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 [1].
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Authors: Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore these properties in a range of standard non-convex test functions and by training a Res Net architecture for a classification task over CIFAR. 5 Numerical experiments As an illustration of our theoretical analysis, we plot in Fig. 1a the convergence rate of (SGD) in the standard Shekel risk benchmark function... We demonstrate the benefits of this cooldown heuristic in a standard Res Net18 architecture for a classification task over CIFAR10. |
| Researcher Affiliation | Collaboration | Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG & Criteo AI Lab EMAIL Nadav Hallak Technion EMAIL Ali Kavis École Polytechnique Fédérale de Lausanne (EPFL) EMAIL Volkan Cevher École Polytechnique Fédérale de Lausanne (EPFL) EMAIL |
| Pseudocode | No | The paper presents the SGD algorithm as a mathematical formula: Xn+1 = Xn γn Vn. (SGD) but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any statements about providing open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We explore these properties in a range of standard non-convex test functions and by training a Res Net architecture for a classification task over CIFAR. We demonstrate the benefits of this cooldown heuristic in a standard Res Net18 architecture for a classification task over CIFAR10. |
| Dataset Splits | No | The paper mentions using CIFAR10 and training a Res Net architecture, but it does not provide specific details about the training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | For our experiments, we ran N = 103 instances of (SGD) with a constant, 1/ n, and 1/n step-size schedule... we ran (SGD) with a constant step-size for 100 epochs, with checkpoints at different cutoffs; then, at each checkpoint, we launched the cooldown period with step-size 1/n. |