RECURRENT NEURAL NETWORK REGULARIZATION cover art

RECURRENT NEURAL NETWORK REGULARIZATION

RECURRENT NEURAL NETWORK REGULARIZATION

Listen for free

View show details

About this listen

This episode breaks down the 'RECURRENT NEURAL NETWORK REGULARIZATION' research paper, which investigates how to correctly apply a regularization technique called dropout to Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. The authors argue that dropout, while effective in traditional neural networks, has limitations in RNNs. They propose a modified implementation of dropout specifically for RNNs and LSTMs, which significantly reduces overfitting across various tasks such as language modelling, speech recognition, machine translation, and image caption generation. The paper provides a detailed explanation of the proposed technique, its effectiveness through experimental results, and comparisons with existing approaches.

Audio : (Spotify) https://open.spotify.com/episode/51KtuybPXYBNu7sfVPWFZK?si=T_GBETMHTAK8rFOZ_lr4oQ

Paper: https://arxiv.org/abs/1409.2329v5
No reviews yet
In the spirit of reconciliation, Audible acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.