Search Results for author: Alexey Zakharov

Found 8 papers, 2 papers with code

Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization

no code implementations14 Apr 2022 Alexey Zakharov, Maxim Pisov, Alim Bukharaev, Alexey Petraikin, Sergey Morozov, Victor Gombolevskiy, Mikhail Belyaev

We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously.

Computed Tomography (CT)

Bayesian sense of time in biological and artificial brains

no code implementations14 Jan 2022 Zafeirios Fountas, Alexey Zakharov

Enquiries concerning the underlying mechanisms and the emergent properties of a biological brain have a long history of theoretical postulates and experimental findings.

Bayesian Inference

Exploration and preference satisfaction trade-off in reward-free learning

no code implementations ICML Workshop URL 2021 Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston

In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.

OpenAI Gym

Episodic Memory for Learning Subjective-Timescale Models

no code implementations3 Oct 2020 Alexey Zakharov, Matthew Crosby, Zafeirios Fountas

In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error.

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

1 code implementation13 Aug 2020 Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary

In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.

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