bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Repair in Ontology-based Data Access Evolving Systems
Authors: Lembo Domenico , Rosati Riccardo , Santarelli Valerio , Savo Domenico Fabio , Thorstensen Evgenij
Published: July 2017

In this paper we study the evolution of ontology-based data access (OBDA) specifications, and focus on the case in which the ontology and/or the data source schema change, which may require a modification to the mapping between them to preserve both consistency and knowledge. Our approach is based on the idea of repairing the mapping according to the usual principle of minimal change and on a recent, mapping-based notion of consistency of the specification. We define and analyze two notions of mapping repair under ontology and source schema update. We then present a set of results on the complexity of query answering in the above framework, when the ontology is expressed in DL-LiteR.
The Bag Semantics of Ontology-Based Data Access
Authors: Nikolaou Charalampos , Kostylev Egor V. , Konstantinidis George , Kaminski Mark , Cuenca Grau Bernardo , Horrocks Ian
Published: July 2017

Ontology-based data access (OBDA) is a popular approach for integrating and querying multiple data sources by means of a shared ontology. The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates. Motivated by the need for OBDA systems supporting database-style aggregate queries, we propose a bag semantics for OBDA, where duplicate tuples in the views defined by the mappings are retained, as is the case in standard databases. We show that bag semantics makes conjunctive query answering in OBDA coNP-hard in data complexity. To regain tractability, we consider a rather general class of queries and show its rewritability to a generalisation of the relational calculus to bags.
Inferring Human Attention by Learning Latent Intentions
Authors: Wei Ping , Xie Dan , Zheng Nanning , Zhu Song-Chun
Published: July 2017

This paper addresses the problem of inferring 3D human attention in RGB-D videos at scene scale. 3D human attention describes where a human is looking in 3D scenes. We propose a probabilistic method to jointly model attention, intentions, and their interactions. Latent intentions guide human attention which conversely reveals the intention features. This mutual interaction makes attention inference a joint optimization with latent intentions. An EM-based approach is adopted to learn the latent intentions and model parameters. Given an RGB-D video with 3D human skeletons, a joint-state dynamic programming algorithm is utilized to jointly infer the latent intentions, the 3D attention directions, and the attention voxels in scene point clouds. Experiments on a new 3D human attention dataset prove the strength of our method.
Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models
Authors: Wilson Nic , George Anne-Marie
Published: July 2017

We analyse preference inference, through consistency, for general preference languages based on lexicographic models. We identify a property, which we call strong compositionality, that applies for many natural kinds of preference statement, and that allows a greedy algorithm for determining consistency of a set of preference statements. We also consider different natural definitions of optimality, and their relations to each other, for general preference languages based on lexicographic models. Based on our framework, we show that testing consistency, and thus inference, is polynomial for a specific preference language

which allows strict and non-strict statements, comparisons between outcomes and between partial tuples, both ceteris paribus and strong statements, and their combination. Computing different kinds of optimal sets is also shown to be polynomial; this is backed up by our experimental results.
Knowledge Graph Representation with Jointly Structural and Textual Encoding
Authors: Xu Jiacheng , Qiu Xipeng , Chen Kan , Huang Xuanjing
Published: July 2017

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline and obtain state-of-the-art results on link prediction and triplet classification tasks.
Aggregating Crowd Wisdoms with Label-aware Autoencoders
Authors: Yin Li'ang , Han Jianhua , Zhang Weinan , Yu Yong
Published: July 2017

Aggregating crowd wisdoms takes multiple labels from various sources and infers true labels for objects. Recent research work makes progress by learning source credibility from data and roughly form three kinds of modeling frameworks: weighted majority voting, trust propagation, and generative models. In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms. LAA integrates a classifier and a reconstructor into a unified model to infer labels in an unsupervised manner. Analogizing classical autoencoders, we can regard the classifier as an encoder, the reconstructor as a decoder, and inferred labels as latent features. To the best of our knowledge, it is the first trial to combine label aggregation with autoencoders. We adopt networks to implement the classifier and the reconstructor which have the potential to automatically learn underlying patterns of source credibility. To further improve inference accuracy, we introduce object ambiguity and latent aspects into LAA. Experiments on three real-world datasets show that proposed models achieve impressive inference accuracy improvement over state-of-the-art models.
Universal Reinforcement Learning Algorithms: Survey and Experiments
Authors: Aslanides John , Leike Jan , Hutter Marcus
Published: July 2017

Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal reinforcement learning (URL) is concerned with algorithms that make as few assumptions as possible about the environment. The universal Bayesian agent AIXI and a family of related URL algorithms have been developed in this setting. While numerous theoretical optimality results have been proven for these agents, there has been no empirical investigation of their behavior to date. We present a short and accessible survey of these URL algorithms under a unified notation and framework, along with results of some experiments that qualitatively illustrate some properties of the resulting policies, and their relative performance on partially-observable gridworld environments. We also present an open- source reference implementation of the algorithms which we hope will facilitate further understanding of, and experimentation with, these ideas.
Mining Convex Polygon Patterns with Formal Concept Analysis
Authors: Belfodil Aimene , Kuznetsov Sergei O. , Robardet Céline , Kaytoue Mehdi
Published: July 2017

Pattern mining is an important task in AI for eliciting hypotheses from the data. When it comes to spatial data, the geo-coordinates are often considered independently as two different attributes. Consequently, rectangular patterns are searched for. Such an arbitrary form is not able to capture interesting regions in general. We thus introduce convex polygons, a good trade-off for capturing high density areas in any pattern mining task. Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms that we compare experimentally.
Fair Division of a Graph
Authors: Bouveret Sylvain , Cechlárová Katarína , Elkind Edith , Igarashi Ayumi , Peters Dominik
Published: July 2017

We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envy-freeness and maximin share guarantee. While finding good allocations according to these solution concepts is computationally hard in general, we design efficient algorithms for special cases wherethe underlying graph has simple structure, and/or the number of agents---or, less restrictively, the number of agent types---is small. In particular, despite non-existence results in the general case, we prove that for acyclic graphs a maximin share allocation always exists and can be found efficiently.
Human-Centric Justification of Machine Learning Predictions
Authors: Biran Or , McKeown Kathleen
Published: July 2017

Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation. Through a task-based experiment, we show that our approach significantly helps humans to correctly decide whether or not predictions are accurate, and significantly increases their satisfaction with the justification.
Optimal Feature Selection for Decision Robustness in Bayesian Networks
Authors: Choi YooJung , Darwiche Adnan , Van den Broeck Guy
Published: July 2017

In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation. Moreover, we develop a search algorithm for optimal feature selection that utilizes efficient incremental circuit modifications. Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach.
Stacked Similarity-Aware Autoencoders
Authors: Chu Wenqing , Cai Deng
Published: July 2017

As one of the most popular unsupervised learning approaches, the autoencoder aims at transforming the inputs to the outputs with the least discrepancy. The conventional autoencoder and most of its variants only consider the one-to-one reconstruction, which ignores the intrinsic structure of the data and may lead to overfitting. In order to preserve the latent geometric information in the data, we propose the stacked similarity-aware autoencoders. To train each single autoencoder, we first obtain the pseudo class label of each sample by clustering the input features. Then the hidden codes of those samples sharing the same category label will be required to satisfy an additional similarity constraint. Specifically, the similarity constraint is implemented based on an extension of the recently proposed center loss. With this joint supervision of the autoencoder reconstruction error and the center loss, the learned feature representations not only can reconstruct the original data, but also preserve the geometric structure of the data. Furthermore, a stacked framework is introduced to boost the representation capacity. The experimental results on several benchmark datasets show the remarkable performance improvement of the proposed algorithm compared with other autoencoder based approaches.
Analogy-preserving functions: A way to extend Boolean samples
Authors: Couceiro Miguel , Hug Nicolas , Prade Henri , Richard Gilles
Published: July 2017

