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].
Implicit Regularization Paths of Weighted Neural Representations
Authors: Jin-Hong Du, Pratik Patil
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a practical consequence of the path equivalences, we develop an efficient cross-validation method for tuning and apply it to subsampled pretrained representations across several models (e.g., Res Net-50) and datasets (e.g., CIFAR-100). |
| Researcher Affiliation | Academia | Jin-Hong Du Carnegie Mellon University EMAIL Pratik Patil University of California Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta-algorithm for tuning of ensemble sizes and subsample matrices. |
| Open Source Code | Yes | The code for reproducing the results of this paper can be found at https://jaydu1.github.io/overparameterized-ensembling/weighted-neural. |
| Open Datasets | Yes | We consider Res Net-{18, 34, 50, 101} applied to the CIFAR-{10,100} [9], Fashion-MNIST [21], Flowers-102 [14], and Food-101 [4] datasets. |
| Dataset Splits | Yes | For datasets with different data aspect ratios, we stratify 10% of the training samples as the training set for the CIFAR-100 dataset. The training and predicting errors are the mean square errors on the training and test sets, respectively, aggregated over all the labels. |
| Hardware Specification | No | The paper mentions 'the ACCESS allocation MTH230020 provided for some of the experiments performed on the Bridges2 system at the Pittsburgh Supercomputing Center' but does not provide specific hardware details like GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions 'The improved CV method is implemented in the Python library [24]' but does not provide specific version numbers for Python or other software dependencies. |
| Experiment Setup | Yes | The risk estimates are computed based on M0 = 25 base estimators using Algorithm 1 with λ = 10 3. |