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..
Can Implicit Bias Imply Adversarial Robustness?
Authors: Hancheng Min, Rene Vidal
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, numerical experiments on real datasets show that shallow networks with our generalized Re LU activation functions are much more robust than those with a Re LU activation. |
| Researcher Affiliation | Academia | 1University of Pennsylvania, Philadelphia, PA, USA. Correspondence to: Hancheng Min <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Estimating dist(fp, F) |
| Open Source Code | Yes | Code available at https://github.com/hanchmin/pReLU_ICML24. |
| Open Datasets | Yes | We first consider training a p Re LU network of width h = 500 to predict whether an MNIST digit is even or odd. |
| Dataset Splits | No | We run SGD (batch size 100 and step size 0.2 for 2 105 epochs) with small initialization (all weights initialized as mean-zero Gaussian with standard deviation 10 7) to train a p Re LU network with h = 2000 neurons on a dataset drawn from DX,Y with D = 1000, K = 10, and K1 = 6. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud services) are mentioned in the paper. |
| Software Dependencies | No | The paper mentions optimizers like "SGD" and "Adam," but does not provide specific version numbers for any software, programming languages, or libraries used for implementation. |
| Experiment Setup | Yes | We run SGD (batch size 100 and step size 0.2 for 2 105 epochs) with small initialization (all weights initialized as mean-zero Gaussian with standard deviation 10 7) to train a p Re LU network with h = 2000 neurons on a dataset drawn from DX,Y with D = 1000, K = 10, and K1 = 6. |