Training set extension is an important issue in machine learning. Indeed when the examples at hand are in a limited quantity, the performances of standard classifiers may significantly decrease and it can be helpful to build additional examples. In this paper, we consider the use of analogical reasoning, and more particularly of analogical proportions for extending training sets. Here the ground truth labels are considered to be given by a (partially known) function. We examine the conditions that are required for such functions to ensure an error-free extension in a Boolean setting. To this end, we introduce the notion of Analogy Preserving (AP) functions, and we prove that their class is the class of affine Boolean functions. This noteworthy theoretical result is complemented with an empirical investigation of approximate AP functions, which suggests that they remain suitable for training set extension.
Real-Time Navigation in Classical Platform Games via Skill Reuse
Authors: Dann Michael , Zambetta Fabio , Thangarajah John
Published: July 2017

In platform videogames, players are frequently tasked with solving medium-term navigation problems in order to gather items or powerups. Artificial agents must generally obtain some form of direct experience before they can solve such tasks. Experience is gained either through training runs, or by exploiting knowledge of the game's physics to generate detailed simulations. Human players, on the other hand, seem to look ahead in high-level, abstract steps. Motivated by human play, we introduce an approach that leverages not only abstract "skills", but also knowledge of what those skills can and cannot achieve. We apply this approach to Infinite Mario, where despite facing randomly generated, maze-like levels, our agent is capable of deriving complex plans in real-time, without relying on perfect knowledge of the game's physics.
Collaborative Rating Allocation
Authors: Du Yali , Xu Chang , Tao Dacheng
Published: July 2017

This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed ``energy limited''. Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Authors: Chakraborti Tathagata , Sreedharan Sarath , Zhang Yu , Kambhampati Subbarao
Published: July 2017

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
Object Detection Meets Knowledge Graphs
Authors: Fang Yuan , Kuan Kingsley , Lin Jie , Tan Cheston , Chandrasekhar Vijay
Published: July 2017

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve better consistency with background knowledge. Finally, empirical evaluation on two benchmark datasets show that our approach can significantly increase recall by up to 6.3 points without compromising mean average precision, when compared to the state-of-the-art baseline.
Dynamic Multi-Task Learning with Convolutional Neural Network
Authors: Fang Yuchun , Ma Zhengyan , Zhang Zhaoxiang , Zhang Xu-Yao , Bai Xiang
Published: July 2017

Multi-task learning and deep convolutional neural network (CNN) have been successfully used in various fields. This paper considers the integration of CNN and multi-task learning in a novel way to further improve the performance of multiple related tasks. Existing multi-task CNN models usually empirically combine different tasks into a group which is then trained jointly with a strong assumption of model commonality. Furthermore, traditional approaches usually only consider small number of tasks with rigid structure, which is not suitable for large-scale applications. In light of this, we propose a dynamic multi-task CNN model to handle these problems. The proposed model directly learns the task relations from data instead of subjective task grouping. Due to its flexible structure, it supports task-wise incremental training, which is useful for efficient training of massive tasks. Specifically, we add a new task transfer connection (TTC) between the layers of each task. The learned TTC is able to reflect the correlation among different tasks guiding the model dynamically adjusting the multiplexing of the information among different tasks. With the help of TTC, multiple related tasks can further boost the whole performance for each other. Experiments demonstrate that the proposed dynamic multi-task CNN model outperforms traditional approaches.
Identifying Human Mobility via Trajectory Embeddings
Authors: Gao Qiang , Zhou Fan , Zhang Kunpeng , Trajcevski Goce , Luo Xucheng , Zhang Fengli
Published: July 2017

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.
Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits
Authors: Chakraborty Mithun , Chua Kai Yee Phoebe , Das Sanmay , Juba Brendan
Published: July 2017

