Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

A. Easwaran

h-index 28 View on Semantic Scholar

We have 4 of 196 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Aug 2026

Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines

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 instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and r...

Eduardo de Conto, B. Genest, A. Easwaran et al. · 0 citations
Preprint Aug 2026

HyperCut: Fast Inter-Layer Scheduling via Directed Hypergraph and Early Filtering

This work proposes a hierarchical partitioning-and-mapping framework, HyperCut, that enables early filtering of inter-layer schedules based on hypergraph partitioning that achieves 2.0x performance improvement and 80.47% exploration time reduction over the SET baseline, measured by geometric mean.

Ziang Wei, Zirui Xu, Sufeng Guo et al. · 0 citations
Jul 2026

A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation

Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation c...

Mainak Dan, A. Easwaran · 0 citations
#reinforcement learning Open access Aug 2026

Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning

This work proposes neurosymbolic HRL with {\em Incremental Knowledge (InK), where symbolic high-level components perform symbolic planning on an updatable representation of current InK, while low-level goal-conditioned neural modules learn motion primitives through experience using reward shaping.

Subrat Prasad Panda, B. Genest, A. Easwaran · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.