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..
On Dropout and Nuclear Norm Regularization
Authors: Poorya Mianjy, Raman Arora
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical ๏ฌndings with empirical results. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | 1The code for the experiments can be found at: https://github.com/r3831/dln_dropout |
| Open Datasets | No | The training data {xi} is sampled from a standard Gaussian distribution which in particular ensures that C = I. The labels {yi} are generated as yi Nxi, where N Rdk+1 d0. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits. It mentions training data but not its partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or TensorFlow with their versions). |
| Experiment Setup | Yes | At each step of the dropout training, we use a minibatch of size 1000 to train the network. The learning rate is tuned over the set {1, 0.1, 0.01}. All experiments are repeated 50 times, the curves correspond to the average of the runs, and the grey region shows the standard deviation. |