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.
L2-Nonexpansive Neural Networks
Authors: Haifeng Qian, Mark N. Wegman
ICLR 2019 | Venue PDF | LLM Run Details | Input Tokens: 22,919 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,157 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 | Experiments are divided into three groups to study different properties of L2NNNs. |
| Researcher Affiliation | Industry | Haifeng Qian & Mark N. Wegman IBM Research Yorktown Heights, NY 10598, USA qianhaifeng,EMAIL |
| Pseudocode | No | Not found. The paper describes its methods in prose and mathematical formulas, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our MNIST and CIFAR-10 classifiers are available at http://researcher.watson.ibm.com/group/9298 |
| Open Datasets | Yes | With MNIST and CIFAR-10 classifiers |
| Dataset Splits | Yes | In early-stopping runs, 5000 training images are withheld as validation set and training stops when loss on validation set stops decreasing. |
| Hardware Specification | No | Not found. The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | Not found. The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We use a loss function with three terms, with trade-off hyperparameters γ and ω: L = La + γ Lb + ω Lc (3) |