The Expressive Power of Neural Networks: A View from the Width
Authors: Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further conduct extensive experiments to provide some insights about the upper bound of such an approximation. To this end, we study a series of network architectures with varied width. For each network architecture, we randomly sample the parameters... The approximation error is empirically calculated... Table 1 lists the results. |
| Researcher Affiliation | Academia | Zhou Lu1,3 1400010739@pku.edu.cn Hongming Pu1 1400010621@pku.edu.cn Feicheng Wang1,3 1400010604@pku.edu.cn Zhiqiang Hu2 huzq@pku.edu.cn Liwei Wang2,3 wanglw@cis.pku.edu.cn 1, Department of Mathematics, Peking University 2, Key Laboratory of Machine Perception, MOE, School of EECS, Peking University 3, Center for Data Science, Peking University, Beijing Institute of Big Data Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It refers to a figure and describes a construction informally, but no formal algorithm steps. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no explicit statements about releasing code, nor are there any repository links. |
| Open Datasets | No | The paper describes generating its own 'uniformly placed inputs' and sampling parameters, but it does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper states, 'half of all the test inputs from [ 1, 1)n and the corresponding values evaluated by target function constitute the training set.' This describes a training split, but it does not mention a distinct validation set or specify a comprehensive train/validation/test split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'mini-batch Ada Delta optimizer' but does not provide specific version numbers for any software components, which is required for reproducibility. |
| Experiment Setup | Yes | The training set is used to train approximator network with a mini-batch Ada Delta optimizer and learning rate 1.0. The parameters of approximator network are randomly initialized according to [8]. The training process proceeds 100 epoches for n = 1 and 200 epoches for n = 2; the best approximator function is recorded. |