Can deep-sub-micron device noise be used as the basis for probabilistic neural computation?

This thesis explores the potential of probabilistic neural architectures for computation with future nanoscale Metal-Oxide-Semiconductor Field Effect Transistors (MOSFETs). In particular, the performance of a Continuous Restricted Boltzmann Machine {CRBM) implemented with generated noise of Random Telegraph Signal (RTS) and 1/ f form has been studied with reference to the 'typical' Gaussian implementation. In this study, a time domain RTS based noise analysis capability has been developed based upon future nanoscale MOSFETs, to represent the effect of nanoscale MOSFET noise on circuit implementation in particular the synaptic analogue multiplier which is subsequently used to implement stochastic behaviour of the CRBM. The result of this thesis indicates little degradation in performance from that of the typical Gaussian CRBM. Through simulation experiments, the CRBM with nanoscale MOSFET noise shows the ability to reconstruct training data, although it takes longer to converge to equilibrium. The results in this thesis do not prove that nanoscale MOSFET noise can be exploited in all contexts and with all data, for probabilistic computation. However, the result indicates, for the first time, that nanoscale MOSFET noise has the potential to be used for probabilistic neural computation hardware implementation. This thesis thus introduces a methodology for a form of technology-downstreaming and highlights the potential of probabilistic architecture for computation with future nanoscale MOSFETs

Explainable Recommender with Geometric Information Bottleneck

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours.Comment: Accepted by TKD

Explainable Recommender with Geometric Information Bottleneck

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms

Representation Learning and Applications in Local Differential Privacy

Latent variable models (LVMs) provide an elegant, efficient, and interpretable approach to learning the generation process of observed data. Latent variables can capture salient features within often highly-correlated data, forming powerful tools in machine learning. For high-dimensional data, LVMs are typically parameterised by deep neural networks, and trained by maximising a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. In this work, we first introduce a novel approach to latent variable modelling, based on an objective that encourages both data reconstruction and generation. This ensures by design that the latent representations capture information about the data. Second, we consider a novel approach to inducing local differential privacy (LDP) in high dimensions with a specifically-designed LVM. LDP offers a rigorous approach to preserving one’s privacy against both adversaries and the database administrator. Existing LDP mechanisms struggle to retain data utility in high dimensions owing to prohibitive noise requirements. We circumvent this by inducing LDP on the low- dimensional manifold underlying the data. Further, we introduce a novel approach for downstream model learning using LDP training data, enabling the training of performant machine learning models. We achieve significant performance gains over current state-of-the-art LDP mechanisms, demonstrating far-reaching implications for the widespread practice of data collection and sharing. Finally, we scale up this approach, adapting current state-of-the-art representation learning models to induce LDP in even higher-dimensions, further widening the scope of LDP mechanisms for high-dimensional data collection

On deep generative modelling methods for protein-protein interaction

Proteins form the basis for almost all biological processes, identifying the interactions that proteins have with themselves, the environment, and each other are critical to understanding their biological function in an organism, and thus the impact of drugs designed to affect them. Consequently a significant body of research and development focuses on methods to analyse and predict protein structure and interactions. Due to the breadth of possible interactions and the complexity of structures, \textit{in sillico} methods are used to propose models of both interaction and structure that can then be verified experimentally. However the computational complexity of protein interaction means that full physical simulation of these processes requires exceptional computational resources and is often infeasible. Recent advances in deep generative modelling have shown promise in correctly capturing complex conditional distributions. These models derive their basic principles from statistical mechanics and thermodynamic modelling. While the learned functions of these methods are not guaranteed to be physically accurate, they result in a similar sampling process to that suggested by the thermodynamic principles of protein folding and interaction. However, limited research has been applied to extending these models to work over the space of 3D rotation, limiting their applicability to protein models. In this thesis we develop an accelerated sampling strategy for faster sampling of potential docking locations, we then address the rotational diffusion limitation by extending diffusion models to the space of $SO(3)$ and finally present a framework for the use of this rotational diffusion model to rigid docking of proteins

Explainable recommender with geometric information bottleneck

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours