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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.pdf15 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. -
Improving Deep Ensembles for Better Deep Uncertainty Quantification
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/improving_deep_ensembles.pdf15 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 -
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.pdf15 Nov 2021: 23. 3.4 Experimental Setup. 243.4.1 Model Configuration. 243.4.2 Experiment Size. 24. -
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.pdf15 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 -
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.pdf15 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. -
Efficiently-Parametrised Approximate Posteriors in Pseudo-Point…
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/efficiently_parametrized_approximate_posteriors.pdf15 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):. -
Depth Uncertainty Networks for Active Learning
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/depth_uncertainty_networks_for_active_learning_reduced.pdf15 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). -
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.pdf15 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. -
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.pdf15 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 -
Building a Conversational User Simulator Using Generative Adversarial …
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/building_a_conversational_user_simulator.pdf15 Nov 2021: 244.3.1 Policy Training. 244.3.2 Policy Evaluation. 24. 5 Adversarial Training Experiments 265.1 MLE Pre-Training.
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