Recently Added

  • Deep divergence learning 

    Cilingir, Kubra; Manzelli, Rachel; Kulis, Brian (2020-07-12)
    Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural networks, and Bregman divergence learning approaches ...
  • Joint bilateral learning for real-time universal photorealistic style transfer 

    Xia, Xide; Zhang, Meng; Xue, Tianfan; Sun, Zheng; Fang, Hui; Kulis, Brian; Chen, Jiawen (2020-08-24)
    Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, ...
  • Learning to approximate a Bregman divergence 

    Siahkamari, Ali; Xia, Xide; Saligrama, Venkatesh; Casta nón, David; Kulis, Brian (2020)
    Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning. In this paper, we focus on the problem of approximating an arbitrary ...
  • An audio-based wakeword-independent verification system 

    Wang, Joseph; Kumar, Rajath; Rodehorst, Mike; Kulis, Brian; Vitaladevuni, Shiv (2020-10-25)
    We propose an audio-based wakeword-independent verification model to determine whether a wakeword spotting model correctly woke and should respond or incorrectly woke and should not respond. Our model works on any ...
  • Building a robust word-level wakeword verification network 

    Kumar, Rajath; Rodehorst, Mike; Wang, Joseph; Gu, Jiacheng; Kulis, Brian (2020-10-25)
  • Metadata-aware end-to-end keyword spotting 

    Liu, Hongyi; Abhyankar, Apurva; Mishchenko, Yuriy; Senechal, Thibaud; Fu, Genshen; Kulis, Brian; Stein, Noah; Shah, Anish; Vitaladevuni, Shiv (2020-10-25)
  • Development and validation of a prognostic risk score system for COVID-19 inpatients: a multi-center retrospective study in China 

    Yuan, Ye; Sun, Chuan; Tang, Xiuchuan; Cheng, Cheng; Mombaerts, Laurent; Wang, Maolin; Hu, Tao; Sun, Chenyu; Guo, Yuqi; Li, Xiuting; Xu, Hui; Ren, Tongxin; Xiao, Yang; Xiao, Yaru; Zhu, Hongling; Wu, Honghan; Li, Kezhi; Chen, Chuming; Liu, Yingxia; Liang, Zhichao; Cao, Zhiguo; Zhang, Hai-Tao; Ch Paschaldis, Ioannis; Liu, Quanying; Goncalves, Jorge; Zhong, Qiang; Yan, Li (2020-11-28)
    Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians ...
  • Congestion-aware routing and rebalancing of autonomous mobility-on-demand systems in mixed traffic 

    Wollenstein-Betech, Salomon; Houshmand, Arian; Salazar, Mauro; Pavone, Marco; Cassandras, Christos G.; Paschalidis, Ioannis Ch. (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    This paper studies congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on demand mobility under mixed traffic conditions. Specifically, ...
  • Online learning with imperfect hints 

    Cutkosky, Ashok; Bhaskara, Aditya; Purohit, Manish; Kumar, Ravi (2020-07-13)
    We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown ...
  • Parameter-free, dynamic, and strongly-adaptive online learning 

    Cutkosky, Ashok (2020-07-13)
    We provide a new online learning algorithm that for the first time combines several disparate notions of adaptivity. First, our algorithm obtains a “parameter-free” regret bound that adapts to the norm of the comparator ...

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