pwc-archive/papers-with-abstracts huggingface dataset:
HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering
Authors: Vidyasagar Sadhu, Saman Zonouz, Vincent Sritapan, Dario Pompili
Date: 2019-04-21 00:00:00
Tasks: Collaborative Filtering, Privacy Preserving

Mobile context determination is an important step for many context aware
services such as location-based services, enterprise policy enforcement,
building or room occupancy detection for power or HVAC operation, etc.
Especially in enterprise scenarios where policies (e.g., attending a
confidential meeting only when the user is in "Location X") are defined based
on mobile context, it is paramount to verify the accuracy of the mobile
context. To this end, two stochastic models based on the theory of Hidden
Markov Models (HMMs) to obtain mobile context are proposed-personalized model
(HPContext) and collaborative filtering model (HCFContext). The former predicts
the current context using sequential history of the user's past context
observations, the latter enhances HPContext with collaborative filtering
features, which enables it to predict the current context of the primary user
based on the context observations of users related to the primary user, e.g.,
same team colleagues in company, gym friends, family members, etc. Each of the
proposed models can also be used to enhance or complement the context obtained
from sensors. Furthermore, since privacy is a concern in collaborative
filtering, a privacy-preserving method is proposed to derive HCFContext model
parameters based on the concepts of homomorphic encryption. Finally, these
models are thoroughly validated on a real-life dataset.
Deep Metric Learning Beyond Binary Supervision
Authors: Sungyeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, Suha Kwak
Date: 2019-04-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Captioning, Image Retrieval, Metric Learning, Retrieval, Semantic Similarity, Semantic Textual Similarity, Triplet

Metric Learning for visual similarity has mostly adopted binary supervision
indicating whether a pair of images are of the same class or not. Such a binary
indicator covers only a limited subset of image relations, and is not
sufficient to represent semantic similarity between images described by
continuous and/or structured labels such as object poses, image captions, and
scene graphs. Motivated by this, we present a novel method for deep metric
learning using continuous labels. First, we propose a new triplet loss that
allows distance ratios in the label space to be preserved in the learned metric
space. The proposed loss thus enables our model to learn the degree of
similarity rather than just the order. Furthermore, we design a triplet mining
strategy adapted to metric learning with continuous labels. We address three
different image retrieval tasks with continuous labels in terms of human poses,
room layouts and image captions, and demonstrate the superior performance of
our approach compared to previous methods.
Explaining a prediction in some nonlinear models
Authors: Cosimo Izzo
Date: 2019-04-21 00:00:00
Tasks: General Classification, Prediction, regression

In this article we will analyse how to compute the contribution of each input value to its aggregate output in some nonlinear models. Regression and classification applications, together with related algorithms for deep neural networks are presented. The proposed approach merges two methods currently present in the literature: integrated gradient and deep Taylor decomposition. Compared to DeepLIFT and Deep SHAP, it provides a natural choice of the reference point peculiar to the model at use.
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
Authors: Qian Yang, Aaron Steinfeld, John Zimmerman
Date: 2019-04-21 00:00:00
Tasks: Decision Making

Clinical decision support tools (DST) promise improved healthcare outcomes by
offering data-driven insights. While effective in lab settings, almost all DSTs
have failed in practice. Empirical research diagnosed poor contextual fit as
the cause. This paper describes the design and field evaluation of a radically
new form of DST. It automatically generates slides for clinicians' decision
meetings with subtly embedded machine prognostics. This design took inspiration
from the notion of "Unremarkable Computing", that by augmenting the users'
routines technology/AI can have significant importance for the users yet remain
unobtrusive. Our field evaluation suggests clinicians are more likely to
encounter and embrace such a DST. Drawing on their responses, we discuss the
importance and intricacies of finding the right level of unremarkableness in
DST design, and share lessons learned in prototyping critical AI systems as a
situated experience.
TiK-means: $K$-means clustering for skewed groups
Authors: Nicholas S. Berry, Ranjan Maitra
Date: 2019-04-21 00:00:00
Tasks: Clustering

