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20212026
most citedParallel Machine Learning for Forecasting the Dynamics of Complex Networks

56 citations · 56 across the 5 of their papers we have counts for

collaborators

5 papers

math.LO2026

On Cohesive Products of Fields

Rumen Dimitrov, Valentina Harizanov, Henry J. Klatt +1

We develop the foundations of effective ultraproducts of fields and their Galois groups using the methods of computability theory. These computability-theoretic analogs of ultrapro…

cs.NE2025

Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics

Keshav Srinivasan, Dietmar Plenz, Michelle Girvan

Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles…

physics.bio-ph2024

Machine learning from limited data: Predicting biological dynamics under a time-varying external input

Hoony Kang, Keshav Srinivasan, Wolfgang Losert

Reservoir computing (RC) is known as a powerful machine learning approach for learning complex dynamics from limited data. Here, we use RC to predict highly stochastic dynamics of…

math.LO2023

Cohesive Powers of Structures

Valentina Harizanov, Keshav Srinivasan

A cohesive power of a structure is an effective analog of the classical ultrapower of a structure. We start with a computable structure, and consider its countable ultrapower over…

cs.LG2021★ 56 cited

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

Keshav Srinivasan, Nolan Coble, Joy Hamlin +3

Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task…