Adversarial Robustness of Supervised Sparse Coding
Authors: Jeremias Sulam, Ramchandran Muthukumar, Raman Arora
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In this section, we illustrate the robustness certificate guarantees both in synthetic and real data, as well as the trade-offs between constants in our sample complexity result. ... We use the MNIST dataset for this analysis. |
| Researcher Affiliation | Academia | Jeremias Sulam Johns Hopkins University jsulam1@jhu.edu Ramchandran Muthukumar Johns Hopkins University rmuthuk1@jhu.edu Raman Arora Johns Hopkins University arora@cs.jhu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code to reproduce all of our experiments is made available at our github repository. |
| Open Datasets | Yes | Image data We now demonstrate that a positive encoder exist for natural images as well. In Figure 1b we similarly depict the value of τs( ), as a function of s, for an encoder computed on MNIST digits and CIFAR images (from a validation set) with learned dictionaries (further details in Section 6). ... We use the MNIST dataset for this analysis. |
| Dataset Splits | Yes | In Figure 1b we similarly depict the value of τs( ), as a function of s, for an encoder computed on MNIST digits and CIFAR images (from a validation set) with learned dictionaries (further details in Section 6). ... To address this, we split the data equally into a training set and a development set: the former is used to learn the dictionary, and the latter to provide a high probability bound on the event that τs(x) > τ s . This is to ensure that the random samples of the encoder margin are i.i.d. for measure concentration. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It discusses experimental settings and datasets but omits hardware specifications. |
| Software Dependencies | No | The paper mentions using 'Adam [Kingma and Ba, 2014]' for stochastic gradient descent, but does not provide specific version numbers for any software components, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | We train a model with 256 atoms by minimizing the following regularized empirical risk using stochastic gradient descent (employing Adam [Kingma and Ba, 2014]; the implementation details are deferred to Appendix D)... Recall that ϕD(x) depends on λ, and we train two different models with two values for this parameter (λ = 0.2 and λ = 0.3). |