2 citations · 2 across the 5 of their papers we have counts for
6 papers
GiVA: Gradient-Informed Bases for Vector-Based Adaptation
Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky +4
As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent…
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Neeraj Gangwar, Anshuka Rangi, Rishabh Deshmukh +3
Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While these methods perform well in single…
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models
Neeraj Gangwar, Suma P Bhat, Nickvash Kani
While large models pre-trained on high-quality data exhibit excellent performance on mathematical reasoning (e.g., GSM8k, MultiArith), it remains challenging to specialize smaller…
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions
Hongbo Zheng, Suyuan Wang, Neeraj Gangwar +1
Vector representations have been pivotal in advancing natural language processing (NLP), with prior research focusing on embedding techniques for mathematical expressions using mat…
Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems
Neeraj Gangwar, Nickvash Kani
Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical soft…
Semantic Representations of Mathematical Expressions in a Continuous Vector Space
Neeraj Gangwar, Nickvash Kani
Mathematical notation makes up a large portion of STEM literature, yet finding semantic representations for formulae remains a challenging problem. Because mathematical notation is…