Unsupervised Learning by Program Synthesis
Authors: Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examples and that it can recover some English inflectional morphology. |
| Researcher Affiliation | Academia | Kevin Ellis Department of Brain and Cognitive Sciences Massachusetts Institute of Technology ellisk@mit.edu Armando Solar-Lezama MIT CSAIL Massachusetts Institute of Technology asolar@csail.mit.edu Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology jbt@mit.edu |
| Pseudocode | Yes | Algorithm 1 SMT encoding of programs generated by production P of grammar G |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We took five inflected forms of the top 5000 lexemes as measured by token frequency in the CELEX lexical inventory [15]." and "We take our visual concepts from the Synthetic Visual Reasoning Test (SVRT), a set of visual classification problems... |
| Dataset Splits | No | The paper states "We split this in half to give 2500 lexemes for training and testing" but does not explicitly provide details about a validation dataset split or a specific cross-validation setup for the general model training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, number of cores) used for running the experiments. |
| Software Dependencies | No | The paper mentions software tools like "Z3" and "Morfessor" but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | Then, the x and y coordinates of each shape are perturbed by additive noise drawn uniformly from δ to δ; in our experiments, we put δ = 3." and "In our experiments, we put ϵ = 0.1." and "Concretely, we sampled many subsets of the data, each with 4, 5, 6, or 7 lexemes (thus 20, 25, 30, or 35 words)". |