12 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Debugging using Orthogonal Gradient Descent
Narsimha Chilkuri, Chris Eliasmith
In this report we consider the following problem: Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch? In ot…
cs.LG2021★ 5 cited
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Narsimha Chilkuri, Eric Hunsberger, Aaron Voelker +2
Recent studies have demonstrated that the performance of transformers on the task of language modeling obeys a power-law relationship with model size over six orders of magnitude.…
cs.LG2021★ 12 cited
Parallelizing Legendre Memory Unit Training
Narsimha Chilkuri, Chris Eliasmith
Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. He…