Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Authors: Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a new approximation algorithm of RTRL, Optimal Kronecker-Sum Approximation (OK). We prove that OK is optimal for a class of approximations of RTRL, which includes all approaches published so far. Additionally, we show that OK has empirically negligible noise: Unlike previous algorithms it matches TBPTT in a real world task (character-level Penn Tree Bank) and can exploit online parameter updates to outperform TBPTT in a synthetic string memorization task. Code available at Git Hub. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Zurich, Switzerland. Correspondence to: FB <EMAIL>, MMG <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 One step of unbiasedly approximating RTRL. Algorithm 2 The OK approximation Algorithm 3 Opt(C) |
| Open Source Code | Yes | Code available at Git Hub. |
| Open Datasets | Yes | The second, character-level language modeling on the Penn Tree Bank dataset (CHAR-PTB), is a complex real-world task commonly used to assess the capabilities of RNNs. |
| Dataset Splits | Yes | We split the data following (Mikolov et al., 2012). |
| Hardware Specification | No | The paper mentions network sizes (e.g., 'RHN with 128 units') but does not specify any hardware details such as CPU/GPU models, memory, or cloud computing instances used for experiments. |
| Software Dependencies | No | We optimize the log-likelihood using the Adam optimizer (Kingma & Ba, 2015) with default Tensorflow (Abadi et al., 2016) parameters, β1 = 0.9 and β2 = 0.999. |
| Experiment Setup | Yes | We use curriculum learning and start with T = 1, increasing T by one when the RNN error drops below 0.15 bits/char. After each sequence, the hidden states are reset to zero. To improve performance, the length of the sequence is sampled uniformly at random from T 5 to T. We use a RHN with 128 units and a batch size of 16. We optimize the log-likelihood using the Adam optimizer (Kingma & Ba, 2015) with default Tensorflow (Abadi et al., 2016) parameters, β1 = 0.9 and β2 = 0.999. For each model, we pick the best learning rate from {10 2.5, 10 3, 10 3.5, 10 4}. We repeat each experiment 5 times. |