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..
Hardness of Learning Neural Networks under the Manifold Hypothesis
Authors: Bobak Kiani, Jason Wang, Melanie Weber
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Looking forward, we comment on and empirically explore intermediate regimes of manifolds, which have heterogeneous features commonly found in real world data.4 |
| Researcher Affiliation | Academia | John A. Paulson School of Engineering and Applied Sciences, Harvard University; e-mail: EMAIL. Harvard College, Harvard University; e-mail: EMAIL. John A. Paulson School of Engineering and Applied Sciences, Harvard University; e-mail: EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks with explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | Code is made public at https://github.com/Weber-Geo ML/manifold-learning-complexity |
| Open Datasets | Yes | We investigate synthetic unit hyperspheres to sanity check our method and proceed to three realworld image datasets: MNIST [35], Fashion MNIST (FMNIST) [95], and Kuzushiji-MNIST (KMNIST) [29]. |
| Dataset Splits | No | The paper mentions 'a training set of size 1000' and '10000 samples' but does not specify explicit training/validation/test splits with percentages or counts for reproduction. |
| Hardware Specification | Yes | This training was done on an NVIDIA L4 24GB GPU. |
| Software Dependencies | No | We use the Pytorch package for our neural network experiments [73]. No specific version number for PyTorch or other software dependencies is provided. |
| Experiment Setup | Yes | For Euclidean datasets, we train a fully-connected 5-layer Re LU network of width 2048 on 100000 samples for 30 epochs, batch size 64, and a learning rate of 4 × 10−4. |