The $K$-means algorithm is extended to allow for partitioning of skewed
groups. Our algorithm is called TiK-Means and contributes a $K$-means type
algorithm that assigns observations to groups while estimating their
skewness-transformation parameters. The resulting groups and transformation
reveal general-structured clusters that can be explained by inverting the
estimated transformation. Further, a modification of the jump statistic chooses
the number of groups. Our algorithm is evaluated on simulated and real-life
datasets and then applied to a long-standing astronomical dispute regarding the
distinct kinds of gamma ray bursts.
Generative Exploration and Exploitation
Authors: Jiechuan Jiang, Zongqing Lu
Date: 2019-04-21 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to overcome sparse reward. GENE automatically generates start states to encourage the agent to explore the environment and to exploit received reward signals. GENE can adaptively tradeoff between exploration and exploitation according to the varying distributions of states experienced by the agent as the learning progresses. GENE relies on no prior knowledge about the environment and can be combined with any RL algorithm, no matter on-policy or off-policy, single-agent or multi-agent. Empirically, we demonstrate that GENE significantly outperforms existing methods in three tasks with only binary rewards, including Maze, Maze Ant, and Cooperative Navigation. Ablation studies verify the emergence of progressive exploration and automatic reversing.
Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
Authors: Yuxuan Du, Min-Hsiu Hsieh, DaCheng Tao
Date: 2019-04-21 00:00:00

The exploration of quantum algorithms that possess quantum advantages is a
central topic in quantum computation and quantum information processing. One
potential candidate in this area is quantum generative adversarial learning
(QuGAL), which conceptually has exponential advantages over classical
adversarial networks. However, the corresponding learning algorithm remains
obscured. In this paper, we propose the first quantum generative adversarial
learning algorithm-- the quantum multiplicative matrix weight algorithm
(QMMW)-- which enables the efficient processing of fundamental tasks. The
computational complexity of QMMW is polynomially proportional to the number of
training rounds and logarithmically proportional to the input size. The core
concept of the proposed algorithm combines QuGAL with online learning. We
exploit the implementation of QuGAL with parameterized quantum circuits, and
numerical experiments for the task of entanglement test for pure state are
provided to support our claims.
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
Authors: Chaofan Tao, Fengmao Lv, Lixin Duan, Min Wu
Date: 2019-04-21 00:00:00
Tasks: Domain Adaptation

How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distribution change. In this paper, we propose an easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on adversarial learning. Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples. Specifically, we set an unfair multi-class classifier named categorical discriminator, which classifies source samples accurately but be confused about the categories of target samples. The generator learns a common subspace that aligns the unlabeled samples based on the target pseudo-labels. For MMEN, we also provide theoretical explanations to show that the learning of feature alignment reduces domain mismatch at the category level. Experimental results on various benchmark datasets demonstrate the effectiveness of our method over existing state-of-the-art baselines.
A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search
Authors: Mehdi Neshat, Bradley Alexander, Nataliia Sergiienko, Markus Wagner
Date: 2019-04-21 00:00:00
Tasks: Position

Renewable energy, such as ocean wave energy, plays a pivotal role in addressing the tremendous growth of global energy demand. It is expected that wave energy will be one of the fastest-growing energy resources in the next decade, offering an enormous potential source of sustainable energy. This research investigates the placement optimization of oscillating buoy-type wave energy converters (WEC). The design of a wave farm consisting of an array of fully submerged three-tether buoys is evaluated. In a wave farm, buoy positions have a notable impact on the farm's output. Optimizing the buoy positions is a challenging research problem because of very complex interactions (constructive and destructive) between buoys. The main purpose of this research is maximizing the power output of the farm through the placement of buoys in a size-constrained environment. This paper proposes a new hybrid approach of the heuristic local search combined with a numerical optimization method that utilizes a knowledge-based surrogate power model. We compare the proposed hybrid method with other state-of-the-art search methods in five different wave scenarios -- one simplified irregular wave model and four real wave climates. Our method considerably outperforms all previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes.
Obfuscation for Privacy-preserving Syntactic Parsing
Authors: Zhifeng Hu, Serhii Havrylov, Ivan Titov, Shay B. Cohen
Date: 2019-04-21 00:00:00
Proceeding: WS 2020 7
Tasks: Privacy Preserving, Sentence

