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.
A Probabilistic Approach to Neural Network Pruning
Authors: Xin Qian, Diego Klabjan
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,859 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,248 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 | In Appendix D we show some numerical results that support our theorems and assumptions. |
| Researcher Affiliation | Academia | Department of Industrial Engineering and Management Science, Northwestern University. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links about releasing their own source code. |
| Open Datasets | Yes | We train a 5-hidden-layer FCN with 1024 neurons in each hidden layer on the Covertype dataset (Blackard & Dean, 1998) by randomly selecting initial weights. |
| Dataset Splits | No | The paper mentions using the Covertype dataset but does not specify the train/validation/test splits. |
| Hardware Specification | No | The paper does not specify the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | No | The paper mentions training a 5-hidden-layer FCN with 1024 neurons in each hidden layer and randomly selecting initial weights, but it does not provide specific hyperparameters (e.g., learning rate, batch size, optimizer) or detailed training configurations. |