The power of deeper networks for expressing natural functions
Authors: David Rolnick, Max Tegmark
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results apply to standard feedforward neural networks and are borne out by empirical tests. We empirically tested Conjecture 5.2 by training ANNs to predict the product of input values x1, . . . , xn with n = 20 |
| Researcher Affiliation | Academia | David Rolnick, Max Tegmark Massachusetts Institute of Technology {drolnick, tegmark}@mit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Methods are described in prose. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper states: "Input variables xi were drawn uniformly at random from the interval [0, 2]", indicating a synthetically generated dataset rather than a publicly available one with concrete access information. |
| Dataset Splits | No | The paper mentions training ANNs and empirical tests but does not specify any dataset splits (e.g., train/validation/test percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the "Ada Delta optimizer (Zeiler, 2012)" and activation functions like "tanh(x)" and "rectified linear units (Re LUs)", but does not provide version numbers for any software libraries or frameworks used. |
| Experiment Setup | Yes | The networks were trained using the Ada Delta optimizer (Zeiler, 2012) to minimize the absolute value of the difference between the predicted and actual values. Input variables xi were drawn uniformly at random from the interval [0, 2], so that the expected value of the output would be of manageable size. |