Efficient Representation of Low-Dimensional Manifolds using Deep Networks
Authors: Ronen Basri, David W. Jacobs
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that training with stochastic gradient descent can indeed find efficient representations similar to the one presented in this paper. |
| Researcher Affiliation | Academia | Ronen Basri Dept. of Computer Science and Applied Math Weizmann Institute of Science Rehovot, 76100 Israel ronen.basri@weizmann.co.il David W. Jacobs Dept. of Computer Science University of Maryland College Park, MD djacobs@cs.umd.edu |
| Pseudocode | No | The paper describes the construction and analysis in prose and mathematical notation but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link, explicit code release statement, or code in supplementary materials. |
| Open Datasets | No | The paper mentions generating synthetic data and using face images, but it does not provide concrete access information (specific link, DOI, repository name, formal citation, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions using 'validation points' and 'validation error', but it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions training models but does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions training with 'stochastic gradient descent' but does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |