2 citations · 3 across the 5 of their papers we have counts for
5 papers
Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning
Mantek Singh, Jeshwanth Challagundla, Siddharth Raina +1
We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Our approach fine-tunes a 2B-para…
LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3
This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of label…
SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models
Jeshwanth Challagundla, Mantek Singh, Siddharth Raina +3
System Instructions (SIs), or system prompts, are pivotal for guiding Large Language Models (LLMs) but manual crafting is resource-intensive and often suboptimal. Existing automate…
Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification
Kanishka Tyagi, Chinmay Rane, Ketaki Vaidya +3
This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE c…
Adaptive multiple optimal learning factors for neural network training
Jeshwanth Challagundla
This thesis presents a novel approach to neural network training that addresses the challenge of determining the optimal number of learning factors. The proposed Adaptive Multiple…