Neurally-Guided Structure Inference
Authors: Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on two representative structure inference tasks: probabilistic matrix decomposition and symbolic program parsing. It outperforms data-driven and search-based alternatives on both tasks. ... To evaluate the accuracy of our approach, we replicate the experiments in Grosse et al. (2012), including one synthetically generated dataset and two real-world datasets: motion capture and image patches. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University, 2MIT CSAIL, 3IIIS, Tsinghua University, 4Department of Brain and Cognitive Sciences, MIT, 5Center for Brains, Minds and Machines (CBMM), MIT. |
| Pseudocode | Yes | Algorithm 1 Neurally-Guided Structure Inference Function Infer(D, Type): rule Select Rule(D, Type) for each non-terminal symbol s in rule do Cs Decompose Data(D, rule, s) Replace s in rule with Infer(Cs, s) return rule |
| Open Source Code | Yes | Project Page: http://ngsi.csail.mit.edu. |
| Open Datasets | Yes | Looking at hand-written digits from the MNIST dataset (Le Cun et al., 1998)... The dataset of human motion capture (Hsu et al., 2005; Taylor et al., 2007)... The natural image patches dataset contains samples from the Sparsenet dataset proposed in Olshausen & Field (1996). |
| Dataset Splits | No | The paper mentions generating synthetic data for training and testing generalizability on programs of different lengths/depths, but it does not specify explicit training/validation/test split percentages or sample counts. |
| Hardware Specification | Yes | We ran all experiments on a machine with an Intel Xeon E5645 CPU and a GTX 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and CNN-GRU models but does not specify version numbers for any software libraries or dependencies (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | We train the model with the Adam optimizer (Kingma & Ba, 2015). The hyperparameters for the optimizer are set to be β1 = 0.9, β2 = 0.9, α = 10 4. The model is trained for 100,000 iterations, with a batch size of 100. ... We adopt a unidirectional GRU with a hidden dimension of 256 as the code string encoder for production rule selection. We train the model using the Adam optimizer, with hyperparameters β1 = 0.9, β2 = 0.9, α = 10 4. The batch size is set to 64. |