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MPhil in Machine Learning and Machine Intelligence



Dissertations, June 2021

A Model-Based Design Tool for 3D GUI Layout Design that Accommodates User Attributes

A Policy Agnostic Framework for Post Hoc Analysis of Organ Allocation Policies

Bootstrap Your Flow

Building a Conversational User Simulator Using Generative Adversarial Networks

Causal Representation Learning for Latent Space Optimization

Conditional Neural Processes and Semi-Supervised Learning

Continuity of Autoencoders, Unsupervised Anomaly Detection and Deep Atlases

Contrastive Self-Supervised Learning for Tabular Data

Controlling Hallucination while Generating Text from Structured Data

Depth Uncertainty Networks for Active Learning

Efficiently-Parametrised Approximate Posteriors in Pseudo-Point Approximations to Gaussian Processes

Exploration and Exploitation: From Bandits to Bayesian Optimisation

Fair Policy Learning

Flow Field and Shape Inference in Magnetic Resonance Velocimetry Using Physics-Informed Neural Networks

GPT-3 for Few-Shot Dialogue State Tracking

Improving Deep Ensembles for Better Deep Uncertainty Quantification

Interpretability for Conditional Average Treatment Effect Estimation

Knowledge Distillation for End-to-End Automatic Speech Recognition

Mitigating Gender Bias in Dialogue Generation

Probabilistic Model Compression


Advanced Machine Learning Module Posters, March 2021

Conditional Neural Processes

Importance Weighted Autoencoder (IWAE)

Neural Processes

Weight Uncertainty in Neural Networks