified framework for estimation and analysis of generalized
additive models in high dimensions. The framework defines a large class of
penalized regression estimators, encompassing many existing methods. An
efficient computational algorithm for this class is presented that easily
scales to thousands of observations and features. We prove minimax optimal
convergence bounds for this class under a weak compatibility condition. In
addition, we characterize the rate of convergence when this compatibility
condition is not met. Finally, we also show that the optimal penalty parameters
for structure and sparsity penalties in our framework are linked, allowing
cross-validation to be conducted over only a single tuning parameter. We
complement our theoretical results with empirical studies comparing some
existing methods within this framework.
Financial Applications of Gaussian Processes and Bayesian Optimization
Authors: Joan Gonzalvez, Edmond Lezmi, Thierry Roncalli, Jiali Xu
Date: 2019-03-12 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Gaussian Processes, global-optimization

In the last five years, the financial industry has been impacted by the
emergence of digitalization and machine learning. In this article, we explore
two methods that have undergone rapid development in recent years: Gaussian
processes and Bayesian optimization. Gaussian processes can be seen as a
generalization of Gaussian random vectors and are associated with the
development of kernel methods. Bayesian optimization is an approach for
performing derivative-free global optimization in a small dimension, and uses
Gaussian processes to locate the global maximum of a black-box function. The
first part of the article reviews these two tools and shows how they are
connected. In particular, we focus on the Gaussian process regression, which is
the core of Bayesian machine learning, and the issue of hyperparameter
selection. The second part is dedicated to two financial applications. We first
consider the modeling of the term structure of interest rates. More precisely,
we test the fitting method and compare the GP prediction and the random walk
model. The second application is the construction of trend-following
strategies, in particular the online estimation of trend and covariance
windows.
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Authors: Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas
Date: 2019-03-12 00:00:00
Tasks: Clustering

Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets.
GOGGLES: Automatic Image Labeling with Affinity Coding
Authors: Nilaksh Das, Sanya Chaba, Renzhi Wu, Sakshi Gandhi, Duen Horng Chau, Xu Chu
Date: 2019-03-11 00:00:00
Tasks: Few-Shot Learning

Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. However, data programming relies on designing labeling functions which still requires significant domain expertise. Also, it is prohibitively difficult to write labeling functions for image datasets as it is hard to express domain knowledge using raw features for images (pixels). We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. The core premise of affinity coding is that the affinity scores of instance pairs belonging to the same class on average should be higher than those of pairs belonging to different classes, according to some affinity functions. We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set. We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a minimum of 71% to a maximum of 98% without requiring any extensive human annotation. In terms of end-to-end performance, GOGGLES outperforms the state-of-the-art data programming system Snuba by 21% and a state-of-the-art few-shot learning technique by 5%, and is only 7% away from the fully supervised upper bound.
Generating superpixels using deep image representations
Authors: Thomas Verelst, Matthew Blaschko, Maxim Berman
Date: 2019-03-11 00:00:00
Tasks: Clustering, General Classification, Object, Object Tracking, Segmentation, Semantic Segmentation, Superpixels

Superpixel algorithms are a common pre-processing step for computer vision
algorithms such as segmentation, object tracking and localization. Many
superpixel methods only rely on colors features for segmentation, limiting
performance in low-contrast regions and applicability to infrared or medical
images where object boundaries have wide appearance variability. We study the
inclusion of deep image features in the SLIC superpixel algorithm to exploit
higher-level image representations. In addition, we devise a trainable
superpixel algorithm, yielding an intermediate domain-specific image
representation that can be applied to different tasks. A clustering-based
superpixel algorithm is transformed into a pixel-wise classification task and
superpixel training data is derived from semantic segmentation datasets. Our
results demonstrate that this approach is able to improve superpixel quality
consistently.
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
Authors: Ren Yang, Xiaoyan Sun, Mai Xu, Wen-Jun Zeng
Date: 2019-03-11 00:00:00

The past decade has witnessed great success in applying deep learning to
enhance the quality of compressed video. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.
Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement
Authors: Xiaoshui Huang, Lixin Fan, Qiang Wu, Jian Zhang, Chun Yuan
Date: 2019-03-11 00:00:00
Tasks: Point Cloud Registration

Many types of 3D acquisition sensors have emerged in recent years and point
cloud has been widely used in many areas. Accurate and fast registration of
cross-source 3D point clouds from different sensors is an emerged research
problem in computer vision. This problem is extremely challenging because
cross-source point clouds contain a mixture of various variances, such as
density, partial overlap, large noise and outliers, viewpoint changing. In this
paper, an algorithm is proposed to align cross-source point clouds with both
high accuracy and high efficiency. There are two main contributions: firstly,
two components, the weak region affinity and pixel-wise refinement, are
proposed to maintain the global and local information of 3D point clouds. Then,
these two components are integrated into an iterative tensor-based registration
algorithm to solve the cross-source point cloud registration problem. We
conduct experiments on synthetic cross-source benchmark dataset and real
cross-source datasets. Comparison with six state-of-the-art methods, the
proposed method obtains both higher efficiency and accuracy.
Transfer Adaptation Learning: A Decade Survey
Authors: Lei Zhang, Xinbo Gao
Date: 2019-03-12 00:00:00
Tasks: Survey

