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  • Model Elicitation through Direct Questioning
    arXiv.cs.AI Pub Date : 2020-11-24
    Sachin Grover; David Smith; Subbarao Kambhampati

    The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about getting useful information about the human's (teammate's) model. There are many challenges before a robot can interact, such as incorporating the structural differences in the human's model, ensuring simpler responses, etc

    更新日期:2020-11-25
  • Fuzzy Stochastic Timed Petri Nets for Causal properties representation
    arXiv.cs.AI Pub Date : 2020-11-24
    Alejandro Sobrino; Eduardo C. Garrido-Merchan; Cristina Puente

    Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between objects. Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. A causal graph is a skeletal picture, showing causal associations and impact between entities

    更新日期:2020-11-25
  • DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network
    arXiv.cs.AI Pub Date : 2020-11-24
    Junyou He; Guibao Mei; Feng Xing; Xiaorui Yang; Yongjun Bao; Weipeng Yan

    Click through rate(CTR) prediction is a core task in advertising systems. The booming e-commerce business in our company, results in a growing number of scenes. Most of them are so-called long-tail scenes, which means that the traffic of a single scene is limited, but the overall traffic is considerable. Typical studies mainly focus on serving a single scene with a well designed model. However, this

    更新日期:2020-11-25
  • Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots
    arXiv.cs.AI Pub Date : 2020-11-23
    Ruikang Zhong; Xiao Liu; Yuanwei Liu; Yue Chen; Xianbin Wang

    A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor

    更新日期:2020-11-25
  • Energy-Based Models for Continual Learning
    arXiv.cs.AI Pub Date : 2020-11-24
    Shuang Li; Yilun Du; Gido M. van de Ven; Antonio Torralba; Igor Mordatch

    We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs have a natural way to support a dynamically-growing number of tasks or classes that causes less interference with previously learned information. We find that EBMs outperform the baseline methods

    更新日期:2020-11-25
  • Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention
    arXiv.cs.AI Pub Date : 2020-11-23
    Varnith Chordia; Vijay Kumar BG

    Accurate and efficient product classification is significant for E-commerce applications, as it enables various downstream tasks such as recommendation, retrieval, and pricing. Items often contain textual and visual information, and utilizing both modalities usually outperforms classification utilizing either mode alone. In this paper we describe our methodology and results for the SIGIR eCom Rakuten

    更新日期:2020-11-25
  • Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
    arXiv.cs.AI Pub Date : 2020-11-24
    Aneesh Pappu; Brooks Paige

    Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is a classically low-data regime and it isn't clear that graph neural networks can avoid overfitting in low-resource settings. In contrast, fingerprint methods are the traditional standard for low-data environments due to their

    更新日期:2020-11-25
  • xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs
    arXiv.cs.AI Pub Date : 2020-11-24
    Susie Xi Rao; Shuai Zhang; Zhichao Han; Zitao Zhang; Wei Min; Zhiyao Chen; Yinan Shan; Yang Zhao; Ce Zhang

    At online retail platforms, it is crucial to actively detect risks of fraudulent transactions to improve our customer experience, minimize loss, and prevent unauthorized chargebacks. Traditional rule-based methods and simple feature-based models are either inefficient or brittle and uninterpretable. The graph structure that exists among the heterogeneous typed entities of the transaction logs is informative

    更新日期:2020-11-25
  • VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval
    arXiv.cs.AI Pub Date : 2020-11-24
    Sijie Zhu; Taojiannan Yang; Chen Chen

    Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. However, these results rely on the assumption that there exists a reference image exactly centered at the location of any query image, which is not applicable

    更新日期:2020-11-25
  • SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
    arXiv.cs.AI Pub Date : 2020-11-24
    Sheng Ao; Qingyong Hu; Bo Yang; Andrew Markham; Yulan Guo

    Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet

    更新日期:2020-11-25
  • Deep learning for video game genre classification
    arXiv.cs.AI Pub Date : 2020-11-21
    Yuhang Jiang; Lukun Zheng

    Video game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many of which are not concretely defined. Second, video game covers vary in many different

