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 [1].
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. |