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].
Dimension-Free Bounds for Low-Precision Training
Authors: Zheng Li, Christopher M. De Sa
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Next, we validate our theoretical results experimentally on convex problems. To do this, we analyzed how the size of the noise floor of convergence of SGD and LP-SGD varies as the dimension is changed for a class of synthetic problems. |
| Researcher Affiliation | Academia | Zheng Li IIIS, Tsinghua University EMAIL Christopher De Sa Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 LP-SGD: Low-Precision Stochastic Gradient Descent |
| Open Source Code | No | The paper does not contain any statement about making the source code for the described methodology publicly available, nor does it provide any links to a code repository. |
| Open Datasets | Yes | Second, we ran LP-SGD on the MNIST dataset [10]. |
| Dataset Splits | No | The paper mentions using a synthetic dataset and the MNIST dataset, and describes some experimental parameters and running for 500 epochs, but does not explicitly provide information on train/validation/test splits (e.g., percentages, counts, or specific split files) for either dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We set α = 0.01, β = 0.2, p1 = 0.9, pd = 0.001, and s = 16, we chose each entry of w uniformly from [-1/2, 1/2], and we set δ such that the low-precision numbers would range from -1 to 1. |