High-dimensional Asymptotics of Denoising Autoencoders
Authors: Hugo Cui, Lenka Zdeborová
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further show that our results accurately capture the learning curves on a range of real data sets. (...) We show that these formulae also describe quantitatively rather well the denoising MSE for real data sets, including MNIST [15] and Fashion MNIST [16]. |
| Researcher Affiliation | Academia | Hugo Cui Statistical Physics of Computation Lab Department of Physics EPFL, Lausanne, Switzerland hugo.cui@epfl.ch Lenka Zdeborová Statistical Physics of Computation Lab Department of Physics EPFL, Lausanne, Switzerland |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code used in the present manuscript can be found in the following repository. |
| Open Datasets | Yes | We show that these formulae also describe quantitatively rather well the denoising MSE for real data sets, including MNIST [15] and Fashion MNIST [16]. (...) For each data set, samples sharing the same label were considered to belong to the same cluster. (...) For each cluster, the corresponding mean µ and covariance Σ were numerically evaluated from the empirical mean and covariance over the 6000 boots (shoes) in the Fashion MNIST training set, and the 6265 1s (7s) in the MNIST training set. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly specify a separate validation set or its split. |
| Hardware Specification | No | The paper mentions using "Pytorch implementation" for numerical simulations but does not specify any particular hardware details such as GPU/CPU models, memory, or cloud computing instances. |
| Software Dependencies | No | The paper mentions using "Pytorch implementation of full-batch Adam [43]" but does not specify version numbers for Pytorch or Python, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | Dots represent numerical simulations for d = 700, training the DAE using the Pytorch implementation of full-batch Adam, with learning rate η = 0.05 over 2000 epochs, averaged over N = 10 instances. Error bars represent one standard deviation. (...) training a DAE (p = 1, σ = tanh) trained with n = 784 training points, using the Pytorch implementation of full-batch Adam, with learning rate η = 0.05 and weight decay λ = 0.1 over 2000 epochs, averaged over N = 10 instances. |