Adji discusses the advantages of noise injection as a regularization technique for LSTMs, emphasizing its ability to reduce overfitting without decreasing the number of parameters. Through testing on benchmark datasets, he highlights how this method outperforms traditional dropout regularization, showcasing improved performance as measured by perplexity in next word prediction tasks.
