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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. -
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. -
Bootstrap Your Flow
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/bootstrap_your_flow_reduced.pdf15 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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