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..
Emergent Symbols through Binding in External Memory
Authors: Taylor Whittington Webb, Ishan Sinha, Jonathan Cohen
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across a series of tasks, we show that this architecture displays nearly perfect generalization of learned rules to novel entities given only a limited number of training examples, and outperforms a number of other competitive neural network architectures. |
| Researcher Affiliation | Academia | Taylor W. Webb University of California Los Angeles Los Angeles, CA EMAIL Ishan Sinha, Jonathan D. Cohen Princeton University Princeton, NJ |
| Pseudocode | Yes | Algorithm 1: Emergent Symbol Binding Network. |
| Open Source Code | Yes | All code, including code for dataset generation, model implementation, training, and evaluation, is available on Git Hub. |
| Open Datasets | No | For all tasks, we employ the same set of n = 100 images, in which each image is a distinct Unicode character (the specific characters used are shown in A.7). ... All code, including code for dataset generation, model implementation, training, and evaluation, is available on Git Hub. - While code for dataset generation is provided, the paper does not provide a direct link or hosting for the generated dataset itself, only the method to create it. |
| Dataset Splits | No | The paper provides details on training and test sets but does not explicitly mention or quantify validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer and refers to publicly available code for MNM, but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | All models were trained with a batch size of 32 using the ADAM optimizer (Kingma & Ba, 2014). The learning rate for all models trained with TCN was 5e 4. ... Table 5: Learning rates for all models trained without TCN. ... Table 6: Default number of training epochs for all tasks and regimes. |