77 citations · 233 across the 52 of their papers we have counts for
7 papers · 1 filter
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan +5
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches ove…
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
Biswajit Khara, Aditya Balu, Ameya Joshi +4
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs). This approach contras…
Differentiable Spline Approximations
Minsu Cho, Aditya Balu, Ameya Joshi +6
The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differen…
Distributed Multigrid Neural Solvers on Megavoxel Domains
Aditya Balu, Sergio Botelho, Biswajit Khara +6
We consider the distributed training of large-scale neural networks that serve as PDE solvers producing full field outputs. We specifically consider neural solvers for the generali…
NURBS-Diff: A Differentiable Programming Module for NURBS
Anjana Deva Prasad, Aditya Balu, Harshil Shah +3
Boundary representations (B-reps) using Non-Uniform Rational B-splines (NURBS) are the de facto standard used in CAD, but their utility in deep learning-based approaches is not wel…
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang +4
Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized…