27 citations · 65 across the 13 of their papers we have counts for
15 papers
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…
QiT: Quantum-Inspired Transformer for Visual Recognition Task
Badri N. Patro, Vijay Agneeswaran
Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities and interactions can be expres…
Unsure but Certain: Uncovering the Representation-Confidence Gap in Diffusion Language Models
Saurabh Yadav, Badri Narayana Patro, Vijay Srinivas Agneeswaran
Diffusion language models use broad context to create text, suggesting they might handle input noise better than standard models. Testing reveals this is only partially true. Inter…
Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models
Saurabh Yadav, Badri Narayana Patro, Vijay Srinivas Agneeswaran
Diffusion Language Models (DLMs) offer a compelling alternative to autoregressive (AR) generation by enabling bidirectional context and iterative refinement. However, their reliabi…
NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
Badri N. Patro, Vijay S. Agneeswaran
State space models (SSMs) achieve linear-time complexity but struggle with multi-channel physiological signals due to three limitations: fixed kernels cannot capture multi-scale te…
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…
LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems
Badri N. Patro, Vijay S. Agneeswaran
The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems approaching human-level performance. We pre…
AI Foundation Model for Heliophysics: Applications, Design, and Implementation
Sujit Roy, Talwinder Singh, Marcus Freitag +14
Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness…
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges
Badri Narayana Patro, Vijay Srinivas Agneeswaran
Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinfor…
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…
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
Badri N. Patro, Vinod K. Kurmi, Sandeep Kumar +1
In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for…