Jun 12, 2020 · We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix, which only keeps entries that are nonzero within n steps of the recurrent ...

Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits ...

Jun 12, 2020 · We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix, which only keeps entries that are nonzero within n steps of ...

Implementation of Practical Real Time Recurrent Learning. My master thesis about practical RTRL with the Sparse n-step Approximation algorithm.

The Sparse n-step Approximation (SnAp) is introduced to the RTRL influence matrix and substantially outperforms other RTRL approximations with comparable ...

Jun 2, 2020 · For highly sparse networks, SnAp with n=2 remains tractable and can outperform backpropagation through time in terms of learning speed when ...

Jun 12, 2020 · The Sparse n-step Approximation (SnAp) is introduced to the RTRL influence matrix, which only keeps entries that are nonzero within n steps ...

Alex Graves. Publications. A Practical Sparse Approximation for Real Time Recurrent Learning (2021). Published with Wowchemy — the free, open source website ...

Oct 11, 2021 · (2020): The Sparse n-step Approximation (SnAp-n) algorithm. The core concept of the SnAp-n algorithm is the introduction of sparsity to RTRL.