In this paper, we introduce a multi-agent multi-armed bandit-based model for ad hoc teamwork with expensive communication. The goal of the team is to maximize the total reward gained from pulling arms of a bandit over a number of epochs. In each epoch, each agent decides whether to pull an arm, or to broadcast the reward it obtained in the previous epoch to the team and forgo pulling an arm. These decisions must be made only on the basis of the agent’s private information and the public information broadcast prior to that epoch. We first benchmark the achievable utility by analyzing an idealized version of this problem where a central authority has complete knowledge of rewards acquired from all arms in all epochs and uses a multiplicative weights update algorithm for allocating arms to agents. We then introduce an algorithm for the decentralized setting that uses a value-of-information based communication strategy and an exploration-exploitation strategy based on the centralized algorithm, and show experimentally that it converges rapidly to the performance of the centralized method.
A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data
Authors: Guo Jinjin , Gong Zhiguo
Published: July 2017

In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data. The proposed model can flexibly accommodate the incremental arriving of the social documents in an online manner by leveraging Dirichlet Process, and a density based technique is exploited to deduce the temporal dynamics of events. The spatial patterns of events are also incorporated in the model by a mixture of Gaussians. To remove the bias caused by the streaming process of the documents, Sequential Monte Carlo is used for the parameter inference. Our extensive experiments over two different real datasets show that the proposed model is capable to extract interpretable events effectively in terms of perplexity and coherence.
Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach
Authors: Hamaguchi Takuo , Oiwa Hidekazu , Shimbo Masashi , Matsumoto Yuji
Published: July 2017

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC: how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time. The experimental results show the effectiveness of our proposed model in the OOKB setting. Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset.
Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel
Authors: He Jia , Du Changying , Du Changde , Zhuang Fuzhen , He Qing , Long Guoping
Published: July 2017

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.
Ordinal Zero-Shot Learning
Authors: Huo Zengwei , Geng Xin
Published: July 2017

Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use. Fortunately in many classification tasks, the class labels are ordered, and therefore closely related to each other. This paper deals with zero-shot learning for ordinal classification. The key idea is using label relevance to expand supervision information from seen labels to unseen labels. The proposed method SIDL generates a supervision intensity distribution (SID) that contains each label's supervision intensity, and then learns a mapping from instance to SID. Experiments on two typical ordinal classification problems, i.e., head pose estimation and age estimation, show that SIDL performs significantly better than the compared regression methods. Furthermore, SIDL appears much more robust against the increase of unseen labels than other compared baselines.
Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Countermeasures by Use of Adversarial Training
Authors: Iwasawa Yusuke , Nakayama Kotaro , Yairi Ikuko , Matsuo Yutaka
Published: July 2017

Deep neural networks have been successfully applied to activity recognition with wearables in terms of recognition performance. However, the black-box nature of neural networks could lead to privacy concerns. Namely, generally it is hard to expect what neural networks learn from data, and so they possibly learn features that highly discriminate user-information unintentionally, which increases the risk of information-disclosure. In this study, we analyzed the features learned by conventional deep neural networks when applied to data of wearables to confirm this phenomenon.Based on the results of our analysis, we propose the use of an adversarial training framework to suppress the risk of sensitive/unintended information disclosure. Our proposed model considers both an adversarial user classifier and a regular activity-classifier during training, which allows the model to learn representations that help the classifier to distinguish the activities but which, at the same time, prevents it from accessing user-discriminative information. This paper provides an empirical validation of the privacy issue and efficacy of the proposed method using three activity recognition tasks based on data of wearables. The empirical validation shows that our proposed method suppresses the concerns without any significant performance degradation, compared to conventional deep nets on all three tasks.
Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression
Authors: Karunaratne Pasan , Moshtaghi Masud , Karunasekera Shanika , Harwood Aaron , Cohn Trevor
Published: July 2017