    更新日期:2020-11-25
  • SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries
    arXiv.cs.AI Pub Date : 2020-11-24
    Xirong Li; Fangming Zhou; Chaoxi Xu; Jiaqi Ji; Gang Yang

    Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-modal representation learning that encodes both query sentences and videos into common spaces for semantic similarity computation. Inspired by the initial success of previously few works in combining multiple sentence encoders

    更新日期:2020-11-25
  • Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning
    arXiv.cs.AI Pub Date : 2020-11-24
    Suk Joon Hong; Brandon Bennett

    The Winograd Schema Challenge (WSC) is a common-sense reasoning task that requires background knowledge. In this paper, we contribute to tackling WSC in four ways. Firstly, we suggest a keyword method to define a restricted domain where distinctive high-level semantic patterns can be found. A thanking domain was defined by key-words, and the data set in this domain is used in our experiments. Secondly

    更新日期:2020-11-25
  • CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
    arXiv.cs.AI Pub Date : 2020-11-24
    Muhammad Zaigham Zaheer; Arif Mahmood; Marcella Astrid; Seung-Ik Lee

    Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize

    更新日期:2020-11-25
  • Efficient Sampling for Predictor-Based Neural Architecture Search
    arXiv.cs.AI Pub Date : 2020-11-24
    Lukas Mauch; Stephen Tiedemann; Javier Alonso Garcia; Bac Nguyen Cong; Kazuki Yoshiyama; Fabien Cardinaux; Thomas Kemp

    Recently, predictor-based algorithms emerged as a promising approach for neural architecture search (NAS). For NAS, we typically have to calculate the validation accuracy of a large number of Deep Neural Networks (DNNs), what is computationally complex. Predictor-based NAS algorithms address this problem. They train a proxy model that can infer the validation accuracy of DNNs directly from their network

    更新日期:2020-11-25
  • Adversarial Generation of Continuous Images
    arXiv.cs.AI Pub Date : 2020-11-24
    Ivan Skorokhodov; Savva Ignatyev; Mohamed Elhoseiny

    In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) -- an MLP that predicts an RGB pixel value given its (x,y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation

    更新日期:2020-11-25
  • RIN: Textured Human Model Recovery and Imitation with a Single Image
    arXiv.cs.AI Pub Date : 2020-11-24
    Haoxi Ran; Guangfu Wang; Li Lu

    Human imitation has become topical recently, driven by GAN's ability to disentangle human pose and body content. However, the latest methods hardly focus on 3D information, and to avoid self-occlusion, a massive amount of input images are needed. In this paper, we propose RIN, a novel volume-based framework for reconstructing a textured 3D model from a single picture and imitating a subject with the

    更新日期:2020-11-25
  • Code Search Intent Classification Using Weak Supervision
    arXiv.cs.AI Pub Date : 2020-11-24
    Nikitha Rao; Chetan Bansal; Joe Guan

    Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding process. Recently, natural language based code search has been an active area of research. However, the lack of real-world large-scale datasets is a significant bottleneck. In this work

    更新日期:2020-11-25
  • Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
    arXiv.cs.AI Pub Date : 2020-11-24
    Xiupeng Shi; Yiik Diew Wong; Chen Chai; Michael Zhi-Feng Li; Tianyi Chen; Zeng Zeng

    Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised

    更新日期:2020-11-25
  • On the Adversarial Robustness of 3D Point Cloud Classification
    arXiv.cs.AI Pub Date : 2020-11-24
    Jiachen Sun; Karl Koenig; Yulong Cao; Qi Alfred Chen; Z. Morley Mao

    3D point clouds play pivotal roles in various safety-critical fields, such as autonomous driving, which desires the corresponding deep neural networks to be robust to adversarial perturbations. Though a few defenses against adversarial point cloud classification have been proposed, it remains unknown whether they can provide real robustness. To this end, we perform the first security analysis of state-of-the-art

    更新日期:2020-11-25
  • Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
    arXiv.cs.AI Pub Date : 2020-11-24
    Ahmadreza Ahmadi; Tønnes Nygaard; Navinda Kottege; David Howard; Nicolas Hudson

    Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling technology for autonomous legged robots, as it allows the robot to harness their innate flexibility to adapt their behaviour to the demands of their operating environment. In this paper, we show how highly capable machine

    更新日期:2020-11-25
  • Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
    arXiv.cs.AI Pub Date : 2020-11-24
    Hyemi Kim; Seungjae Shin; JoonHo Jang; Kyungwoo Song; Weonyoung Joo; Wanmo Kang; Il-Chul Moon

    The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation

    更新日期:2020-11-25
  • Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels
    arXiv.cs.AI Pub Date : 2020-11-24
    Gurcan Comert

    Dynamic behavior of traffic adversely affect the performance of the prediction models in intelligent transportation applications. This study applies Gaussian processes (GPs) to traffic speed prediction. Such predictions can be used by various transportation applications, such as real-time route guidance, ramp metering, congestion pricing and special events traffic management. One-step predictions with

    更新日期:2020-11-25
  • GMOT-40: A Benchmark for Generic Multiple Object Tracking
    arXiv.cs.AI Pub Date : 2020-11-24
    Hexin Bai; Wensheng Cheng; Peng Chu; Juehuan Liu; Kai Zhang; Haibin Ling

    Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target, and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to

    更新日期:2020-11-25
  • Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation
    arXiv.cs.AI Pub Date : 2020-11-24
    Woohwan Jung; Kyuseok Shim

    Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data. To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision

    更新日期:2020-11-25
  • Benchmarking Inference Performance of Deep Learning Models on Analog Devices
    arXiv.cs.AI Pub Date : 2020-11-24
    Omobayode Fagbohungbe; Lijun Qian

    Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the device and the associated many noise sources will cause changes to the value of the weights in the trained deep learning models deployed on such devices. In this study, systematic evaluation of the inference performance of trained

    更新日期:2020-11-25
  • Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
    arXiv.cs.AI Pub Date : 2020-11-24
    Loris Nanni; Alessandra Lumini; Stefano Ghidoni; Gianluca Maguolo

    Classification of biological images is an important task with crucial application in many fields, such as cell phenotypes recognition, detection of cell organelles and histopathological classification, and it might help in early medical diagnosis, allowing automatic disease classification without the need of a human expert. In this paper we classify biomedical images using ensembles of neural networks

    更新日期:2020-11-25
  • REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning
    arXiv.cs.AI Pub Date : 2020-11-24
    Yunzhe Tao; Sahika Genc; Tao Sun; Sunil Mallya

    Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown. In this work, we propose a REPresentation-And-INstance Transfer algorithm (REPAINT) for deep actor-critic reinforcement learning paradigm. In representation

    更新日期:2020-11-25
  • When Machine Learning Meets Privacy: A Survey and Outlook
    arXiv.cs.AI Pub Date : 2020-11-24
    Bo Liu; Ming Ding; Sina Shaham; Wenny Rahayu; Farhad Farokhi; Zihuai Lin

    The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context

    更新日期:2020-11-25
  • The Interpretable Dictionary in Sparse Coding
    arXiv.cs.AI Pub Date : 2020-11-24
    Edward Kim; Connor Onweller; Andrew O'Brien; Kathleen McCoy

    Artificial neural networks (ANNs), specifically deep learning networks, have often been labeled as black boxes due to the fact that the internal representation of the data is not easily interpretable. In our work, we illustrate that an ANN, trained using sparse coding under specific sparsity constraints, yields a more interpretable model than the standard deep learning model. The dictionary learned

    更新日期:2020-11-25
  • An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
    arXiv.cs.AI Pub Date : 2020-11-23
    Carlos H. C. Pena; Mateus G. Machado; Mariana S. Barros; José D. P. Silva; Lucas D. Maciel; Tsang Ing Ren; Edna N. S. Barros; Pedro H. M. Braga; Hansenclever F. Bassani

    The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching

    更新日期:2020-11-25
  • End-to-End Framework for Efficient Deep Learning Using Metasurfaces Optics
    arXiv.cs.AI Pub Date : 2020-11-23
    Carlos Mauricio Villegas Burgos; Tianqi Yang; Nick Vamivakas; Yuhao Zhu

