27 citations · 34 across the 13 of their papers we have counts for
14 papers
MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal +7
Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for a…
SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding
Yiqiao Jin, Rachneet Kaur, Zhen Zeng +2
Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While multimoda…
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
Rachneet Kaur, Nishan Srishankar, Zhen Zeng +2
Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual inte…
AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation
Elizabeth Fons, Elena Kochkina, Rachneet Kaur +5
This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model…
TADACap: Time-series Adaptive Domain-Aware Captioning
Elizabeth Fons, Rachneet Kaur, Zhen Zeng +4
While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. E…
Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecasting
Kausik Lakkaraju, Rachneet Kaur, Parisa Zehtabi +5
AI models, including both time-series-specific and general-purpose Foundation Models (FMs), have demonstrated strong potential in time-series forecasting across sectors like financ…
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
William Watson, Nicole Cho, Nishan Srishankar +7
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is cha…
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations
Gaurav Verma, Rachneet Kaur, Nishan Srishankar +3
State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting…
LETS-C: Leveraging Text Embedding for Time Series Classification
Rachneet Kaur, Zhen Zeng, Tucker Balch +1
Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time…
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng +4
AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the cons…
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Elizabeth Fons, Rachneet Kaur, Soham Palande +4
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, clima…
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa +2
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches…
Financial Time Series Forecasting using CNN and Transformer
Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa +3
Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult…
Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data
Vipul Satone, Rachneet Kaur, Faraz Faghri +3
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are chara…