Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
Authors: Evan Markou, Thalaiyasingam Ajanthan, Stephen Gould
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on synthetic and real-world architectures for classification tasks demonstrate that our approach accelerates convergence and enhances training stability. ... In our experiments, we perform feature normalisation onto a hypersphere... Our method underwent rigorous evaluation across various UFM sizes and real model architectures trained on actual datasets, including CIFAR10 [37], CIFAR100 [37], STL10 [14], and Image Net1000 [15], implemented on Res Net [29] and VGG [56] architectures. |
| Researcher Affiliation | Collaboration | Evan Markou Australian National University EMAIL Thalaiyasingam Ajanthan Australian National University & Amazon EMAIL Stephen Gould Australian National University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/evanmarkou/Guiding-Neural-Collapse.git. |
| Open Datasets | Yes | Our method underwent rigorous evaluation across various UFM sizes and real model architectures trained on actual datasets, including CIFAR10 [37], CIFAR100 [37], STL10 [14], and Image Net1000 [15], implemented on Res Net [29] and VGG [56] architectures. |
| Dataset Splits | No | Our experiments on real datasets run for 200 epochs with batch size 256; for the UFM analysis, we run 2000 iterations. ... Numerical results for the top-1 train and test accuracy are reported in Tables 1 and 2, respectively. |
| Hardware Specification | Yes | All experiments were conducted using Nvidia RTX3090 and A100 GPUs. |
| Software Dependencies | No | We solve the Riemannian optimisation problem defined in Equation 7 using a Riemannian Trust-Region method [1] from py Manopt [60]. Following the authors recommendation, we set the gain/momentum parameter to 10 to expedite convergence, aligning it with other widely used optimisers like Adam [36] and SGD. |
| Experiment Setup | Yes | Our experiments on real datasets run for 200 epochs with batch size 256; for the UFM analysis, we run 2000 iterations. ... We maintain a proximal coefficient δ set to 10 3 consistently across all experiments. ... Specifically, we set α = 2/(T + 1), where T represents the number of iterations. Additionally, we include a thresholding value of 10 4... Finally, in our experiments, we set the temperature parameter τ to five. |