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41 - 50 of 83 search results for KaKaoTalk:vb20 200 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 83 match some words.

  1. Results that match 1 of 2 words

  2. Beyond independent masking in tabular self-supervision

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/beyond_independent_masking_in_tabular_self-supervision.pdf
    25 Nov 2022: This lead to a training time of 200 epochs. We set the corruptionparameter p = 0.3, as opposed to the default of value of 0.5 used elsewhere, due to the

  3. Optimal PAC-Bayes Bounds and their Variational Approximations

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/optimal_pac-bayes_bounds.pdf
    24 Nov 2023: Optimal PAC-Bayes Bounds and theirVariational Approximations. Szilvia Ujváry. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence.

  4. Multimodal Emotion Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/multimodal_emotion_recognition.pdf
    11 Feb 2021: During training,the newbob learning rate scheduler with an initial learning rate of 5 105 was used, andbatch size was set to 200.

  5. MPhil in Machine Learning and Machine IntelligenceCambridge…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/continuity_of_autoencoders.pdf
    21 Jan 2022: MPhil in Machine Learning and Machine IntelligenceCambridge University Department of Engineering. Continuity of autoencoders, unsupervised anomalydetection and Deep Atlases. Dissertation submitted by. Charles A. ArnalHomerton College. August 2021.

  6. A Policy Agnostic Framework for Post Hoc Analysis of Organ Allocation …

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/framework_for_analysis_of_organ_allocation_policies.pdf
    15 Nov 2021: A Policy Agnostic Framework for PostHoc Analysis of Organ Allocation. Policies. Agathe de Vulpian. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. in Machine Learning and

  7. One-shot Learning in DiscriminativeNeural Networks Jordan Burgess…

    https://www.mlmi.eng.cam.ac.uk/files/jordan_burgess_8224871_assignsubmission_file_burgess_jordan_thesis1.pdf
    30 Oct 2019: One-shot Learning in DiscriminativeNeural Networks. Jordan Burgess. Queens’ College. A dissertation submitted to the University of Cambridgein partial fulfilment of the requirements for the degree ofMaster of Philosophy in Machine Learning, Speech

  8. Probabilistic Programming in JuliaNew Inference Algorithms Kai Xu…

    https://www.mlmi.eng.cam.ac.uk/files/kai_xu_8224821_assignsubmission_file_xu_kai_dissertation.pdf
    30 Oct 2019: Probabilistic Programming in JuliaNew Inference Algorithms. Kai Xu. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Homerton College August 2016. Declaration. I Kai Xu of

  9. Improved Ergodic Inference via Kernelised Stein Discrepancy

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/improved_ergodic_inference_via_kernelised_stein_discrepancy.pdf
    11 Feb 2021: Improved Ergodic Inference via. Kernelised Stein Discrepancy. Wenlong Chen. Darwin College. This dissertation is submitted on August 19, 2020 for the degree of Master of. Philosophy in Machine Learning and Machine Intelligence. Declaration. I,

  10. Establishing a Unified Framework for Iterative Machine Teaching

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/framework_for_iterative_machine_teaching.pdf
    17 Nov 2023: Establishing a Unified Framework forIterative Machine Teaching. Muqing Xue. Department of Engineering. University of Cambridge. This thesis is submitted for the degree of. Master of Philosophy in Machine Learning and Machine Intelligence. Downing

  11. Compression without Quantization Gergely Flamich Department of…

    https://www.mlmi.eng.cam.ac.uk/files/compression_without_quantization_flamich_reduced.pdf
    18 Nov 2019: However, if we used different sized images during training, it would be less justi-fied to set the same average code budget for, say, a 200 300 pixel image and a

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