7 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model traini…
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on millions of synthetic tasks. We…
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse…
Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models,…
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it
Viswanath Ganapathy, Sauptik Dhar, Olimpiya Saha +3
In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments. Several studies have…