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..
Continual Learning Through Synaptic Intelligence
Authors: Friedemann Zenke, Ben Poole, Surya Ganguli
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments We evaluated our approach for continual learning on the split and permuted MNIST (Le Cun et al., 1998; Goodfellow et al., 2013), and split versions of CIFAR-10 and CIFAR-100 (Krizhevsky & Hinton, 2009). |
| Researcher Affiliation | Academia | 1Stanford University. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link providing access to source code for the methodology was found. |
| Open Datasets | Yes | We evaluated our approach for continual learning on the split and permuted MNIST (Le Cun et al., 1998; Goodfellow et al., 2013), and split versions of CIFAR-10 and CIFAR-100 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | Yes | However, here we used ξ = 0.1 and the value for c = 0.1 was determined via a coarse grid search on a heldout validation set. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2014)' but does not specify software versions for programming languages, libraries, or other dependencies. |
| Experiment Setup | Yes | We used a small multi-layer perceptron (MLP) with only two hidden layers consisting of 256 units each with Re LU nonlinearities, and a standard categorical cross-entropy loss function plus our consolidation cost term (with damping parameter ξ = 1 10 3). To avoid the complication of crosstalk between digits at the readout layer due to changes in the label distribution during training, we used a multi-head approach in which the categorical cross entropy loss at the readout layer was computed only for the digits present in the current task. Finally, we optimized our network using a minibatch size of 64 and trained for 10 epochs. To achieve good absolute performance with a smaller number of epochs we used the adaptive optimizer Adam (η = 1 10 3, β1 = 0.9, β2 = 0.999). |