2 citations · 3 across the 5 of their papers we have counts for
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G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups
Sourya Basu, Suhas Lohit, Matthew Brand
Group equivariance is a strong inductive bias useful in a wide range of deep learning tasks. However, constructing efficient equivariant networks for general groups and domains is…
Efficient Model-Agnostic Multi-Group Equivariant Networks
Razan Baltaji, Sourya Basu, Lav R. Varshney
Constructing model-agnostic group equivariant networks, such as equitune (Basu et al., 2023b) and its generalizations (Kim et al., 2023), can be computationally expensive for large…
Transformers are Universal Predictors
Sourya Basu, Moulik Choraria, Lav R. Varshney
We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense. We further analyze performan…
Efficient Equivariant Transfer Learning from Pretrained Models
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri +4
Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba e…
Autoequivariant Network Search via Group Decomposition
Sourya Basu, Akshayaa Magesh, Harshit Yadav +1
Recent works show that group equivariance as an inductive bias improves neural network performance for both classification and generation. However, designing group-equivariant neur…