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1 - 10 of 26 search results for KaKaoTalk:PC53 24 / |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 26 match some words.

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  2. Practical bayesian optimization of machine learning algorithms…

    https://www.mlmi.eng.cam.ac.uk/files/practical_bayesian_optimization.pdf
    1 Feb 2021: In: In Advances in Neural Information Processing Systems 24. 2010,pp. 1723–1731.

  3. Building a Conversational User Simulator Using Generative Adversarial …

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/building_a_conversational_user_simulator.pdf
    15 Nov 2021: 244.3.1 Policy Training. 244.3.2 Policy Evaluation. 24. 5 Adversarial Training Experiments 265.1 MLE Pre-Training.

  4. Mitigating Gender Bias in Dialogue Generation Gabrielle (Ming Yi) ...

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/mitigating_gender_bias_in_dialogue_generation.pdf
    15 Nov 2021: 23. 3.6 Token counts of GB-Ctrl validation data. 24. 3.7 Toxicity in GB-Ctrl finetuning data. ... 24. 3.8 Size of GBS-Ctrl finetuning datasets. 26. 3.9 StereoSet examples.

  5. Lossless DNA Compression Woramanot Yomjinda Supervisor: Christian…

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/lossless_dna_compression.pdf
    11 Feb 2021: 23. 3.1.1 Probability Table and Encoding. 24. 3.1.2 Binary Value Stream and Renormalisation. ... 24. any value (such as the middle value 0.591) to represent the original input sequence.

  6. Interpretable Machine Learning

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/interpretable_machine_learning.pdf
    11 Feb 2021: 204.3 Model. 22. 4.3.1 Definition. 224.3.2 Decoding procedure. 24. xii Table of contents.

  7. Multimodal Emotion Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/multimodal_emotion_recognition.pdf
    11 Feb 2021: 434.24 Inputs to the time synchronous branch. 434.25 Emotion classification results using different training and testing setting. ... Table 4.4 Model structure of the audio-based AER system. 24 Multimodal Emotion Recognition System.

  8. Flow Field and Shape Inference in Magnetic Resonance Velocimetry…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/flow_field_and_shape_inference_reduced.pdf
    15 Nov 2021: 24. 3.3 Flow at 100 Reynolds Number. 31. 3.3.1 Flow at 100 Reynolds Number: Assumptions. ... 41. 3.25 Absolute discrepancy between the reconstruction and ground truth z-velocityfields from Figure 3.24.

  9. A model-based design tool for 3D GUI layout design that accommodates…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/a_model-based_design_tool.pdf
    15 Nov 2021: This ideais often traced back to the study by Hess and Polt [24] demonstrating correlation betweenpupil size and mental activity in the form of simple multiplication problems.

  10. Efficiently-Parametrised Approximate Posteriors in Pseudo-Point…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/efficiently_parametrized_approximate_posteriors.pdf
    15 Nov 2021: exp(L1)p(u)q(u)q(u). du. (L1 log p(u)log q(u))q(u)du. = Eq(u) (L1 log p(u)log q(u)) L2 (2.24). ... L2 in Hensman et al. (2013) as follows (see equation 2.24 and 2.20 for the definition of L2and L1 respectively):.

  11. 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: Improving Deep Ensembles for BetterDeep Uncertainty Quantification. Ginte Petrulionyte. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine

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