In time-series forecasting, regression is a popular method, with Gaussian Process Regression widely held to be the state of the art. The versatility of Gaussian Processes has led to them being used in many varied application domains. However, though many real-world applications involve data which follows a working-week structure, where weekends exhibit substantially different behavior to weekdays, methods for explicit modelling of working-week effects in Gaussian Process Regression models have not been proposed. Not explicitly modelling the working week fails to incorporate a signiﬁcant source of information which can be invaluable in forecasting scenarios. In this work we provide novel kernel-combination methods to explicitly model working-week effects in time-series data for more accurate predictions using Gaussian Process Regression. Further, we demonstrate that prediction accuracy can be improved by constraining the non-convex optimization process of ﬁnding optimal hyperparameter values. We validate the effectiveness of our methods by performing multi-step prediction on two real-world publicly available time-series datasets - one relating to electricity Smart Meter data of the University of Melbourne, and the other relating to the counts of pedestrians in the City of Melbourne.
Learning Latest Classifiers without Additional Labeled Data
Authors: Kumagai Atsutoshi , Iwata Tomoharu
Published: July 2017

In various applications such as spam mail classification, the performance of classifiers deteriorates over time. Although retraining classifiers using labeled data helps to maintain the performance, continuously preparing labeled data is quite expensive. In this paper, we propose a method to learn classifiers by using newly obtained unlabeled data, which are easy to prepare, as well as labeled data collected beforehand. A major reason for the performance deterioration is the emergence of new features that do not appear in the training phase. Another major reason is the change of the distribution between the training and test phases. The proposed method learns the latest classifiers that overcome both problems. With the proposed method, the conditional distribution of new features given existing features is learned using the unlabeled data. In addition, the proposed method estimates the density ratio between training and test distributions by using the labeled and unlabeled data. We approximate the classification error of a classifier, which exploits new features as well as existing features, at the test phase by incorporating both the conditional distribution of new features and the densityratio, simultaneously. By minimizing the approximated error while integrating out new feature values, we obtain a classifier that exploits new features and fits on the test phase. The effectiveness of the proposed method is demonstrated with experiments using synthetic and real-world data sets.
Name Nationality Classification with Recurrent Neural Networks
Authors: Lee Jinhyuk , Kim Hyunjae , Ko Miyoung , Choi Donghee , Choi Jaehoon , Kang Jaewoo
Published: July 2017

Personal names tend to have many variations differing from country to country. Though there exists a large amount of personal names on the Web, nationality prediction solely based on names has not been fully studied due to its difficulties in extracting subtle character level features. We propose a recurrent neural network based model which predicts nationalities of each name using automatic feature extraction. Evaluation of Olympic record data shows that our model achieves greater accuracy than previous feature based approaches in nationality prediction tasks. We also evaluate our proposed model and baseline models on name ethnicity classification task, again achieving better or comparable performances. We further investigate the effectiveness of character embeddings used in our proposed model.
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach
Authors: Li Jundong , Tang Jiliang , Liu Huan
Published: July 2017

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. Since real-world data is usually unlabeled, unsupervised feature selection has received increasing attention in recent years. Without label information, unsupervised feature selection needs alternative criteria to define feature relevance. Recently, data reconstruction error emerged as a new criterion for unsupervised feature selection, which defines feature relevance as the capability of features to approximate original data via a reconstruction function. Most existing algorithms in this family assume predefined, linear reconstruction functions. However, the reconstruction function should be data dependent and may not always be linear especially when the original data is high-dimensional. In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection framework REFS, which embeds the reconstruction function learning process into feature selection. Experiments on various types of real-world datasets demonstrate the effectiveness of the proposed framework REFS.
Locality Adaptive Discriminant Analysis
Authors: Li Xuelong , Chen Mulin , Nie Feiping , Wang Qi
Published: July 2017

Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in the original data space. In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data. Compared to LDA and its variants, the proposed method has three salient advantages: (1) it finds the principle projection directions without imposing any assumption on the data distribution; (2) it’s able to exploit the local manifold structure of data in the desired subspace; (3) it exploits the points’ neighbor relationship automatically without introducing any additional parameter to be tuned. Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method.
Discriminative Deep Hashing for Scalable Face Image Retrieval
Authors: Lin Jie , Li Zechao , Tang Jinhui
Published: July 2017

