Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Authors: Carles Domingo i Enrich, Youssef Mroueh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate and clarify our findings, we perform experiments of the settings studied Sec. 5, Sec. 6 and Sec. 7. We use the Re Lu activation function σ(x) = (x)+, although remark that the results of Sec. 5 hold for a generic activation function, and the results of Sec. 6 and Sec. 7 hold for non-negative integer powers of the Re Lu activation. The empirical estimates in the plots are detailed in App. G. They are averaged over 10 repetitions; the error bars show the maximum and minimum. |
| Researcher Affiliation | Collaboration | Carles Domingo-Enrich Courant Institute of Mathematical Sciences (NYU) cd2754@nyu.edu Youssef Mroueh IBM Research AI mroueh@us.ibm.com |
| Pseudocode | No | The paper provides mathematical definitions and descriptions of methods but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper constructs specific probability measures µd and νd for its experiments and uses 'samples of µd and νd' or 'a standard multivariate Gaussian and a Gaussian with unit variance', but it does not state that these are publicly available datasets or provide access information for them. |
| Dataset Splits | No | The paper describes using a certain number of samples for empirical estimates (e.g., '4400 million samples of µd and νd are used'), but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes experiments but does not specify any hardware details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions the ReLU activation function but does not provide any specific software names with version numbers that are necessary for replication. |
| Experiment Setup | No | The paper mentions the use of the ReLU activation function and the number of samples used for estimates (e.g., '4400 million samples'), but it does not provide specific hyperparameter values like learning rates, batch sizes, optimizer settings, or detailed training configurations in the main text. |