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..
Loss Landscape Characterization of Neural Networks without Over-Parametrization
Authors: Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurelien Lucchi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate the soundness of our new function class through both theoretical analysis and empirical experimentation across a diverse range of deep learning models. and 5 Experimental validation of the α-β-condition |
| Researcher Affiliation | Academia | Rustem Islamov1 Niccoló Ajroldi2 Antonio Orvieto2,3,4 Aurelien Lucchi1 1University of Basel 2Max Planck Institute for Intelligent Systems 3 ELLIS Institute Tübingen 4Tübingen AI Center |
| Pseudocode | Yes | Algorithm 1 SGD with constant stepsize, Algorithm 2 SPSmax: Stochastic Polyak Stepsize, Algorithm 3 NGN: Non-negative Gauss Newton |
| Open Source Code | No | The paper refers to using and modifying existing open-source code but does not explicitly state that its own modified code or new code for this work is provided as open-source. |
| Open Datasets | Yes | Fashion-MNIST [83] dataset., CIFAR10 dataset [41], CIFAR100 [41], Criteo 1TB dataset [44], Fast MRI dataset [85], OGBG dataset [31], WMT dataset [9], Pythia models [8], Slim Pajama [72] dataset. |
| Dataset Splits | No | The paper refers to using standard datasets but does not explicitly provide the training, validation, or test dataset splits (e.g., percentages or counts) or cite a specific split methodology used for reproducibility. |
| Hardware Specification | Yes | LSTM, MLP, CNN and Resnet experiments are performed using one NVIDIA Ge Force RTX 3090 GPU with a memory of 24 GB. For training Algoperf and Pythia language models, we resort instead to 4x A100-SXM4 GPUs, with a memory of 40 GB each, and employ data parallelism for efficient distributed training. |
| Software Dependencies | No | The paper mentions using 'Py Torch [65] package' but does not provide specific version numbers for it or any other key software dependencies required for replication. |
| Experiment Setup | Yes | We train the model in all cases with fixed learning rate 0.09 for 1500 epochs and batch size 64 on Fashion-MNIST [83] dataset. and Table 3: Training details of large models from Appendix D.8 and Appendix D.9 |