6 papers · 1 filter
Agile Reinforcement Learning through Separable Neural Architecture and Applications
Rajib Mostakim, Reza T. Batley, Sourav Saha
Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often paramet…
Separable Neural Architectures as Physical World Models: from Mathematical Theory to Applications
Reza T Batley, Andrew Kichline, Sourav Saha
This work introduces the Separable Neural Architecture (SNA), a function representational class combining neural approximation with tensor decomposition. The SNA decouples localize…
Separable neural architectures as a primitive for unified predictive and generative intelligence
Reza T. Batley, Apurba Sarker, Rajib Mostakim +2
Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicit…
A Unified Generative-Predictive Framework for Deterministic Inverse Design
Reza T. Batley, Sourav Saha
Inverse design of heterogeneous material microstructures is a fundamentally ill-posed and famously computationally expensive problem. This is exacerbated by the high-dimensional de…
The Method of Infinite Descent
Reza T. Batley, Sourav Saha
Training - the optimisation of complex models - is traditionally performed through small, local, iterative updates [D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature 323, 533-5…
Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Reza T. Batley, Chanwook Park, Wing Kam Liu +1
Data-driven science and computation have advanced immensely to construct complex functional relationships using trainable parameters. However, efficiently discovering interpretable…