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1 - 10 of 12 search results for katalk:za33 24 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 12 match some words.

  1. Results that match 1 of 2 words

  2. Conditional Neural Processes and Semi-Supervised Learning

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/conditional_neural_processes_and_semi-supervised_learning_0.pdf
    15 Nov 2021: Conditional Neural Processes andSemi-Supervised Learning. Max-Olivier Van Bastelaer. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence.

  3. 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.

  4. 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.

  5. GPT-3 for Few-Shot Dialogue State Tracking

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/gpt_3_for_few_shot_dialogue_state_tracking.pdf
    15 Nov 2021: 23. 3.4 Experimental Setup. 243.4.1 Model Configuration. 243.4.2 Experiment Size. 24.

  6. 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

  7. 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.

  8. Causal Representation Learning for Latent Space Optimization

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/causal_representation_learning_for_latent_space_optimization.pdf
    15 Nov 2021: Causal Representation Learning forLatent Space Optimization. Wenlin Chen. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. St

  9. 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.

  10. 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: decaying priors. 604.24 Overfitting bias for DUNs with uniform and decaying priors trained with R̃. ... w = t(φ,ε) = µ σ ε ; ε N(0,I), (2.24).

  11. Bootstrap Your Flow

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/bootstrap_your_flow_reduced.pdf
    15 Nov 2021: 3.1 Estimating α -divergence over the target vs proposal distribution. 24. ... 2.24). We provide a proof for why these importance weights are valid in Appendix A.1.

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