    Deep learning using Convolutional Neural Networks (CNNs) has been shown to significantly out-performed many conventional vision algorithms. Despite efforts to increase the CNN efficiency both algorithmically and with specialized hardware, deep learning remains difficult to deploy in resource-constrained environments. In this paper, we propose an end-to-end framework to explore optically compute the

    更新日期:2020-11-25
  • From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion
    arXiv.cs.AI Pub Date : 2020-11-23
    Deepali Jain; Atil Iscen; Ken Caluwaerts

    Legged robots navigating crowded scenes and complex terrains in the real world are required to execute dynamic leg movements while processing visual input for obstacle avoidance and path planning. We show that a quadruped robot can acquire both of these skills by means of hierarchical reinforcement learning (HRL). By virtue of their hierarchical structure, our policies learn to implicitly break down

    更新日期:2020-11-25
  • Multi-task Language Modeling for Improving Speech Recognition of Rare Words
    arXiv.cs.AI Pub Date : 2020-11-23
    Chao-Han Huck Yang; Linda Liu; Ankur Gandhe; Yile Gu; Anirudh Raju; Denis Filimonov; Ivan Bulyko

    End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the average accuracy of these systems may be high, the performance on rare content words often lags behind hybrid ASR systems. To address this problem, second-pass rescoring is often applied. In this paper, we propose a second-pass

    更新日期:2020-11-25
  • A Use of Even Activation Functions in Neural Networks
    arXiv.cs.AI Pub Date : 2020-11-23
    Fuchang Gao; Boyu Zhang

    Despite broad interest in applying deep learning techniques to scientific discovery, learning interpretable formulas that accurately describe scientific data is very challenging because of the vast landscape of possible functions and the "black box" nature of deep neural networks. The key to success is to effectively integrate existing knowledge or hypotheses about the underlying structure of the data

    更新日期:2020-11-25
  • Nudge Attacks on Point-Cloud DNNs
    arXiv.cs.AI Pub Date : 2020-11-22
    Yiren Zhao; Ilia Shumailov; Robert Mullins; Ross Anderson

    The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat. Existing adversarial attacks on point clouds achieve high success rates but modify a large number of points, which is usually difficult to do in real-life scenarios. In this paper, we explore a family of attacks that only perturb a few points of an input point

    更新日期:2020-11-25
  • Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos
    arXiv.cs.AI Pub Date : 2020-11-23
    Vitor Fortes Rey; Kamalveer Kaur Garewal; Paul Lukowicz

    Human activity recognition (HAR) using wearable sensors has benefited much less from recent advances in Machine Learning than fields such as computer vision and natural language processing. This is to a large extent due to the lack of large scale repositories of labeled training data. In our research we aim to facilitate the use of online videos, which exists in ample quantity for most activities and

    更新日期:2020-11-25
  • APAN: Asynchronous Propagate Attention Network for Real-time Temporal Graph Embedding
    arXiv.cs.AI Pub Date : 2020-11-23
    Xuhong Wang; Ding Lyu; Mengjian Li; Yang Xia; Qi Yang; Xinwen Wang; Xinguang Wang; Ping Cui; Yupu Yang; Bowen Sun; Zhenyu Guo

    Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagate Attention Network, an asynchronous continuous time dynamic

    更新日期:2020-11-25
  • Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
    arXiv.cs.AI Pub Date : 2020-11-23
    Tyler Malloy; Tim Klinger; Miao Liu; Matthew Riemer; Gerald Tesauro; Chris R. Sims

    This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm. Previous research with a related approach in continuous control experiments suggests that this method favors learning policies that are more robust to changing environment dynamics. The multi-agent game setting naturally

    更新日期:2020-11-25
  • Language guided machine action
    arXiv.cs.AI Pub Date : 2020-11-23
    Feng Qi

