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..
On the Quality of the Initial Basin in Overspecified Neural Networks
Authors: Itay Safran, Ohad Shamir
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the geometric structure of the associated non-convex objective function, in the context of Re LU networks and starting from a random initialization of the network parameters. Before continuing, we emphasize that our observations are purely geometric in nature, independent of any particular optimization procedure. |
| Researcher Affiliation | Academia | Itay Safran EMAIL Ohad Shamir EMAIL Weizmann Institute of Science, Israel |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code. |
| Open Datasets | No | The paper refers to 'data' or 'training data' using generic symbols like S = (xt, yt)m t=1, but does not provide any concrete access information, such as specific names of public datasets, links, DOIs, or formal citations. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values or training configurations. |