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance, which is presenting a blowout publication trend. This paper surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of transfer adaptation learning being created by researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges and under-studied issues (universality, interpretability, and credibility) to be broken in the field toward universal representation and safe applications in open-world scenarios.
Knowledge Adaptation for Efficient Semantic Segmentation
Authors: Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan
Date: 2019-03-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Knowledge Distillation, Segmentation, Semantic Segmentation

Both accuracy and efficiency are of significant importance to the task of
semantic segmentation. Existing deep FCNs suffer from heavy computations due to
a series of high-resolution feature maps for preserving the detailed knowledge
in dense estimation. Although reducing the feature map resolution (i.e.,
applying a large overall stride) via subsampling operations (e.g., pooling and
convolution striding) can instantly increase the efficiency, it dramatically
decreases the estimation accuracy. To tackle this dilemma, we propose a
knowledge distillation method tailored for semantic segmentation to improve the
performance of the compact FCNs with large overall stride. To handle the
inconsistency between the features of the student and teacher network, we
optimize the feature similarity in a transferred latent domain formulated by
utilizing a pre-trained autoencoder. Moreover, an affinity distillation module
is proposed to capture the long-range dependency by calculating the non-local
interactions across the whole image. To validate the effectiveness of our
proposed method, extensive experiments have been conducted on three popular
benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly
competitive baseline, our proposed method can improve the performance of a
student network by 2.5\% (mIOU boosts from 70.2 to 72.7 on the cityscapes test
set) and can train a better compact model with only 8\% float operations
(FLOPS) of a model that achieves comparable performances.
A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images
Authors: Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, Xin Tong
Date: 2019-03-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder

This paper focuses on the challenging task of learning 3D object surface
reconstructions from single RGB images. Existing methods achieve varying
degrees of success by using different geometric representations. However, they
all have their own drawbacks, and cannot well reconstruct those surfaces of
complex topologies. To this end, we propose in this paper a skeleton-bridged,
stage-wise learning approach to address the challenge. Our use of skeleton is
due to its nice property of topology preservation, while being of lower
complexity to learn. To learn skeleton from an input image, we design a deep
architecture whose decoder is based on a novel design of parallel streams
respectively for synthesis of curve- and surface-like skeleton points. We use
different shape representations of point cloud, volume, and mesh in our
stage-wise learning, in order to take their respective advantages. We also
propose multi-stage use of the input image to correct prediction errors that
are possibly accumulated in each stage. We conduct intensive experiments to
investigate the efficacy of our proposed approach. Qualitative and quantitative
results on representative object categories of both simple and complex
topologies demonstrate the superiority of our approach over existing ones. We
will make our ShapeNet-Skeleton dataset publicly available.
Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition
Authors: Yuhang Wu, Ioannis A. Kakadiaris
Date: 2019-03-12 00:00:00
Tasks: Face Recognition, Face Verification, Robust Face Recognition

Concatenation of the deep network representations extracted from different
facial patches helps to improve face recognition performance. However, the
concatenated facial template increases in size and contains redundant
information. Previous solutions aim to reduce the dimensionality of the facial
template without considering the occlusion pattern of the facial patches. In
this paper, we propose an occlusion-guided compact template learning (OGCTL)
approach that only uses the information from visible patches to construct the
compact template. The compact face representation is not sensitive to the
number of patches that are used to construct the facial template and is more
suitable for incorporating the information from different view angles for
image-set based face recognition. Instead of using occlusion masks in face
matching (e.g., DPRFS [38]), the proposed method uses occlusion masks in
template construction and achieves significantly better image-set based face
verification performance on a challenging database with a template size that is
an order-of-magnitude smaller than DPRFS.
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar
Authors: Arash Akbarinia, Karl R. Gegenfurtner
Date: 2019-03-12 00:00:00
Tasks: Transfer Learning

Machine learning is advancing towards a data-science approach, implying a
necessity to a line of investigation to divulge the knowledge learnt by deep
neuronal networks. Limiting the comparison among networks merely to a
predefined intelligent ability, according to ground truth, does not suffice, it
should be associated with innate similarity of these artificial entities. Here,
we analysed multiple instances of an identical architecture trained to classify
objects in static images (CIFAR and ImageNet data sets). We evaluated the
performance of the networks under various distortions and compared it to the
intrinsic similarity between their constituent kernels. While we expected a
close correspondence between these two measures, we observed a puzzling
phenomenon. Pairs of networks whose kernels' weights are over 99.9% correlated
can exhibit significantly different performances, yet other pairs with no
correlation can reach quite compatible levels of performance. We show
implications of this for transfer learning, and argue its importance in our
general understanding of what intelligence is, whether natural or artificial.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Authors: Ziyuan Zhao, Xiaoman Zhang, Cen Chen, Wei Li, Songyou Peng, Jie Wang, Xulei Yang, Le Zhang, Zeng Zeng
Date: 2019-03-12 00:00:00
Tasks: Deep Learning

Segmentation stands at the forefront of many high-level vision tasks. In this
study, we focus on segmenting finger bones within a newly introduced
semi-supervised self-taught deep learning framework which consists of a student
network and a stand-alone teacher module. The whole system is boosted in a
life-long learning manner wherein each step the teacher module provides a
refinement for the student network to learn with newly unlabeled data.
Experimental results demonstrate the superiority of the proposed method over
conventional supervised deep learning methods.