output
20172022
most citedDeepMoD: Deep learning for Model Discovery in noisy data

132 citations

Showing 2018Show all

6 papers · 1 filter

cond-mat.stat-mech2018★ 3 cited

Structural and temporal heterogeneities on networks

Liubov Tupikina, Denis S. Grebenkov

A heterogeneous continuous time random walk is an analytical formalism for studying and modeling diffusion processes in heterogeneous structures on microscopic and macroscopic scal…

physics.flu-dyn2018★ 6 cited

Marine crustaceans with hairy appendages: role of hydrodynamic boundary layers in sensing and feeding

Kaitlyn Hood, M. S. Suryateja Jammalamadaka, A. E. Hosoi

Decapod crustaceans have appendages with an array of rigid hairs covered in chemo-receptors, used to sense and track food. Crustaceans directly influence the flow behavior by chang…

cs.LG2018★ 90 cited

Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph

Luca Albergante, Evgeny M. Mirkes, Huidong Chen +6

Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-ce…

q-bio.QM2018★ 6 cited

The AgeGuess database: an open online resource on chronological and perceived ages of people aged 3-100

J. A. Barthold Jones, U. W. Nash, J. Vieillefont +3

In many developed countries, human life expectancy has doubled over the last 180 years from ~40 to ~80 years. Underlying this great advance is a change in how we age, yet our under…

cs.NE2018★ 36 cited

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Joel Lehman, Jeff Clune, Dusan Misevic +50

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…

q-bio.PE2018★ 13 cited

Epidemic extinction in networks: Insights from the 12,110 smallest graphs

Petter Holme, Liubov Tupikina

We investigate the expected time to extinction in the susceptible-infectious-susceptible (SIS) model of disease spreading. Rather than using stochastic simulations, or asymptotic c…