Understanding Influence Functions and Datamodels via Harmonic Analysis
Authors: Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on the CIFAR-10 data to test the estimation procedure and the quality of the linear fit in Figures 1 and 2. We use the FFCV library Leclerc et al. (2022) to train models on CIFAR-10; each model takes 30s to train on our GPUs. |
| Researcher Affiliation | Academia | Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora Department of Computer Science, Princeton University {nsaunshi, arushig, mbraverm, arora}@cs.princeton.edu |
| Pseudocode | Yes | Algorithm 1 Efficient algorithm for residual estimation |
| Open Source Code | No | The paper references the FFCV library (Leclerc et al., 2022) which is open-source, but does not state that the authors' *own* code for this paper's methodology or experiments is being released or is publicly available. |
| Open Datasets | Yes | Experiments are conducted on the CIFAR-10 data to test the estimation procedure and the quality of the linear fit in Figures 1 and 2. |
| Dataset Splits | No | The paper mentions training on subsets of CIFAR-10 and refers to 'test examples' but does not specify the train/validation/test splits or their sizes for reproducibility, beyond stating models were trained on sets of size 5000 from a 10k image subset. |
| Hardware Specification | No | The paper vaguely mentions "train on our GPUs" but does not provide specific GPU models, CPU models, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions using "the FFCV library Leclerc et al. (2022)" but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | We use the default Res Net based architecture in FFCV with a batch size of 512, an initial learning rate of 0.5, 24 epochs, weight decay of 5e-4 and SGD with momentum as the optimizer. |