    Here we build a hierarchical modular network called Language guided machine action (LGMA), whose modules process information stream mimicking human cortical network that allows to achieve multiple general tasks such as language guided action, intention decomposition and mental simulation before action execution etc. LGMA contains 3 main systems: (1) primary sensory system that multimodal sensory information

    更新日期:2020-11-25
  • Semantic CPPS in Industry 4.0
    arXiv.cs.AI Pub Date : 2020-11-18
    Giuseppe Fenza; Mariacristina Gallo; Vincenzo Loia; Domenico Marinoand Francesco Orciuoli; Alberto Volpe

    Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of their intrinsic requirement is the capability to interface

    更新日期:2020-11-25
  • Synthesis and Pruning as a Dynamic Compression Strategy for Efficient Deep Neural Networks
    arXiv.cs.AI Pub Date : 2020-11-23
    Alastair Finlinson; Sotiris Moschoyiannis

    The brain is a highly reconfigurable machine capable of task-specific adaptations. The brain continually rewires itself for a more optimal configuration to solve problems. We propose a novel strategic synthesis algorithm for feedforward networks that draws directly from the brain's behaviours when learning. The proposed approach analyses the network and ranks weights based on their magnitude. Unlike

    更新日期:2020-11-25
  • Elementary Effects Analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of Social Distancing and Mask Usage
    arXiv.cs.AI Pub Date : 2020-11-20
    Kelvin K. F. Li; Stephen A. Jarvis; Fayyaz Minhas

    COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for effective policy making. This paper aims to investigate the effectiveness

    更新日期:2020-11-25
  • FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network
    arXiv.cs.AI Pub Date : 2020-11-23
    Dianbo Liu; He Zhu

    The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable private and sensitive data. Huge amount of private data are being generated from personal devices such as smart phone and wearable in recent years. Being able to utilize these personal data will bring big opportunities to

    更新日期:2020-11-25
  • DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
    arXiv.cs.AI Pub Date : 2020-11-22
    Muhammad Abdul-Mageed; Shady Elbassuoni; Jad Doughman; AbdelRahim Elmadany; El Moatez Billah Nagoudi; Yorgo Zoughby; Ahmad Shaher Iskander Gaba; Ahmed Helal; Mohammed El-Razzaz

    Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic

    更新日期:2020-11-25
  • A Bayesian Account of Measures of Interpretability in Human-AI Interaction
    arXiv.cs.AI Pub Date : 2020-11-22
    Sarath Sreedharan; Anagha Kulkarni; Tathagata Chakraborti; David E. Smith; Subbarao Kambhampati

    Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and deployment of human-aware agents in the real world, notions of interpretability are just some among many considerations; and the techniques developed in isolation lack two key properties to be useful when considered together:

    更新日期:2020-11-25
  • Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems
    arXiv.cs.AI Pub Date : 2020-11-22
    Hyungjun Park; Daiki Min; Jong-hyun Ryu; Dong Gu Choi

    Typical reinforcement learning (RL) methods show limited applicability for real-world industrial control problems because industrial systems involve various constraints and simultaneously require continuous and discrete control. To overcome these challenges, we devise a novel RL algorithm that enables an agent to handle a highly constrained action space. This algorithm has two main features. First

    更新日期:2020-11-25
  • Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
    arXiv.cs.AI Pub Date : 2020-11-21
    Tianrong Chen; Ziyi Wang; Ioannis Exarchos; Evangelos A. Theodorou

    In this paper, we present a deep learning framework for solving large-scale multi-agent non-cooperative stochastic games using fictitious play. The Hamilton-Jacobi-Bellman (HJB) PDE associated with each agent is reformulated into a set of Forward-Backward Stochastic Differential Equations (FBSDEs) and solved via forward sampling on a suitably defined neural network architecture. Decision-making in

    更新日期:2020-11-25
  • BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures
    arXiv.cs.AI Pub Date : 2020-11-21
    Tianchen Zhao; Xuefei Ning; Songyi Yang; Shuang Liang; Peng Lei; Jianfei Chen; Huazhong Yang; Yu Wang

