1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Precision, Stability, and Generalization: A Comprehensive Assessment of RNNs learnability capability for Classifying Counter and Dyck Languages
Neisarg Dave, Daniel Kifer, Lee Giles +1
This study investigates the learnability of Recurrent Neural Networks (RNNs) in classifying structured formal languages, focusing on counter and Dyck languages. Traditionally, both…
cs.CL2024★ 1 cited
Investigating Symbolic Capabilities of Large Language Models
Neisarg Dave, Daniel Kifer, C. Lee Giles +1
Prompting techniques have significantly enhanced the capabilities of Large Language Models (LLMs) across various complex tasks, including reasoning, planning, and solving math word…
cs.LG2024
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
Neisarg Dave, Daniel Kifer, C. Lee Giles +1
This paper analyzes two competing rule extraction methodologies: quantization and equivalence query. We trained RNN models, extracting DFA with a quantization approa…