Raghav Agarwal
Biographic Data
| ID | 10133764 |
|---|---|
| NAME | Raghav Agarwal |
| GIVEN NAMES | Raghav |
| FAMILY NAME | Agarwal |
| SIGNATURE | AGARWAL R |
| AFFILIATIONS | Texas Instruments (India) |
| ORCID | 0009-0001-2974-3743 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Zero-Shot Learning and Few-Shot Learning with Generative AI
Modern artificial intelligence systems frequently rely on vast amounts of labeled data to achieve robust performance, yet many real-world scenarios suffer from limited data availability. This paper investigates the potential of integrating zero-shot and few-shot learning paradigms with generative AI models to bridge the persistent data gap. Zero-shot learning empowers models to recognize and classify instances from unseen categories by leveraging…
CohortSync
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageab…
No prominent works on this page.
Zero-Shot Learning and Few-Shot Learning with Generative AI
Modern artificial intelligence systems frequently rely on vast amounts of labeled data to achieve robust performance, yet many real-world scenarios suffer from limited data availability. This paper investigates the potential of integrating zero-shot and few-shot learning paradigms with generative AI models to bridge the persistent data gap. Zero-shot learning empowers models to recognize and classify instances from unseen categories by leveraging…
CohortSync
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageab…
Computer Science (2 works) · Artificial Intelligence (1 works) · Computer security (1 works) · COVID-19 diagnosis using AI (1 works) · Distributed and Parallel Computing Systems (1 works) · Distributed computing (1 works) · Distributed systems and fault tolerance (1 works) · Domain Adaptation and Few-Shot Learning (1 works) · Engineering (1 works) · Generative grammar (1 works)