With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.
Fast SVM Trained by Divide-and-Conquer Anchors
Authors: Liu Meng , Xu Chang , Xu Chao , Tao Dacheng
Published: July 2017

Supporting vector machine (SVM) is the most frequently used classifier for machine learning tasks. However, its training time could become cumbersome when the size of training data is very large. Thus, many kinds of representative subsets are chosen from the original dataset to reduce the training complexity. In this paper, we propose to choose the representative points which are noted as anchors obtained from non-negative matrix factorization (NMF) in a divide-and-conquer framework, and then use the anchors to train an approximate SVM. Our theoretical analysis shows that the solving the DCA-SVM can yield an approximate solution close to the primal SVM. Experimental results on multiple datasets demonstrate that our DCA-SVM is faster than the state-of-the-art algorithms without notably decreasing the accuracy of classification results.
Accelerated Local Anomaly Detection via Resolving Attributed Networks
Authors: Liu Ninghao , Huang Xiao , Hu Xia
Published: July 2017

Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due to the heterogeneous types of information and the scale of real-world data. Meanwhile, it has been observed that local anomalies, which may align with global condition, are hard to be detected by existing algorithms with interpretability. Motivated by the observations, in this paper, we propose to study the problem of effective and efficient local anomaly detection in attributed networks. In particular, we design a collective way for modeling heterogeneous network and attribute information, and develop a novel and efficient distributed optimization algorithm to handle large-scale data. In the experiments, we compare the proposed framework with the state-of-the-art methods on both real and synthetic datasets, and demonstrate its effectiveness and efficiency through quantitative evaluation and case studies.
EigenNet: Towards Fast and Structural Learning of Deep Neural Networks
Authors: Luo Ping
Published: July 2017

Deep Neural Network (DNN) is difficult to train and easy to overfit in training. We address these two issues by introducing EigenNet, an architecture that not only accelerates training but also adjusts number of hidden neurons to reduce over-fitting. They are achieved by whitening the information flows of DNNs and removing those eigenvectors that may capture noises. The former improves conditioning of the Fisher information matrix, whilst the latter increases generalization capability. These appealing properties of EigenNet can benefit many recent DNN structures, such as network in network and inception, by wrapping their hidden layers into the layers of EigenNet. The modeling capacities of the original networks are preserved. Both the training wall-clock time and number of updates are reduced by using EigenNet, compared to stochastic gradient descent on various datasets, including MNIST, CIFAR-10, and CIFAR-100.
Cross-Domain Recommendation: An Embedding and Mapping Approach
Authors: Man Tong , Shen Huawei , Jin Xiaolong , Cheng Xueqi
Published: July 2017

Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing cross-domain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.
Count-Based Exploration in Feature Space for Reinforcement Learning
Authors: Martin Jarryd , Narayanan S. Suraj , Everitt Tom , Hutter Marcus
Published: July 2017

We introduce a new count-based optimistic exploration algorithm for Reinforcement Learning (RL) that is feasible in environments with high-dimensional state-action spaces. The success of RL algorithms in these domains depends crucially on generalisation from limited training experience. Function approximation techniques enable RL agents to generalise in order to estimate the value of unvisited states, but at present few methods enable generalisation regarding uncertainty. This has prevented the combination of scalable RL algorithms with efficient exploration strategies that drive the agent to reduce its uncertainty. We present a new method for computing a generalised state visit-count, which allows the agent to estimate the uncertainty associated with any state. Our \phi-pseudocount achieves generalisation by exploiting same feature representation of the state space that is used for value function approximation. States that have less frequently observed features are deemed more uncertain. The \phi-Exploration-Bonus algorithm rewards the agent for exploring in feature space rather than in the untransformed state space. The method is simpler and less computationally expensive than some previous proposals, and achieves near state-of-the-art results on high-dimensional RL benchmarks.
Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer
Authors: Moon Seungwhan , Carbonell Jaime
Published: July 2017

