4 citations · 7 across the 7 of their papers we have counts for
7 papers
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device techno…
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by…
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Sagar Srinivas Sakhinana, Geethan Sannidhi, Chidaksh Ravuru +1
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynam…
Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials using electron micrographs is crucial in areas such as semiconductors and quantum materials. Traditional classification methods falter due to the intricate…
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductor imaging and analysis are critical yet understudied in deep learning, limiting our ability for precise control and optimization in semiconductor manufacturing. We intr…
Agentic Retrieval-Augmented Generation for Time Series Analysis
Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana
Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical…