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1 - 10 of 16 search results for KaKaoTalk:vb20 200 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 16 match some words.

  1. Results that match 1 of 2 words

  2. The University of Cambridge, Advanced Machine Learning Conditional…

    https://www.mlmi.eng.cam.ac.uk/files/conditional_neural_processes.pdf
    1 Feb 2021: Figure 3: Left: We provide the model with 1, 40, 200 and 784 context points(top row) and query the entire image.

  3. Depth Uncertainty Networks for Active Learning

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/depth_uncertainty_networks_for_active_learning_reduced.pdf
    15 Nov 2021: Depth Uncertainty Networks forActive Learning. Chelsea Murray. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Corpus Christi

  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. 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

  6. 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,

  7. Precise Positioning of a Drone using Spoken Language Commands ...

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/precise_positioning_of_a_drone_using_spoken_language_commands.pdf
    11 Feb 2021: meaningful distributions using just 200 training examples. We evaluate our model in the sim-. ... Errors in the transcriptions are manually corrected for. 200 position-description pairs are used for training, while 50 are reserved for validation.

  8. Co-activation detection in ten-finger typing on a virtual keyboard

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/co-activation_detection_in_ten-finger_typing_on_a_virtual_keyboard.pdf
    19 Feb 2021: Co-activation detection in ten-fingertyping on a virtual keyboard. Conor Foy. Supervisors: Prof. Per Ola Kristensson. Dr. John Dudley. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of

  9. The Gaussian Process Latent Autoregressive Model

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/gaussian_process_latent_autoregressive_model.pdf
    11 Feb 2021: The Gaussian Process LatentAutoregressive Model. Rui Xia. Supervisors:Dr. Richard E. Turner. Wessel BruinsmaWilliam Tebbutt. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in

  10. Improving Deep Ensembles for Better Deep Uncertainty Quantification

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/improving_deep_ensembles.pdf
    15 Nov 2021: We briefly trialled a custom architecture – a multi-layer perceptron consisting of threehidden layers with ReLU activations, 200 units each and batch normalisation after every fully-connected layer.

  11. Neural Models for Non-Uniformly Sampled Data

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/neural_models_for_non-uniformly_sampled_data.pdf
    11 Feb 2021: Neural Models for Non-UniformlySampled Data. Aliaksandra Shysheya. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Lucy Cavendish

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