ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
Authors: Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D Rao
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results of the A-Res NEst model are deferred to Appendix B in the supplementary material. Empirical results of the Res NEst model are deferred to Appendix B in the supplementary material. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, 2Qualcomm Institute University of California, San Diego La Jolla, CA 92093, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about making the source code available or provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper refers to empirical results in an appendix but does not specify any datasets used for these results in the main text, nor does it provide access information (links, citations) for any public dataset. |
| Dataset Splits | No | The paper does not provide any specific dataset split information (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |