2022 - 2023 Course Highlights | MPhil in Machine Learning and Machine Intelligence

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Dissertations

Machine Learning:

Autoregressive Diffusion Neural Processes

Data Compression with Variational Implicit Neural Representations

Establishing a Unified Framework for Iterative Machine Teaching

Improving Uncertainty Quantification in Regression Problems through Conformal Training

Large Language Model Self-Critique for Task-Oriented Text Generation

Large Language Models for Reliable Information Extraction

Optimal PAC-Bayes Bounds and their Variational Approximations

Pre-Training Meta-Models for Interpretability

Sim2Real With Neural Processes

Speech and Language Processing:

Distilling and Forgetting in Large Pre-Trained Models

Graph Neural Stochastic Differential Equations

Incorporating Vision Encoders into Retrieval Augmented Visual Question Answering (1)

Incorporating Vision Encoders into Retrieval Augmented Visual Question Answering (2)

Multilingual Models in Neural Machine Translation

Utilizing Large Language Models for Question Answering in Task-Oriented Dialogues

Computer Vision and Robotics:

3D Pose Estimation and Topology Reconstruction Using Foundation Models and Render and Compare

Adapting Pre-Trained Vision-Language Models in Medical Domains

Breaking the Limits of Diffusion Models via Continuous Dynamical Systems

Evaluating Benefits of Heterogeneity in Constrained Multi-Agent Learning

Evaluating the Capabilities of Large Language Models for Spatial and Situational Understanding

Eliciting Latent Knowledge from Language Reward Models

Function Constrained Program Synthesis

Posters from the Advanced Machine Learning module:

Conditional and Latent Neural Processes

InfoGAN and Beyond

The Forward-Forward Algorithm: Some Preliminary Investigations

Toy Models of Superposition

Variational Continual Learning

Weight Uncertainty in Neural Networks (1)

Weight Uncertainty in Neural Networks (2)