L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Authors: Yuchen Zhang, Jason D. Lee, Michael I. Jordan
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the proposed algorithm with several baseline algorithms on the MNIST digit recognition task. |
| Researcher Affiliation | Academia | Yuchen Zhang YUCZHANG@EECS.BERKELEY.EDU Jason D. Lee JASONDLEE@BERKELEY.EDU Michael I. Jordan JORDAN@CS.BERKELEY.EDU Department of EECS, University of California, Berkeley, CA 94720 USA |
| Pseudocode | Yes | Algorithm 1 Recursive Kernel Method Input: Feature-label pairs {(xi, yi)}n i=1; Loss function ℓ: R R R; Number of hidden layers k; Regularization coefficient B. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing the source code for the proposed recursive kernel method. |
| Open Datasets | Yes | We use the MNIST handwritten digits dataset and three variations of it. See Figure 2 for the description of these datasets and several exemplary images. Variations on the MNIST digits. http://www.iro. umontreal.ca/ lisa/twiki/bin/view. cgi/Public/Mnist Variations. |
| Dataset Splits | Yes | For all datasets, we use 10,000 images for training, 2,000 images for validation and 50,000 images for testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud instance specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | For the recursive kernel method, we train one-vs-all SVM classifiers with Algorithm 1. The hyper-parameters are given by k {1, 4} and B = 100. All images are preprocessed by the following steps: deskewing, centering and normalization. |