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..
Scalable Adaptive Stochastic Optimization Using Random Projections
Authors: Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, We compare the performance of our proposed algorithms against both the diagonal and full-matrix ADAGRAD variants in the idealised setting where the data is dense but has low effective rank. Figure 2: Comparison of training loss (top row) and test accuracy (bottom row) on (a) MNIST, (b) CIFAR and (c) SVHN. |
| Researcher Affiliation | Collaboration | Institute for Machine Learning, Department of Computer Science, ETH Zürich, Switzerland Seminar for Statistics, Department of Mathematics, ETH Zürich, Switzerland Disney Research, Zürich, Switzerland |
| Pseudocode | Yes | Algorithm 1 ADA-LR, Algorithm 2 RADAGRAD |
| Open Source Code | No | The paper does not provide explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MNIST, CIFAR-10 and SVHN datasets. We trained and evaluated our network on the Penn Treebank dataset [25]. |
| Dataset Splits | No | For each algorithm learning rates are tuned using cross validation. Step sizes were determined by coarsely searching a log scale of possible values and evaluating performance on a validation set. (Explanation: While validation and cross-validation are mentioned, specific split percentages, sample counts, or explicit methodologies for creating these splits are not provided in the text.) |
| Hardware Specification | No | The paper mentions general use of GPUs but does not provide specific hardware details such as GPU/CPU models, memory specifications, or detailed computer configurations used for the experiments. |
| Software Dependencies | No | The paper mentions using the 'FFTW package' but does not specify its version number or any other software dependencies with explicit version information. |
| Experiment Setup | Yes | We used a batch size of 8 and trained the networks without momentum or weight decay, in order to eliminate confounding factors. Instead, we used dropout regularization (p = 0.5) in the dense layers during training. Step sizes were determined by coarsely searching a log scale of possible values and evaluating performance on a validation set. The memory size of the T-LSTM units was set to 256. |