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..
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Authors: Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1 we demonstrate this empirically for a linearly separable data set (from a subset of MNIST) learned using over-parameterized networks. |
| Researcher Affiliation | Academia | Alon Brutzkus & Amir Globerson The Blavatnik School of Computer Science Tel Aviv University, Israel EMAIL,EMAIL Eran Malach & Shai Shalev-Shwartz School of Computer Science The Hebrew University, Israel EMAIL,EMAIL |
| Pseudocode | No | The paper describes the SGD update rule mathematically (Eq. 3) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | No | The linearly separable data set consists of 4000 MNIST images with digits 3 and 5, each of dimension 784. |
| Dataset Splits | No | The size of the training set is 3000 and the remaining 1000 points form the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The setting of Section 5 is implemented (e.g., SGD with batch of size 1, only first layer is trained, Leaky Re LU activations) and SGD is initialized according to the initialization defined in Eq. 6. |