5 citations · 6 across the 8 of their papers we have counts for
3 papers · 1 filter
Riemannian--Lorentz Fusion of Vision Transformers and State-Space Models
Badri N. Patro, Vijay S. Agneeswaran
Scaling deep learning faces critical bottlenecks: data exhaustion, exponential training costs, and resource concentration. Model merging combines pre-trained checkpoints without gr…
HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
Badri N. Patro, Vijay S. Agneeswaran
Vision State Space Models (SSMs) like Vim, VMamba, and SiMBA rely on complex scanning strategies to adapt sequential SSMs to process 2D images, introducing computational overhead a…
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis
Badri N. Patro, Suhas Ranganath, Vinay P. Namboodiri +1
Transformers have revolutionized image modeling tasks with adaptations like DeIT, Swin, SVT, Biformer, STVit, and FDVIT. However, these models often face challenges with inductive…