Composing Fisher Kernels from Deep Neural Models

Azim, Tayyaba; Ahmed, Sarah

doi:10.1007/978-3-319-98524-4

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Classical and Quantum Variants Of Commonly Used Algorithms1

Introduction1

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2022

2023

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Cited by 2 publications

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“…The concept of kernels is very important in machine learning, both quantum and classical. [113][114][115] Let us imagine a dataset…”

Section: Classical and Quantum Variants Of Commonly Used Algorithmsmentioning

confidence: 99%

Quantum machine learning for chemistry and physics

Sajjan

Selvarajan

et al. 2022

Chem. Soc. Rev.

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“…The concept of kernels is very important in machine learning, both quantum and classical. [113][114][115] Let us imagine a dataset…”

Section: Classical and Quantum Variants Of Commonly Used Algorithmsmentioning

confidence: 99%

Quantum machine learning for chemistry and physics

Sajjan

Selvarajan

et al. 2022

Chem. Soc. Rev.

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show abstract

“…of subjects) matrix. How to construct a kernel is however not always straightforward, and choosing an inappropriate kernel can lead to poor performance (Azim & Ahmed, 2018;Shawe-Taylor & Cristianini, 2004). We here propose a method that allows using the entire set of parameters of a generative probabilistic model of brain dynamics in a way that takes the complex relationships between the parameters into account.…”

Section: Introductionmentioning

confidence: 99%

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

Ahrends

Vidaurre

2023

Preprint

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How specifically brain activity unfolds across time, namely the nature of brain dynamics, can sometimes be more predictive of behavioural and cognitive subject traits than both brain structure and summary measures of brain activity that average across time. Brain dynamics can be described by models of varying complexity, but what is the best way to use these models of brain dynamics for characterising subject differences and predicting individual traits is unclear. While most studies aiming at predicting subjects’ traits focus on having accurate predictions, for many practical applications, it is critical for the predictions not just to be accurate but also reliable. Kernel methods are a robust and computationally efficient way of expressing differences in brain dynamics between subjects for the sake of predicting individual traits, such as clinical or psychological variables. Using the Hidden Markov model (HMM) as a model of brain dynamics, we here propose the use of the Fisher kernel, a mathematically principled approach to predict phenotypes from subject-specific HMMs. The Fisher kernel is computed in a way that preserves the mathematical structure of the HMM. This results in benefits in terms of both accuracy and reliability when compared with kernels that ignore the structure of the underlying model of brain dynamics.

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Composing Fisher Kernels from Deep Neural Models

Cited by 2 publications

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Quantum machine learning for chemistry and physics

Quantum machine learning for chemistry and physics

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

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