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.
Compositional Curvature Bounds for Deep Neural Networks
Authors: Taha Entesari, Sina Sharifi, Mahyar Fazlyab
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,294 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,316 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 | Finally, we demonstrate the efficacy of our method on classification tasks using the MNIST and CIFAR-10 datasets. |
| Researcher Affiliation | Academia | Taha Entesari * 1 Sina Sharifi * 1 Mahyar Fazlyab 1 1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, United States of America. Correspondence to: Mahyar Fazlyab <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Compositional Curvature Estimation of Neural Networks |
| Open Source Code | Yes | Our code is available at https://github.com/o4lc/Compositional Curvature-Bounds-for-DNNs. |
| Open Datasets | Yes | Finally, we demonstrate the efficacy of our method on classification tasks using the MNIST and CIFAR-10 datasets. |
| Dataset Splits | No | The paper mentions training models on MNIST and CIFAR-10 and evaluating on test data, but it does not explicitly specify the proportions for training, validation, and test splits, nor does it explicitly state the use of standard splits for these datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper describes implementation details and training strategies, but it does not specify software dependencies (e.g., libraries, frameworks) along with their version numbers. |
| Experiment Setup | Yes | For training on CIFAR-10, we choose (Ξ·, Ο΅, Ξ»min) = (0.05, 0.6, 0.01). The rest of the training details are as follows. We use the modified cross-entropy loss function from (Prach & Lampert, 2022), ... We use Ο = 0.25. Furthermore, we train our models for 1000 epochs with a batch size of 256 with a cosine annealing strategy with an initial learning rate of 10 4 and a final learning rate 10 5, and report the average results on two seed in Table 1. |