Generalization in multitask deep neural classifiers: a statistical physics approach
Authors: Anthony Ndirango, Tyler Lee
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We discuss the validity of our theoretical results in comparison to a comprehensive suite of numerical experiments. |
| Researcher Affiliation | Industry | Tyler Lee Intel AI Lab tyler.p.lee@intel.com Anthony Ndirango Intel AI Lab anthony.ndirango@intel.com |
| Pseudocode | No | The paper describes mathematical derivations and processes but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | Code supporting this paper is available upon request |
| Open Datasets | No | The paper describes a 'student-teacher setup' where data is generated by a teacher network, and does not mention or provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper mentions 'validation error' in Figure 1 caption, implying a validation set was used, but does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper acknowledges 'the compute infrastructure that made the empirical portions of this work possible' but does not provide specific hardware details like CPU/GPU models or types of accelerators. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | 1/ = 10 3 is the learning rate |