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].
An Information-Geometric Distance on the Space of Tasks
Authors: Yansong Gao, Pratik Chaudhari
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform thorough empirical validation and analysis across diverse image classification datasets to show that the coupled transfer distance correlates strongly with the difficulty of fine-tuning. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Computational Science, University of Pennsylvania 2Department of Electrical and Systems Engineering, University of Pennsylvania. |
| Pseudocode | No | The paper describes the algorithm using equations and text, but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We perform thorough empirical validation and analysis of the coupled transfer distance across diverse image classification datasets (MNIST (Le Cun et al., 1998), CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009) and Deep Fashion (Liu et al., 2016)). |
| Dataset Splits | No | The paper mentions 'validation accuracy' and 'validation loss' but does not specify the exact percentages or methodology for the training/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions using SGD and discusses network architecture types (8-layer CNN, WRN-16-4) and general training concepts (pre-processing, batch-normalization, dropout) but does not provide concrete hyperparameter values or detailed system-level training settings in the provided text. |