25 papers
HyperCut: Fast Inter-Layer Scheduling via Directed Hypergraph and Early Filtering
Ziang Wei, Zirui Xu, Sufeng Guo +4
As deep neural networks (DNNs) continue to scale, inter-layer scheduling, which orchestrates the spatial allocation of compute resources and the temporal execution order across lay…
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
Eduardo de Conto, Blaise Genest, Arvind Easwaran +2
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instan…
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
(Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compelling approach to improve samp…
A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation
Mainak Dan, Arvind Easwaran
This study introduces a unified control mechanism for integrated charging and service allocation of Mobility-on-Demand Electric Vehicles (MoD-EVs) operating under a flexible A-to-B…
SCoReT: Super-Resolution Compression and Reconstruction of Turbulent Flows
Royyuru Sai Prasanna Gangadhar, Shishir Srivastava, Nagabhushana Rao Vadlamani +1
High-fidelity simulations of the Navier--Stokes equations (NSE) generate massive amounts of data, motivating the need for efficient compression and reconstruction strategies for tu…
Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
Mohit Prashant, Arvind Easwaran
Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibili…