Efficiently testing local optimality and escaping saddles for ReLU networks
Authors: Chulhee Yun, Suvrit Sra, Ali Jadbabaie
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | 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 {chulheey,suvrit,jadbabai}@mit.edu |
| 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). |