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 [1].
On the Expressivity of Recurrent Neural Cascades
Authors: Nadezda Alexandrovna Knorozova, Alessandro Ronca
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new insights into this question. We show that the regular languages captured by RNCs with sign and tanh activation with positive recurrent weights are the star-free regular languages. In order to establish our results we develop a novel framework where capabilities of RNCs are assessed by analysing which semigroups and groups a single neuron is able to implement. A notable implication of our framework is that RNCs can achieve the expressivity of all regular languages by introducing neurons that can implement groups. |
| Researcher Affiliation | Collaboration | Nadezda Alexandrovna Knorozova1, Alessandro Ronca2 1Relational AI 2University of Oxford EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not discuss datasets for training or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies or their versions. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |