activity
20182021
most citedPort-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

51 citations · 54 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG202151 cited

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Shaan Desai, Marios Mattheakis, David Sondak +2

Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms…

cs.LG20213 cited

Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL

Jack Parker-Holder, Vu Nguyen, Shaan Desai +1

Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters. As such, there has recently been interest in the field…

stat.ML2020

DYNOTEARS: Structure Learning from Time-Series Data

Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai +4

We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slic…

physics.app-ph2019

Deep learning-based quality filtering of mechanically exfoliated 2D crystals

Yu Saito, Kento Shin, Kei Terayama1 +7

Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensiona…

cond-mat.mtrl-sci2018

Data-driven studies of magnetic two-dimensional materials

Trevor David Rhone, Wei Chen, Shaan Desai +2

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form ABX, ba…