Learning new physics from an imperfect machine

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Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

This work proposes a method to measure the photon polarisation in

B^+ \rightarrow K^+ \pi^- \pi^+ \gamma

decays and prepares the necessary elements for this measurement using the data sets collected by the LHCb experiment at CERN in

2011

2012

, $2016 ...

Learning new physics from an imperfect machine

Chattez avec Graph Search

Learnable latent embeddings for joint behavioural and neural analysis

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Partial Information Sharing Over Social Learning Networks

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Bayes-optimal Learning of Deep Random Networks of Extensive-width

On double-descent in uncertainty quantification in overparametrized models

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Expectation consistency for calibration of neural networks

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Amplitude analysis for the measurement of the photon polarisation in B rightarrow K pi pi gamma decays