5 citations · 6 across the 8 of their papers we have counts for
10 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…
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…
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…
High Significant Fault Detection in Azure Core Workload Insights
Pranay Lohia, Laurent Boue, Sharath Rangappa +1
Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to…
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…