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].
Modular Continual Learning in a Unified Visual Environment
Authors: Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In 3 we describe and evaluate comparative performance of multiple Re Ma P module architectures on a variety of Touch Stream tasks. We compared each architecture across 12 variants of visual SR, MTS, and localization tasks, using fixed visual encoding features from layer FC6 of VGG-16. |
| Researcher Affiliation | Academia | Kevin T. Feigelis Department of Physics Stanford Neurosciences Institute Stanford University Stanford, CA 94305 EMAIL Blue Sheffer Department of Psychology Stanford University Stanford, CA 94305 EMAIL Daniel L. K. Yamins Departments of Psychology and Computer Science Stanford Neurosciences Institute Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | Yes | Algorithm 1: Re Ma P Reward Map Prediction |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Although we work with modern large-scale computer vision-style datasets and tasks in this work, e.g. Image Net (Deng et al. (2009)) and MS-COCO (Lin et al. (2014)). |
| Dataset Splits | No | Each class has 1300 unique training instances, and 50 unique validation instances." The paper mentions 'cross-validated fashion' for learning rates but does not provide overall training/validation/test dataset splits for reproducibility beyond the number of validation instances for one dataset. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU model, CPU type, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions algorithms (e.g., ADAM) and network architectures (e.g., VGG-16) and activation functions (e.g., Re LU, CRe LU) but does not specify versions for any key software components or libraries used for implementation. |
| Experiment Setup | Yes | Module weights were initialized using a normal distribution with µ = 0.0, σ = 0.01, and optimized using the ADAM algorithm (Kingma & Ba (2014)) with parameters β1 = 0.9, β2 = 0.999 and ϵ = 1e 8. Learning rates were optimized on a per-task, per-architecture basis in a cross-validated fashion. Values used in the present study may be seen in Table S2. |