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1 - 7 of 7 search results for TALK:PC53 20 |u:mi.eng.cam.ac.uk where 0 match all words and 7 match some words.
  1. Results that match 1 of 2 words

  2. Mark Gales - Research Team

    mi.eng.cam.ac.uk/~mjfg/team.html
    17 May 2024: Undergraduate Students (2019-20) [Technical Advice, Sabbtical Leave]:. Adian Liusie: Spoken "Grammatical" Error Detaction and Correction.
  3. Stradwin files

    mi.eng.cam.ac.uk/~gmt11/stradview/stradwin_files.htm
    24 May 2024: data. RES_THICKNESS_SCALE_HU. long. 20. THICKNESS DISPLAY. The scale value range (in 100 Hounsfield Units) for the surface colour map when displaying cortical density estimation from CT DICOM data.
  4. Paper8-CV-intro.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/2024-IB-Paper8-CV-Introduction.pdf
    1 May 2024: 20 Engineering Part IB: Paper 8 Information Engineering. Syllabus. 1. Introduction. •
  5. 29 Apr 2024: 20] From rewriting to remembering: Common ground for conversational QA models.
  6. 2024-IB-Paper8-CV.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/2024-IB-Paper8-CV-Features-Matching.pdf
    1 May 2024: Sig. nal. Sigma = 20. As σ increases, the signal is smoothed more and more, and. ... 20 Engineering Part IB: Paper 8 Feature extraction and matching. Correlation.
  7. Prediction of Total and Regional Body Composition from 3D ...

    mi.eng.cam.ac.uk/~cipolla/publications/article/2024-4-Body-composition.pdf
    23 Apr 2024: More recently, Leong et al. [24] use a variational autoencoder(VAE) [20] to learn latent DXA encoding, and map 3DO scans to pseudo-DXA images. ... Dropout [39] is adopted to regularisethe network. We train our model using an 80%–20% train–validation
  8. Prediction of Total and Regional Body Composition from 3D ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2024-4-Body-composition.pdf
    23 Apr 2024: More recently, Leong et al. [24] use a variational autoencoder(VAE) [20] to learn latent DXA encoding, and map 3DO scans to pseudo-DXA images. ... Dropout [39] is adopted to regularisethe network. We train our model using an 80%–20% train–validation

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