The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption. Our primary tool is {\em obfuscation}, relying on the properties of natural language. Specifically, a given English text is obfuscated using a neural model that aims to preserve the syntactic relationships of the original sentence so that the obfuscated sentence can be parsed instead of the original one. The model works at the word level, and learns to obfuscate each word separately by changing it into a new word that has a similar syntactic role. The text obfuscated by our model leads to better performance on three syntactic parsers (two dependency and one constituency parsers) in comparison to an upper-bound random substitution baseline. More specifically, the results demonstrate that as more terms are obfuscated (by their part of speech), the substitution upper bound significantly degrades, while the neural model maintains a relatively high performing parser. All of this is done without much sacrifice of privacy compared to the random substitution upper bound. We also further analyze the results, and discover that the substituted words have similar syntactic properties, but different semantic content, compared to the original words.
TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
Authors: Wayne Wu, Kaidi Cao, Cheng Li, Chen Qian, Chen Change Loy
Date: 2019-04-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation aims at learning a mapping between
two visual domains. However, learning a translation across large geometry
variations always ends up with failure. In this work, we present a novel
disentangle-and-translate framework to tackle the complex objects
image-to-image translation task. Instead of learning the mapping on the image
space directly, we disentangle image space into a Cartesian product of the
appearance and the geometry latent spaces. Specifically, we first introduce a
geometry prior loss and a conditional VAE loss to encourage the network to
learn independent but complementary representations. The translation is then
built on appearance and geometry space separately. Extensive experiments
demonstrate the superior performance of our method to other state-of-the-art
approaches, especially in the challenging near-rigid and non-rigid objects
translation tasks. In addition, by taking different exemplars as the appearance
references, our method also supports multimodal translation. Project page:
https://wywu.github.io/projects/TGaGa/TGaGa.html
A Simple Pooling-Based Design for Real-Time Salient Object Detection
Authors: Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, Jianmin Jiang
Date: 2019-04-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

We solve the problem of salient object detection by investigating how to
expand the role of pooling in convolutional neural networks. Based on the
U-shape architecture, we first build a global guidance module (GGM) upon the
bottom-up pathway, aiming at providing layers at different feature levels the
location information of potential salient objects. We further design a feature
aggregation module (FAM) to make the coarse-level semantic information well
fused with the fine-level features from the top-down pathway. By adding FAMs
after the fusion operations in the top-down pathway, coarse-level features from
the GGM can be seamlessly merged with features at various scales. These two
pooling-based modules allow the high-level semantic features to be
progressively refined, yielding detail enriched saliency maps. Experiment
results show that our proposed approach can more accurately locate the salient
objects with sharpened details and hence substantially improve the performance
compared to the previous state-of-the-arts. Our approach is fast as well and
can run at a speed of more than 30 FPS when processing a $300 \times 400$
image. Code can be found at http://mmcheng.net/poolnet/.
Complete Scene Reconstruction by Merging Images and Laser Scans
Authors: Xiang Gao, Shuhan Shen, Lingjie Zhu, Tianxin Shi, Zhiheng Wang, Zhanyi Hu
Date: 2019-04-21 00:00:00

Image based modeling and laser scanning are two commonly used approaches in large-scale architectural scene reconstruction nowadays. In order to generate a complete scene reconstruction, an effective way is to completely cover the scene using ground and aerial images, supplemented by laser scanning on certain regions with low texture and complicated structure. Thus, the key issue is to accurately calibrate cameras and register laser scans in a unified framework. To this end, we proposed a three-step pipeline for complete scene reconstruction by merging images and laser scans. First, images are captured around the architecture in a multi-view and multi-scale way and are feed into a structure-from-motion (SfM) pipeline to generate SfM points. Then, based on the SfM result, the laser scanning locations are automatically planned by considering textural richness, structural complexity of the scene and spatial layout of the laser scans. Finally, the images and laser scans are accurately merged in a coarse-to-fine manner. Experimental evaluations on two ancient Chinese architecture datasets demonstrate the effectiveness of our proposed complete scene reconstruction pipeline.
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Authors: Feng Yin, Lishuo Pan, Xinwei He, Tianshi Chen, Sergios Theodoridis, Zhi-Quan, Luo
Date: 2019-04-21 00:00:00
Tasks: Gaussian Processes, regression, Time Series, Time Series Analysis

