Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
Authors: Forough Arabshahi, Sameer Singh, Animashree Anandkumar
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. 4 EXPERIMENTS AND RESULTS |
| Researcher Affiliation | Academia | Forough Arabshahi University of California Irvine, CA farabsha@uci.edu Sameer Singh University of California Irvine, CA sameer@uci.edu Animashree Anandkumar California Institute of Technology Pasadena, CA anima@caltech.edu |
| Pseudocode | No | The paper describes methods and processes but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our dataset generation method, proposed model, and data is available here: https://github.com/ Forough A/neural Math |
| Open Datasets | Yes | Our dataset generation method, proposed model, and data is available here: https://github.com/ Forough A/neural Math |
| Dataset Splits | No | The paper states: 'In this experiment we randomly split all of the generated data that includes equations of depths 1 to 4 into train and test partitions with an 80%/20% split ratio.' It specifies train and test splits, but does not explicitly mention a separate validation set split or its proportion. |
| Hardware Specification | No | The paper mentions thanking Amazon Inc. for AWS credits, implying cloud usage, but does not provide specific hardware details such as GPU/CPU models or instance types used for experiments. |
| Software Dependencies | No | The paper mentions software like Mx Net, Sympy, and Adam optimizer, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We use L2-regularization as well as dropout to avoid overfitting, and train all the models for 100 epochs. We have tuned for the hidden dimension {10,20,50}, the optimizers {SGD, NAG (Nesterov accelerated SGD), RMSProp, Adam, Ada Grad, Ada Delta, DCASGD, SGLD (Stochastic Gradient Riemannian Langevin Dynamics)}, dropout rate {0.2,0.3}, learning rate {10 3, 10 5}, regularization ratio {10 4, 10 5} and momentum {0.2,0.7}. Most of the networks achieved their best performance using Adam optimizer Kingma & Ba (2014) with a learning rate of 0.001 and a regularization ratio of 10 5. Hidden dimension and dropout varies under each of the scenarios. |