Hardness of Learning Neural Networks under the Manifold Hypothesis
Authors: Bobak Kiani, Jason Wang, Melanie Weber
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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: bkiani@g.harvard.edu. Harvard College, Harvard University; e-mail: jasonwang1@college.harvard.edu. John A. Paulson School of Engineering and Applied Sciences, Harvard University; e-mail: mweber@g.harvard.edu |
| 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. |