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..
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning
Authors: Thomas Miconi
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As seen in Figure 2, the system successfully evolves an architecture that can automatically acquire a novel, unseen meta-learning task (left panel, red dashed curve). |
| Researcher Affiliation | Industry | Thomas Miconi 1; 1ML Collective. Correspondence to: Thomas Miconi <EMAIL>. |
| Pseudocode | No | The paper describes the algorithms and processes verbally and with equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code is available at https://github.com/Thomas Miconi/Learning To Learn Cog Tasks |
| Open Datasets | Yes | To provide a sizeable number of computationally tractable cognitive tasks, we use the formalism of Yang et al. (2019), which implements a large number of simple cognitive tasks from the animal neuroscience literature (memory-guided saccades, comparing two successive stimuli, etc.) in a common format. |
| Dataset Splits | No | The paper discusses 'training tasks' and a 'withheld test task' but does not explicitly provide information on standard training/validation/test dataset splits with percentages or sample counts. The term 'validation' for a data split is not used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' but does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Each generation is a batch of 500 individuals. Each block is composed of 400 trials, each of which lasts 1000 ms. Following Yang et al. (2019), we use τ = 100 ms and simulation timesteps of 20 ms. Perturbations occur independently for each neuron with a probability of 0.1 at each timestep; perturbations are uniformly distributed in the [ 0.5, 0.5] range. We set τH = 1000 ms, η = 0.03. At generation 0, W is initialized with Gaussian weights with mean 0 and standard deviation 1.5/ N, where N = 70 is the number of neurons in the network... while all values of Π are initialized to 0.5. Evolution runs over 1000 generations for DMS. We feed evolutionary gradients to the Adam optimizer, with a learning rate of 0.003. |