Deep, Skinny Neural Networks are not Universal Approximators
Authors: Jesse Johnson
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proof of Theorem 1 consists of two steps... In the first step, described in Section 5, we examine the family of functions defined by deep, skinny neural networks... We present experimental results that demonstrate the constraints in Section 7... To demonstrate the effect of Theorem 1, we used the Tensor Flow Neural Network Playground (1) to train two different networks on a standard synthetic dataset with one class centered at the origin of the two-dimensional plane, and the other class forming a ring around it. |
| Researcher Affiliation | Academia | Jesse Johnson Sanofi jejo.math@gmail.com. Only a personal email address is provided, so a clear institutional affiliation cannot be determined. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions using |
| Open Datasets | No | The paper mentions using a |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | The experimental setup describes the neural network architectures used: 'The first network has six two-dimensional hidden layers...' and 'The second network has a single hidden layer of dimension three...' |