Max Daniels
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View article: Multi-layer state evolution under random convolutional design <sup>*</sup>
Multi-layer state evolution under random convolutional design <sup>*</sup> Open
Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challeng…
View article: The relationship between change in routine and student mental wellbeing during a nationwide lockdown
The relationship between change in routine and student mental wellbeing during a nationwide lockdown Open
These findings are consistent with the current literature reporting lockdown effects on behavior. Routine timings shifted, but this change was small and largely did not affect the mental wellbeing reported by undergraduate students. The ch…
View article: It’s the Way I Tell Them. A Personal Construct Psychology Method for Analysing Narratives
It’s the Way I Tell Them. A Personal Construct Psychology Method for Analysing Narratives Open
Qualitative research methods aim to produce some form of narrative for analysis and many alternative forms of narrative analysis exist, mostly informed by social constructionist perspectives. This creates a dilemma for personal constructiv…
View article: Multi-layer State Evolution Under Random Convolutional Design
Multi-layer State Evolution Under Random Convolutional Design Open
Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challeng…
View article: Score-based Generative Neural Networks for Large-Scale Optimal Transport
Score-based Generative Neural Networks for Large-Scale Optimal Transport Open
We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions. In certain cases, the optimal transport plan takes the form of a one-to-one mapping from the source support to th…
View article: Generator Surgery for Compressed Sensing
Generator Surgery for Compressed Sensing Open
Image recovery from compressive measurements requires a signal prior for the images being reconstructed. Recent work has explored the use of deep generative models with low latent dimension as signal priors for such problems. However, thei…
View article: The effect of change in routine on student mental wellbeing during a nationwide lockdown
The effect of change in routine on student mental wellbeing during a nationwide lockdown Open
Objective: There is limited existing research on the structure of routine and mental wellbeing. During March 2020, the UK entered a national lockdown, causing a sudden change in undergraduate students’ routines. This study uses this event …
View article: Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder Open
Generative models, such as GANs, learn an explicit low-dimensional representation of a particular class of images, and so they may be used as natural image priors for solving inverse problems such as image restoration and compressive sensi…
View article: Invertible generative models for inverse problems: mitigating representation error and dataset bias
Invertible generative models for inverse problems: mitigating representation error and dataset bias Open
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. …
View article: Invertible generative models for inverse problems: mitigating\n representation error and dataset bias
Invertible generative models for inverse problems: mitigating\n representation error and dataset bias Open
Trained generative models have shown remarkable performance as priors for\ninverse problems in imaging -- for example, Generative Adversarial Network\npriors permit recovery of test images from 5-10x fewer measurements than\nsparsity prior…