4 papers
: A library for Linear RNNs
Karan Bania, Soham Kalburgi, Manit Tanwar +8
Linear recurrent neural networks (LRNNs) provide a structured approach to sequence modeling that bridges classical linear dynamical systems and modern deep learning, offering both…
Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol
Harshvardhan Mestha, Karan Bania, Shreyas V Sathyanarayana +2
Our interest is in the design of software systems involving a human-expert interacting -- using natural language -- with a large language model (LLM) on data analysis tasks. For co…
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient…
CountCLIP -- [Re] Teaching CLIP to Count to Ten
Harshvardhan Mestha, Tejas Agrawal, Karan Bania +2
Large vision-language models (VLMs) are shown to learn rich joint image-text representations enabling high performances in relevant downstream tasks. However, they fail to showcase…