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].
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
Authors: Reuben Feinman, Brenden M. Lake
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our model to the Omniglot challenge of human-level concept learning, using a background set of alphabets to learn an expressive prior distribution over character drawings. In a subsequent evaluation, our GNS model uses probabilistic inference to learn rich conceptual representations from a single training image that generalize to 4 unique tasks, succeeding where previous work has fallen short. |
| Researcher Affiliation | Academia | Reuben Feinman & Brenden M. Lake New York University EMAIL |
| Pseudocode | Yes | The psuedo-code of this sampling procedure is provided in Fig. A7. |
| Open Source Code | Yes | Concept learning experiments can be reproduced using our pre-trained generative model and source code: https://github.com/rfeinman/GNS-Modeling. |
| Open Datasets | Yes | We apply our model to the Omniglot challenge of human-level concept learning, using a background set of alphabets to learn an expressive prior distribution over character drawings. |
| Dataset Splits | No | The paper uses the Omniglot dataset and mentions training on the "background set" and evaluating on "held-out alphabets in the Omniglot evaluation set," but it does not explicitly provide specific percentages or counts for training, validation, and test splits used in its experiments, nor does it refer to a predefined split by citation for its specific usage. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU or CPU models, memory). |
| Software Dependencies | No | The paper does not specify specific software versions for its dependencies (e.g., "Python 3.8, PyTorch 1.9"). |
| Experiment Setup | No | The paper mentions a temperature parameter (T=0.5) for sampling at test time and general training objectives, but it lacks specific hyperparameters (e.g., learning rate, batch size, number of epochs) for training the neural networks. |