Vineeth S

Vineeth S

Hardware compute power has been growing at an unprecedented rate in recent years. The utilization of such advancements plays a key role in producing better results in less time -- both in academia and industry. However, merging the existing hardware with the latest hardware within the same ecosystem poses a challenging task. One of the key challenges, in this case, is varying compute power. In this paper, we consider the training of deep neural networks on a distributed system of workers with varying compute power. A naive implementation of synchronous distributed training will result in the faster workers waiting for the slowest worker to complete processing. To mitigate this issue, we propose to dynamically adjust the data assigned for each worker during the training. We assign each worker a partition of total data proportional to its computing power. Our experiments show that dynamically adjusting the data partition helps to improve the utilization of the system and significantly reduces the time taken for training. Code is available at the repository: \url{https://github.com/vineeths96/Heterogeneous-Systems}.

Bevin V, Ananthakrishnan P, Ragesh KR et al.

The performance of machine learning models for automated invoice processing is critically dependent on large-scale, diverse datasets. However, the acquisition of such datasets is often constrained by privacy regulations and the high cost of manual annotation. To address this, we present a novel pipeline for generating high-fidelity, synthetic invoice documents and their corresponding structured data. Our method first utilizes Optical Character Recognition (OCR) to extract the text content and precise spatial layout from a source invoice. Select data fields are then replaced with contextually realistic, synthetic content generated by a large language model (LLM). Finally, we employ an inpainting technique to erase the original text from the image and render the new, synthetic text in its place, preserving the exact layout and font characteristics. This process yields a pair of outputs: a visually realistic new invoice image and a perfectly aligned structured data file (JSON) reflecting the synthetic content. Our approach provides a scalable and automated solution to amplify small, private datasets, enabling the creation of large, varied corpora for training more robust and accurate document intelligence models.