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Found 3 papers, 1 papers with code
Debugging using Orthogonal Gradient Descent
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?
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Over three orders of magnitude, we show that our new architecture attains the same accuracy as transformers with 10x fewer tokens.
Parallelizing Legendre Memory Unit Training
For instance, our LMU sets a new state-of-the-art result on psMNIST, and uses half the parameters while outperforming DistilBERT and LSTM models on IMDB sentiment analysis.
Ranked #6 on
Sequential Image Classification
on Sequential MNIST
