Arvind Ramanathan — accepted AI-conference papers

2024

Equivariant Graph Neural Operator for Modeling 3D Dynamics

Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli +3

ICML 2024poster

Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics. Machine learning methods have achieved good success by learning graph neural networks to model spati…

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2022

Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations

Sumit Kumar Jha, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, Susmit Jha

AAAI 2022technical

Neural SDEs with Brownian motion as noise lead to smoother attributions than traditional ResNets. Various attribution methods such as saliency maps, integrated gradients, DeepSHAP and DeepLIFT have been shown to be more robust for neural SDEs than for ResNets using the recently proposed sensitivity…

Cited by 12 Source ↗PDF ↗Scholar ↗