498 citations · 7.6k across the 97 of their papers we have counts for
98 papers
Exploring simplicity bias in 1D dynamical systems
Kamaludin Dingle, Mohammad Alaskandarani, Boumediene Hamzi +1
Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. Thi…
Coarse-grained modelling of DNA-RNA hybrids
Eryk J. Ratajczyk, Petr Šulc, Andrew J. Turberfield +2
We introduce oxNA, a new model for the simulation of DNA-RNA hybrids which is based on two previously developed coarse-grained models$\unicode{x2014}$oxDNA and oxRNA. The model nat…
Deep neural networks have an inbuilt Occam's razor
Chris Mingard, Henry Rees, Guillermo Valle-Pérez +1
The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the dat…
Designing the self-assembly of arbitrary shapes using minimal complexity building blocks
Joakim Bohlin, Andrew J. Turberfield, Ard A. Louis +1
The design space for a self-assembled multicomponent objects ranges from a solution in which every building block is unique to one with the minimum number of distinct building bloc…
Free-energy landscapes of DNA and its assemblies: Perspectives from coarse-grained modelling
Jonathan P. K. Doye, Ard A. Louis, John S. Schreck +5
This chapter will provide an overview of how characterizing free-energy landscapes can provide insights into the biophysical properties of DNA, as well as into the behaviour of the…
Why flatness does and does not correlate with generalization for deep neural networks
Shuofeng Zhang, Isaac Reid, Guillermo Valle Pérez +1
The intuition that local flatness of the loss landscape is correlated with better generalization for deep neural networks (DNNs) has been explored for decades, spawning many differ…