Zobrazeno 1 - 10
of 343
pro vyhledávání: '"Louis, Ard. A."'
Deep neural networks (DNNs) exhibit a remarkable ability to automatically learn data representations, finding appropriate features without human input. Here we present a method for analysing feature learning by decomposing DNNs into 1) a forward feat
Externí odkaz:
http://arxiv.org/abs/2410.04264
Quantum machine learning models based on parametrized quantum circuits, also called quantum neural networks (QNNs), are considered to be among the most promising candidates for applications on near-term quantum devices. Here we explore the expressivi
Externí odkaz:
http://arxiv.org/abs/2407.04371
Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time, training data, or model size increases, a phenomenon known as emergence. In this paper, we present a framework where each new ability (a ski
Externí odkaz:
http://arxiv.org/abs/2404.17563
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. This phenomenon is known as \emph{simplicity bias}. By viewing the parame
Externí odkaz:
http://arxiv.org/abs/2403.06989
Publikováno v:
J. Chem. Phys. 160, 115101 (2024)
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 naturally reproduces the physical properties of hybrid duplexes including
Externí odkaz:
http://arxiv.org/abs/2311.07709
The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components, we apply a Bayesian picture,
Externí odkaz:
http://arxiv.org/abs/2304.06670
Publikováno v:
ACS Nano 2023
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 blocks that unambiguously define the target structure. Using a novel pipel
Externí odkaz:
http://arxiv.org/abs/2207.06954
Autor:
Doye, Jonathan P. K., Louis, Ard A., Schreck, John S., Romano, Flavio, Harrison, Ryan M., Mosayebi, Majid, Engel, Megan C., Ouldridge, Thomas E.
Publikováno v:
Frontiers of Nanoscience, Vol. 21, Ch. 9, pp 195-210 (2022)
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 DNA assemblies used in the field of DNA nanotechnology. The landscape
Externí odkaz:
http://arxiv.org/abs/2111.10166
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 different flatness measures. Recently, this link with generalization has bee
Externí odkaz:
http://arxiv.org/abs/2103.06219
Double-descent curves in neural networks describe the phenomenon that the generalisation error initially descends with increasing parameters, then grows after reaching an optimal number of parameters which is less than the number of data points, but
Externí odkaz:
http://arxiv.org/abs/2102.07238