    Binary Neural Networks (BNNs) have received significant attention due to their promising efficiency. Currently, most BNN studies directly adopt widely-used CNN architectures, which can be suboptimal for BNNs. This paper proposes a novel Binary ARchitecture Search (BARS) flow to discover superior binary architecture in a large design space. Specifically, we design a two-level (Macro \& Micro) search

    更新日期:2020-11-25
  • Spatially Correlated Patterns in Adversarial Images
    arXiv.cs.AI Pub Date : 2020-11-21
    Nandish Chattopadhyay; Lionell Yip En Zhi; Bryan Tan Bing Xing; Anupam Chattopadhyay

    Adversarial attacks have proved to be the major impediment in the progress on research towards reliable machine learning solutions. Carefully crafted perturbations, imperceptible to human vision, can be added to images to force misclassification by an otherwise high performing neural network. To have a better understanding of the key contributors of such structured attacks, we searched for and studied

    更新日期:2020-11-25
  • Explainable Composition of Aggregated Assistants
    arXiv.cs.AI Pub Date : 2020-11-21
    Sarath Sreedharan; Tathagata Chakraborti; Yara Rizk; Yasaman Khazaeni

    A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks. In this paper, we will talk about the role of planning in the automated composition of such assistants and explore how concepts in automated planning can help to establish transparency

    更新日期:2020-11-25
  • AI Governance for Businesses
    arXiv.cs.AI Pub Date : 2020-11-20
    Johannes Schneider; Rene Abraham; Christian Meske

    Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted

    更新日期:2020-11-25
  • A General Framework for Distributed Inference with Uncertain Models
    arXiv.cs.AI Pub Date : 2020-11-20
    James Z. Hare; Cesar A. Uribe; Lance Kaplan; Ali Jadbabaie

    This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations

    更新日期:2020-11-25
  • Assessment and Linear Programming under Fuzzy Conditions
    arXiv.cs.AI Pub Date : 2020-11-20
    Michael Voskoglou

    A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers for evaluating a group's mean performance, when qualitative grades instead of numerical scores are used for assessing its members' individual performance. Also, a new technique is developed for solving Linear Programming problems with fuzzy coefficients and everyday life applications are presented to illustrate our results.

    更新日期:2020-11-25
  • Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components
    arXiv.cs.AI Pub Date : 2020-11-23
    Joseph A. Vincent; Mac Schwager

    We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterated over multiple time steps, as would be found

    更新日期:2020-11-25
  • Interpretable Visual Reasoning via Induced Symbolic Space
    arXiv.cs.AI Pub Date : 2020-11-23
    Zhonghao Wang; Mo Yu; Kai Wang; Jinjun Xiong; Wen-mei Hwu; Mark Hasegawa-Johnson; Humphrey Shi

    We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; and achieve an interpretable model via working on the induced symbolic concept space. To this end, we first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task

    更新日期:2020-11-25
  • Manifold Partition Discriminant Analysis
    arXiv.cs.AI Pub Date : 2020-11-23
    Yang Zhou; Shiliang Sun

    We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold into

    更新日期:2020-11-25
  • A Theory on AI Uncertainty Based on Rademacher Complexity and Shannon Entropy
    arXiv.cs.AI Pub Date : 2020-11-19
    Mingyong Zhou

    In this paper, we present a theoretical discussion on AI deep learning neural network uncertainty investigation based on the classical Rademacher complexity and Shannon entropy. First it is shown that the classical Rademacher complexity and Shannon entropy is closely related by quantity by definitions. Secondly based on the Shannon mathematical theory on communication [3], we derive a criteria to ensure

    更新日期:2020-11-25
  • The Dynamic of Body and Brain Co-Evolution
    arXiv.cs.AI Pub Date : 2020-11-23
    Paolo Pagliuca; Stefano Nolfi

    We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies. Interestingly, the advantage

    更新日期:2020-11-25
  • OAK: Ontology-Based Knowledge Map Model for Digital Agriculture
    arXiv.cs.AI Pub Date : 2020-11-20
    Quoc Hung Ngo; Tahar Kechadi; Nhien-An Le-Khac

    Nowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining

    更新日期:2020-11-25
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