Matthew Willetts
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View article: Liquid Chromatographic and Mass Spectrometric Methods for Quantitative Proteomic Analysis from Single-Cell and Nanogram-Level Samples
Liquid Chromatographic and Mass Spectrometric Methods for Quantitative Proteomic Analysis from Single-Cell and Nanogram-Level Samples Open
Liquid chromatography (LC) and mass spectrometry (MS) are two critical components in proteomics. Advances in methods for both LC and MS have significantly enhanced protein identification and quantifications of limited amounts of proteins, …
View article: Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing
Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing Open
Automated Market Makers (AMMs) hold assets and are constantly being rebalanced by external arbitrageurs to match external market prices. Loss-versus-rebalancing (LVR) is a pivotal metric for measuring how an AMM pool performs for its liqui…
View article: Multiblock MEV opportunities & protections in dynamic AMMs
Multiblock MEV opportunities & protections in dynamic AMMs Open
Maximal Extractable Value (MEV) in Constant Function Market Making is fairly well understood. Does having dynamic weights, as found in liquidity boostrap pools (LBPs), Temporal-function market makers (TFMMs), and Replicating market makers …
View article: Optimal Rebalancing in Dynamic AMMs
Optimal Rebalancing in Dynamic AMMs Open
Dynamic AMM pools, as found in Temporal Function Market Making, rebalance their holdings to a new desired ratio (e.g. moving from being 50-50 between two assets to being 90-10 in favour of one of them) by introducing an arbitrage opportuni…
View article: Big Data Analytics Maturity Model for SMEs
Big Data Analytics Maturity Model for SMEs Open
Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability an…
View article: Closed-form solutions for generic N-token AMM arbitrage
Closed-form solutions for generic N-token AMM arbitrage Open
Convex optimisation has provided a mechanism to determine arbitrage trades on automated market markets (AMMs) since almost their inception. Here we outline generic closed-form solutions for $N$-token geometric mean market maker pool arbitr…
View article: Evaluation of a software positioning tool to support SMEs in adoption of big data analytics
Evaluation of a software positioning tool to support SMEs in adoption of big data analytics Open
Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium size…
View article: Software Positioning Tool to Support SMEs in Adoption of Big Data Analytics using a Case Study Application
Software Positioning Tool to Support SMEs in Adoption of Big Data Analytics using a Case Study Application Open
Big Data Analytics is widely adopted by large companies but to a lesser extent by small to medium-sized enterprises (SMEs). SMEs comprise 99% of all businesses in the UK (6 million), employ 61% of the country’s workforce and generate over …
View article: A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs Open
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets a…
View article: Qualitative study on barriers of adopting big data analytics for UK SMEs
Qualitative study on barriers of adopting big data analytics for UK SMEs Open
Big data analytics have been widely adopted by large companies to achieve competitive advantage. However, small and medium-sized enterprises (SMEs) are underutilising this technology due to the existence of a number of barriers to adoption…
View article: Qualitative Study on Barriers of Adopting Big Data Analytics for UK SMEs
Qualitative Study on Barriers of Adopting Big Data Analytics for UK SMEs Open
View article: HLAII peptide presentation of infliximab increases when complexed with TNF
HLAII peptide presentation of infliximab increases when complexed with TNF Open
CD4+ T-cell activation through recognition of Human Leukocyte Antigen II (HLAII)-presented peptides is a key step in the development of unwanted immune response against biotherapeutics, such as the generation of anti-drug antibodies (ADA).…
View article: Increasing the throughput of sensitive proteomics by plexDIA
Increasing the throughput of sensitive proteomics by plexDIA Open
View article: Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants Open
This Article contains an error in Supplementary Table 1, where the "Ground truth → Prediction↓" labels were incorrectly ordered as "Prediction → Ground truth↓".Additionally, the Data and code availability section is incomplete."Upon public…
View article: I Don't Need $\mathbf{u}$: Identifiable Non-Linear ICA Without Side Information
I Don't Need $\mathbf{u}$: Identifiable Non-Linear ICA Without Side Information Open
In this work we introduce a new approach for identifiable non-linear ICA models. Recently there has been a renaissance in identifiability results in deep generative models, not least for non-linear ICA. These prior works, however, have ass…