We study a transfer learning framework where source and target datasets are heterogeneous in both feature and label spaces. Specifically, we do not assume explicit relations between source and target tasks a priori, and thus it is crucial to determine what and what not to transfer from source knowledge. Towards this goal, we define a new heterogeneous transfer learning approach that (1) selects and attends to an optimized subset of source samples to transfer knowledge from, and (2) builds a unified transfer network that learns from both source and target knowledge. This method, termed "Attentional Heterogeneous Transfer", along with a newly proposed unsupervised transfer loss, improve upon the previous state-of-the-art approaches on extensive simulations as well as a challenging hetero-lingual text classification task.
Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection
Authors: Pang Guansong , Cao Longbing , Chen Ling , Liu Huan
Published: July 2017

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors are not independent - they bond together) in constructing a noise-resilient outlier scoring function to produce a reliable outlier ranking in each iteration. We show that HOUR (i) retains a 2-approximation outlier ranking to the optimal one; and (ii) significantly outperforms five state-of-the-art competitors on 15 real-world data sets with different noise levels in terms of AUC and/or P@n. The source code of HOUR is available at https://sites.google.com/site/gspangsite/sourcecode.
Flexible Orthogonal Neighborhood Preserving Embedding
Authors: Pang Tianji , Nie Feiping , Han Junwei
Published: July 2017

In this paper, we propose a novel linear subspace learning algorithm called Flexible Orthogonal Neighborhood Preserving Embedding (FONPE), which is a linear approximation of Locally Linear Embedding (LLE) algorithm. Our novel objective function integrates two terms related to manifold smoothness and a flexible penalty defined on the projection fitness. Different from Neighborhood Preserving Embedding (NPE), we relax the hard constraint by modeling the mismatch between the approximate linear embedding and the original nonlinear embedding instead of enforcing them to be equal, which makes it better cope with the data sampled from a nonlinear manifold. Besides, instead of enforcing an orthogonality between the projected points, we enforce the mapping to be orthogonal. By using this method, FONPE tends to preserve distances and thus the overall geometry can be preserved. Unlike LLE, as FONPE has an explicit linear mapping between the input and the reduced spaces, it can handle novel testing data straightforwardly. Moreover, when the projection matrix in our model becomes an identity matrix, our model can be transformed to denoising LLE (DLLE). Compared with the standard LLE, we demonstrate that DLLE can handle data with noise better. Comprehensive experiments on several benchmark databases demonstrate the effectiveness of our algorithm.
Distributed Accelerated Proximal Coordinate Gradient Methods
Authors: Ren Yong , Zhu Jun
Published: July 2017

We develop a general accelerated proximal coordinate descent algorithm in distributed settings (Dis- APCG) for the optimization problem that minimizes the sum of two convex functions: the first part f is smooth with a gradient oracle, and the other one Ψ is separable with respect to blocks of coordinate and has a simple known structure (e.g., L1 norm). Our algorithm gets new accelerated convergence rate in the case that f is strongly con- vex by making use of modern parallel structures, and includes previous non-strongly case as a special case. We further present efficient implementations to avoid full-dimensional operations in each step, significantly reducing the computation cost. Experiments on the regularized empirical risk minimization problem demonstrate the effectiveness of our algorithm and match our theoretical findings.
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Authors: Ross Andrew Slavin , Hughes Michael C. , Doshi-Velez Finale
Published: July 2017

Expressive classifiers such as neural networks are among the most accurate supervised learning methods in use today, but their opaque decision boundaries make them difficult to trust in critical applications. We propose a method to explain the predictions of any differentiable model via the gradient of the class label with respect to the input (which provides a normal to the decision boundary). Not only is this approach orders of magnitude faster at identifying input dimensions of high sensitivity than sample-based perturbation methods (e.g. LIME), but it also lends itself to efficiently discovering multiple qualitatively different decision boundaries as well as decision boundaries that are consistent with expert annotation. On multiple datasets, we show our approach generalizes much better when test conditions differ from those in training.