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Outlier Detection with Hierarchical VAEs and Hamiltonian Monte Carlo
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/outlier_detection_with_hierarchical_vaes_and_hamiltonian_monte_carlo.pdf25 Nov 2022: 224.2 VAE Outlier Detection Mini Survey. 24. 4.2.1 Denouden et al. ... outlier detectionperformance becomes worse with HMC). 4.2 VAE Outlier Detection Mini Survey 24. -
Matching Networks for Individual Organ Transplantation Allocation
https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/matching_networks_for_individual_organ_transplantation_allocation.pdf11 Feb 2021: models under different settings. 24. 5.4 Factual outcome precisions of SCCN and benchmarks trained on GMixBi-. ... the setting is naturally similar to the Neyman–Rubin causal model [24], described as follow. -
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.pdf11 Feb 2021: 24. 2.3 Max Sliced Kernelized Stein Discrepancy. 26. 2.3.1 Sliced Stein Discrepancy. ... 23. Eπ[h(x)] = 0, then we can avoid this intractability. [24] proposed to achieve it by applying. -
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 -
Better Encoders for Neural Process Family Models
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/better_encoders_for_neural_process_family_models.pdf6 Dec 2022: 23. 3.1.1 Fast Computation of the KL-Divergence. 24. 3.2 How it Fits into the Family. -
Non-Gaussian Lévy Processes in Machine Learning
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/non-gaussian_levy_processes_in_machine_learning_reduced.pdf25 Nov 2022: 554.23 Average value of a filter. 564.24 Sample paths of a GIG subordinator. ... QGIG(x) =exγ. 2/2. x. [(0,). exy. π 2y|H|λ|(δ. 2y)|2dy max(0,λ ). ], for x > 0. 24 Simulation of Lévy processes. Notice that the GIG Lévy -
Beyond independent masking in tabular self-supervision
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/beyond_independent_masking_in_tabular_self-supervision.pdf25 Nov 2022: 24 Self-supervision with correlated masks. meta-data. The provided label is a regression target for the number of comments on a sampleblog post in a 24 hour period (Buza, 2014), -
Graph Representation Learning for Child Mental Health Prediction
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/graph_representation_learning_for_child_mental_health_prediction.pdf25 Nov 2022: Age # of Individuals Percent of Cohort4-7 1644 6.21. 8-11 7,926 16.9712-15 11,124 23.8216-19 11,455 24.5320-23 9,355 -
Fairness in Machine Learning withCausal Reasoning Philip Ball…
https://www.mlmi.eng.cam.ac.uk/files/ball_thesis.pdf6 Nov 2019: 24. Table of contents vii. 2.5.2 Constrained Training. 252.5.3 Post-Processing. 25. ... 2.23)= 0.05 (2.24). -
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).
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