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..
Rethinking Influence Functions of Neural Networks in the Over-Parameterized Regime
Authors: Rui Zhang, Shihua Zhang9082-9090
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real-world data confirm our theoretical results and demonstrate our findings. |
| Researcher Affiliation | Academia | 1NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China 2School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that source code for their methodology is released or provide a link to it. |
| Open Datasets | Yes | In particular, we evaluate our method on MNIST (Lecun et al. 1998) and CIFAR-10 (Krizhevsky and Hinton 2009) |
| Dataset Splits | Yes | In particular, we evaluate our method on MNIST (Lecun et al. 1998) and CIFAR-10 (Krizhevsky and Hinton 2009) for two-layer Re LU neural networks with the width from 104 to 8 104 respectively. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "NEURAL TANGENTS" as a tool used by others, but it does not provide specific version numbers for any software dependencies used in their experiments. |
| Experiment Setup | Yes | We train the neural networks through gradient descent on the regularized mean square error loss function as follows: ... + λ 2 W W(0) 2 F ... We initialize the parameters randomly as follows: wr(0) N 0, κ2Id , ar(0) unif({ 1, 1}), r [m], where 0 < κ 1 controls the magnitude of initialization, and all randomnesses are independent. For simplicity, we fix the second layer a and only update the first layer W during training. |