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  2. Surprise Languages: Rapid-Response Cross-Language IR

    mi.eng.cam.ac.uk/~ar527/oard_evia2019.pdf
    11 Jun 2019: The basic idea wasthat research teams would simply try out what they were alreadyworking on using some new language. ... In reality, however, the release of any new languagecan be treated as a surprise language.
  3. Applying Deep Learning in Non-native Spoken English Assessment

    mi.eng.cam.ac.uk/~kmk/presentations/APSIPA2019_Knill_Keynote.pdf
    21 Nov 2019: maps features x i and x j to new space• parameters of mapping network the same θ. •
  4. BI-DIRECTIONAL LATTICE RECURRENT NEURAL NETWORKSFOR CONFIDENCE…

    mi.eng.cam.ac.uk/~ar527/ragni_icassp2019.pdf
    5 Feb 2019: 22] J. Ma and S. Matsoukas, “Unsupervised training on a largeamount of Arabic broadcast news data,” in ICASSP, 2007.
  5. BI-DIRECTIONAL LATTICE RECURRENT NEURAL NETWORKSFOR CONFIDENCE…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ICASSP2019_li.pdf
    31 Aug 2019: 22] J. Ma and S. Matsoukas, “Unsupervised training on a largeamount of Arabic broadcast news data,” in ICASSP, 2007.
  6. CONFIDENCE ESTIMATION AND DELETION PREDICTION USINGBIDIRECTIONAL…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/SLT2018_ragni.pdf
    31 Aug 2019: 19] J. Ma, S. Matsoukas, O. Kimball, and R. Schwartz, “Unsu-pervised training on large amounts of broadcast news data,” inICASSP, 2006. ... 28] J. Ma and S. Matsoukas, “Unsupervised training on a largeamount of Arabic broadcast news data,” in
  7. IMPROVED AUTO-MARKING CONFIDENCE FOR SPOKEN LANGUAGE ASSESSMENT M.…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/vecchio_slt18.pdf
    25 Feb 2019: Recently,[11] developed a new theoretical framework, Monte CarloDrop-Out (MCD), casting drop-out training in DNNs as ap-proximate Bayesian inference in Deep Gaussian Processes.In so doing, they were ... 18] Cédric Villani, Optimal transport: old and new
  8. LEARNING BETWEEN DIFFERENT TEACHER AND STUDENT MODELS IN ASR ...

    mi.eng.cam.ac.uk/~mjfg/ALTA/ASRU2019_TS.pdf
    20 Dec 2019: 24] C. Zhang and P. C. Woodlang, “Joint optimisation of tan-dem systems using Gaussian mixture density neural networkdiscriminative sequence training,” in ICASSP, New Orleans,USA, Mar 2017, pp.
  9. To appear Proc. ICASSP. c©2019 IEEE. Personal use of ...

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/Knill_ICASSP2019_AcceptedPaper.pdf
    3 Mar 2019: material for advertising or promotional purposes, creating new collective works, for resale orredistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ... Medlock, “A new datasetand method for automatically
  10. SEQUENCE TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/wang_slt18.pdf
    25 Feb 2019: M. Fisher. Design and prepa-ration of the 1996 Hub-4 broadcast news benchmark test cor-pora.
  11. Creatures great and SMAL: Recovering theshape and motion of ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2018-ACCV-3D-animal-shape.pdf
    12 Aug 2019: Joint accuracyresults at multiple stages of the pipeline are reported on the new BADJAdataset, which contains ground truths for real animal subjects. ... 2018). 3. Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation:New
  12. Orientation-Aware Semantic Segmentation on Icosahedron Spheres Chao…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2019-ICCV-Segmentation-Icosahedron.pdf
    12 Aug 2019: In our evaluation our orientation-aware CNN be-comes a new state of the art for the recent 2D3DS dataset,and our Omni-SYNTHIA version of SYNTHIA. ... ages (from New York-like and Highway sequences) and use451 images of the European-like sequence for
  13. A Differential Volumetric Approach to Multi-View Photometric Stereo…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2019-ICCV-Differential-MVPS.pdf
    12 Aug 2019: 1. process easier by using LEDs in a calibrated setting.In this work, our aim is to propose a new MVPS ap-. ... Applied Mathematical Sciences.Springer New York, 2006. 1. [35] Jaesik Park, Sudipta N.
  14. Creatures great and SMAL: Recovering theshape and motion of ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2018-ACCV-3D-animal-shape.pdf
    12 Aug 2019: Joint accuracyresults at multiple stages of the pipeline are reported on the new BADJAdataset, which contains ground truths for real animal subjects. ... 2018). 3. Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation:New
  15. Orientation-Aware Semantic Segmentation on Icosahedron Spheres Chao…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2019-ICCV-Segmentation-Icosahedron.pdf
    12 Aug 2019: In our evaluation our orientation-aware CNN be-comes a new state of the art for the recent 2D3DS dataset,and our Omni-SYNTHIA version of SYNTHIA. ... ages (from New York-like and Highway sequences) and use451 images of the European-like sequence for
  16. A Differential Volumetric Approach to Multi-View Photometric Stereo…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2019-ICCV-Differential-MVPS.pdf
    12 Aug 2019: 1. process easier by using LEDs in a calibrated setting.In this work, our aim is to propose a new MVPS ap-. ... Applied Mathematical Sciences.Springer New York, 2006. 1. [35] Jaesik Park, Sudipta N.
  17. SegNet: A Deep Convolutional Encoder-DecoderArchitecture for Image…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2017-PAMI-SegNet.pdf
    11 Sep 2019: SegNet: A Deep Convolutional Encoder-DecoderArchitecture for Image SegmentationVijay Badrinarayanan, Alex Kendall , and Roberto Cipolla, Senior Member, IEEE. Abstract—We present a novel and practical deep fully convolutional neural network
  18. SegNet: A Deep Convolutional Encoder-DecoderArchitecture for Image…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2017-PAMI-SegNet.pdf
    11 Sep 2019: SegNet: A Deep Convolutional Encoder-DecoderArchitecture for Image SegmentationVijay Badrinarayanan, Alex Kendall , and Roberto Cipolla, Senior Member, IEEE. Abstract—We present a novel and practical deep fully convolutional neural network

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