Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficiently testing local optimality and escaping saddles for ReLU networks
Authors: Chulhee Yun, Suvrit Sra, Ali Jadbabaie
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For experiments, we used artificial datasets sampled iid from standard normal distribution, and trained 1-hidden-layer Re LU networks with squared error loss. ... Table 1 summarizes the results. |
| Researcher Affiliation | Academia | Chulhee Yun, Suvrit Sra & Ali Jadbabaie Massachusetts Institute of Technology Cambridge, MA 02139, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 SOSP-CHECK (Rough pseudocode); Algorithm 2 SOSP-CHECK; Algorithm 3 FO-SUBDIFF-ZERO-TEST; Algorithm 4 FO-INCREASING-TEST; Algorithm 5 SO-TEST |
| Open Source Code | No | The paper does not provide any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | For experiments, we used artificial datasets sampled iid from standard normal distribution |
| Dataset Splits | No | The paper mentions using artificial datasets and training but does not specify train, validation, or test splits by percentages, counts, or by referencing standard splits. |
| Hardware Specification | No | The paper describes the experimental setup but does not provide any specific hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using Adam (Kingma & Ba, 2014) and L-BFGS-B (Byrd et al., 1995) but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | we ran Adam (Kingma & Ba, 2014) using full-batch (exact) gradient for 200,000 iterations and decaying step size (start with 10 3, 0.2 decay every 20,000 iterations). |