View article: Multi-Facet Clustering Variational Autoencoders
Multi-Facet Clustering Variational Autoencoders Open
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a …
View article: I Don't Need u: Identifiable Non-Linear ICA Without Side Information
I Don't Need u: Identifiable Non-Linear ICA Without Side Information Open
In this paper, we investigate the algorithmic stability of unsupervised representation learning with deep generative models, as a function of repeated re-training on the same input data. Algorithms for learning low dimensional linear repre…
View article: Variational Autoencoders: A Harmonic Perspective
Variational Autoencoders: A Harmonic Perspective Open
In this work we study Variational Autoencoders (VAEs) from the perspective of harmonic analysis. By viewing a VAE's latent space as a Gaussian Space, a variety of measure space, we derive a series of results that show that the encoder vari…
View article: Certifiably Robust Variational Autoencoders
Certifiably Robust Variational Autoencoders Open
We introduce an approach for training Variational Autoencoders (VAEs) that are certifiably robust to adversarial attack. Specifically, we first derive actionable bounds on the minimal size of an input perturbation required to change a VAE'…
View article: Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels Open
In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the…
View article: Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders Open
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations. While previous work has developed algorithmic approaches to attacking and defending VAEs, there rema…
View article: Towards a Theoretical Understanding of the Robustness of Variational\n Autoencoders
Towards a Theoretical Understanding of the Robustness of Variational\n Autoencoders Open
We make inroads into understanding the robustness of Variational Autoencoders\n(VAEs) to adversarial attacks and other input perturbations. While previous\nwork has developed algorithmic approaches to attacking and defending VAEs,\nthere r…
View article: Relaxed-Responsibility Hierarchical Discrete VAEs
Relaxed-Responsibility Hierarchical Discrete VAEs Open
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research. Vector-Quantised VAEs are a powerful approach to discrete VAEs, but naive hierarchical extensions can b…
View article: Explicit Regularisation in Gaussian Noise Injections
Explicit Regularisation in Gaussian Noise Injections Open
We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have been few studies on understanding the regularising effect the…
View article: Learning Bijective Feature Maps for Linear ICA
Learning Bijective Feature Maps for Linear ICA Open
Separating high-dimensional data like images into independent latent factors, i.e independent component analysis (ICA), remains an open research problem. As we show, existing probabilistic deep generative models (DGMs), which are tailor-ma…
View article: Non-Determinism in TensorFlow ResNets
Non-Determinism in TensorFlow ResNets Open
We show that the stochasticity in training ResNets for image classification on GPUs in TensorFlow is dominated by the non-determinism from GPUs, rather than by the initialisation of the weights and biases of the network or by the sequence …
View article: Disentangling to Cluster: Gaussian Mixture Variational Ladder\n Autoencoders
Disentangling to Cluster: Gaussian Mixture Variational Ladder\n Autoencoders Open
In clustering we normally output one cluster variable for each datapoint.\nHowever it is not necessarily the case that there is only one way to partition\na given dataset into cluster components. For example, one could cluster objects\nby …
View article: Regularising Deep Networks with Deep Generative Models
Regularising Deep Networks with Deep Generative Models Open
We develop a new method for regularising neural networks. We learn a probability distribution over the activations of all layers of the model and then insert imputed values into the network during training. We obtain a posterior for an arb…
View article: Regularising Deep Networks with DGMs.
Regularising Deep Networks with DGMs. Open
Here we develop a new method for regularising neural networks where we learn a density estimator over the activations of all layers of the model. We extend recent work in data imputation using VAEs (Ivanov et al., 2018) so that we can obta…
View article: Disentangling Improves VAEs' Robustness to Adversarial Attacks.
Disentangling Improves VAEs' Robustness to Adversarial Attacks. Open
This paper is concerned with the robustness of VAEs to adversarial attacks. We highlight that conventional VAEs are brittle under attack but that methods recently introduced for disentanglement such as $\beta$-TCVAE (Chen et al., 2018) imp…