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..
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Authors: Haowei He, Gao Huang, Yang Yuan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive empirical experiments on both modern deep networks and simple 2 layer networks are conducted to validate our assumptions and analyze the intriguing properties of asymmetric valleys. |
| Researcher Affiliation | Academia | 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Department of Automation, Tsinghua University 3Beijing National Research Center for Information Science and Technology (BNRist) |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/962086838/code-for-Asymmetric-Valley |
| Open Datasets | Yes | We perform experiments with widely used deep networks, i.e.,Res Net-56, Res Net-110, Res Net-164 [19], VGG-16 [45] and Dense Net-100 [23], on the CIFAR-10, CIFAR-100, SVHN and STL-10 image classification datasets. |
| Dataset Splits | No | The paper mentions using standard datasets like CIFAR-10 and CIFAR-100, but it does not explicitly provide specific percentages, sample counts, or refer to predefined validation splits for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions). |
| Experiment Setup | No | The paper states, 'Specifically, we run the SWA algorithm (with deceasing learning rate) with popular deep networks...following the configurations in [25].' While this refers to an experimental setup, it defers the specific details to a prior work [25] rather than providing them directly in the paper, which does not meet the criteria for 'Yes'. |