27 citations · 65 across the 13 of their papers we have counts for
7 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…
Counting Without Numbers and Finding Without Words
Badri Narayana Patro
Every year, 10 million pets enter shelters, separated from their families. Despite desperate searches by both guardians and lost animals, 70% never reunite, not because matches do…
Efficient Text-Guided Convolutional Adapter for the Diffusion Model
Aryan Das, Koushik Biswas, Swalpa Kumar Roy +2
We introduce the Nexus Adapters, novel text-guided efficient adapters to the diffusion-based framework for the Structure Preserving Conditional Generation (SPCG). Recently, structu…
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
Badri N. Patro, Vijay S. Agneeswaran
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, rece…
Scattering Vision Transformer: Spectral Mixing Matters
Badri N. Patro, Vijay Srinivas Agneeswaran
Vision transformers have gained significant attention and achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentat…
Efficiency 360: Efficient Vision Transformers
Badri N. Patro, Vijay Srinivas Agneeswaran
Transformers are widely used for solving tasks in natural language processing, computer vision, speech, and music domains. In this paper, we talk about the efficiency of transforme…