Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
Authors: Bo Xie, Yingyu Liang, Le Song
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,086 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,542 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness and scalability of our algorithm on large scale synthetic and real world datasets. |
| Researcher Affiliation | Academia | Bo Xie1, Yingyu Liang2, Le Song1 1Georgia Institute of Technology EMAIL, EMAIL 2Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1: {αi}t 1 = DSGD-KPCA(P(x), k) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Molecular Space dataset contains 2.3 million molecular motifs [6]. |
| Dataset Splits | Yes | We randomly select 20% as test set and out of the remaining training set, we randomly choose 5000 as validation set to select step sizes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | In each iteration, we use a data mini-batch of size 512, and a random feature minibatch of size 128. |