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..
Understanding Representation Learnability of Nonlinear Self-Supervised Learning
Authors: Ruofeng Yang, Xiangyuan Li, Bo Jiang, Shuai Li
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present the learning processes and results of the nonlinear SSL and SL model via simulation experiments. |
| Researcher Affiliation | Academia | Shanghai Jiao Tong University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes methods and proofs in prose and mathematical notation. |
| Open Source Code | Yes | The codes of this section are available at https://github.com/wanshuiyin/AAAI-2023-The Learnability-of-Nonlinear-SSL. |
| Open Datasets | No | The paper designs a "toy data distribution" (Section 3.1) for its experiments and does not use a publicly available dataset with a specific link, DOI, repository name, or formal citation. |
| Dataset Splits | No | The paper describes the construction of its custom data distribution but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments are conduct on a desktop with AMD Ryzen 7 5800H with Radeon Graphics 3.20 GHz and 16 GB memory. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific Python libraries like PyTorch or TensorFlow versions) used for the experiments. |
| Experiment Setup | Yes | In this section, we choose τ = 7, d = 10, ρ = 1/d1.5, α = 1/800, n = d2 and learning rate η = 0.001 if we do not specify otherwise. Experiments are averaged over 20 random seeds, and we show the average results with 95% confidence interval for learning curves. |