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.
Extended Unconstrained Features Model for Exploring Deep Neural Collapse
Authors: Tom Tirer, Joan Bruna
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 31,798 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,767 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 empirically demonstrate the usefulness of our nonlinear extended UFM in modeling the NC phenomenon that occurs with practical networks. |
| Researcher Affiliation | Academia | 1Center for Data Science, New York University, New York 2Courant Institute of Mathematical Sciences, New York University, New York. Correspondence to: Tom Tirer <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Finally, we show the similarity of the NC metrics that are obtained for the nonlinear extended UFM in Figure 4 (rather than those in Figure 3) and metrics obtained by a practical well-trained DNN, namely Res Net18 (He et al., 2016) (composed of 4 Res Blocks), trained on MNIST with SGD with learning rate 0.05 (divided by 10 every 40 epochs) and weight decay (L2 regularization) of 5e-4. Figure 5 shows the results for two cases: 1) MSE loss without bias in the FC layer; and 2) the widely-used setting, with cross-entropy loss and bias. (Additional experiments with CIFAR10 dataset appear in Appendix G). |
| Dataset Splits | No | The paper mentions training on MNIST and CIFAR10 but does not specify the train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions Res Net18 but does not provide specific software dependencies with version numbers (e.g., library names, framework versions). |
| Experiment Setup | Yes | Figure 1 corroborates Theorem 3.1 for K = 4, d = 20, n = 50 and λW = λH = 0.005 (no bias is used, equivalently λb ). Both W and H are initialized with standard normal distribution and are optimized with plain gradient descent with step-size 0.1. ... trained on MNIST with SGD with learning rate 0.05 (divided by 10 every 40 epochs) and weight decay (L2 regularization) of 5e-4. |