77 citations · 199 across the 40 of their papers we have counts for
7 papers · 2 filters
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…
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
Viraj Shah, Rakib Hyder, M. Salman Asif +1
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled…
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…