Gaussian processes (GP) for machine learning have been studied systematically
over the past two decades and they are by now widely used in a number of
diverse applications. However, GP kernel design and the associated
hyper-parameter optimization are still hard and to a large extend open
problems. In this paper, we consider the task of GP regression for time series
modeling and analysis. The underlying stationary kernel can be approximated
arbitrarily close by a new proposed grid spectral mixture (GSM) kernel, which
turns out to be a linear combination of low-rank sub-kernels. In the case where
a large number of the sub-kernels are used, either the Nystr\"{o}m or the
random Fourier feature approximations can be adopted to deal efficiently with
the computational demands. The unknown GP hyper-parameters consist of the
non-negative weights of all sub-kernels as well as the noise variance; their
estimation is performed via the maximum-likelihood (ML) estimation framework.
Two efficient numerical optimization methods for solving the unknown
hyper-parameters are derived, including a sequential majorization-minimization
(MM) method and a non-linearly constrained alternating direction of multiplier
method (ADMM). The MM matches perfectly with the proven low-rank property of
the proposed GSM sub-kernels and turns out to be a part of efficiency, stable,
and efficient solver, while the ADMM has the potential to generate better local
minimum in terms of the test MSE. Experimental results, based on various
classic time series data sets, corroborate that the proposed GSM kernel-based
GP regression model outperforms several salient competitors of similar kind in
terms of prediction mean-squared-error and numerical stability.
A Study on Agreement in PICO Span Annotations
Authors: Grace E. Lee, Aixin Sun
Date: 2019-04-21 00:00:00
Tasks: PICO

In evidence-based medicine, relevance of medical literature is determined by
predefined relevance conditions. The conditions are defined based on PICO
elements, namely, Patient, Intervention, Comparator, and Outcome. Hence, PICO
annotations in medical literature are essential for automatic relevant document
filtering. However, defining boundaries of text spans for PICO elements is not
straightforward. In this paper, we study the agreement of PICO annotations made
by multiple human annotators, including both experts and non-experts.
Agreements are estimated by a standard span agreement (i.e., matching both
labels and boundaries of text spans), and two types of relaxed span agreement
(i.e., matching labels without guaranteeing matching boundaries of spans).
Based on the analysis, we report two observations: (i) Boundaries of PICO span
annotations by individual human annotators are very diverse. (ii) Despite the
disagreement in span boundaries, general areas of the span annotations are
broadly agreed by annotators. Our results suggest that applying a standard
agreement alone may undermine the agreement of PICO spans, and adopting both a
standard and a relaxed agreements is more suitable for PICO span evaluation.
Understanding the Stability of Medical Concept Embeddings
Authors: Grace E. Lee, Aixin Sun
Date: 2019-04-21 00:00:00
Tasks: Word Embeddings

Frequency is one of the major factors for training quality word embeddings. Several work has recently discussed the stability of word embeddings in general domain and suggested factors influencing the stability. In this work, we conduct a detailed analysis on the stability of concept embeddings in medical domain, particularly the relation with concept frequency. The analysis reveals the surprising high stability of low-frequency concepts: low-frequency (<100) concepts have the same high stability as high-frequency (>1000) concepts. To develop a deeper understanding of this finding, we propose a new factor, the noisiness of context words, which influences the stability of medical concept embeddings, regardless of frequency. We evaluate the proposed factor by showing the linear correlation with the stability of medical concept embeddings. The correlations are clear and consistent with various groups of medical concepts. Based on the linear relations, we make suggestions on ways to adjust the noisiness of context words for the improvement of stability. Finally, we demonstrate that the proposed factor extends to the word embedding stability in general domain.
DeepCaps: Going Deeper with Capsule Networks
Authors: Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, Ranga Rodrigo
Date: 2019-04-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder

Capsule Network is a promising concept in deep learning, yet its true
potential is not fully realized thus far, providing sub-par performance on
several key benchmark datasets with complex data. Drawing intuition from the
success achieved by Convolutional Neural Networks (CNNs) by going deeper, we
introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D
convolution based dynamic routing algorithm. With DeepCaps, we surpass the
state-of-the-art results in the capsule network domain on CIFAR10, SVHN and
Fashion MNIST, while achieving a 68% reduction in the number of parameters.
Further, we propose a class-independent decoder network, which strengthens the
use of reconstruction loss as a regularization term. This leads to an
interesting property of the decoder, which allows us to identify and control
the physical attributes of the images represented by the instantiation
parameters.
Good-Enough Compositional Data Augmentation
Authors: Jacob Andreas
Date: 2019-04-21 00:00:00
Proceeding: ACL 2020 6
Tasks: Data Augmentation, Diagnostic, Inductive Bias, Language Modelling, Semantic Parsing

We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model-agnostic and useful for a variety of tasks. Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task. Applied to n-gram language models, it reduces perplexity by roughly 1% on small corpora in several languages.