Coin Flipping Neural Networks
Authors: Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify our proofs experimentally using novel CFNN architectures on CIFAR10 and CIFAR100, reaching an improvement of 9.25% from the baseline. In this section we experiment with CFNNs as classifiers of CIFAR10 and CIFAR100 (Krizhevsky, 2009). We present 3 experiments: a study of a hypernetwork architecture inspired by Theorem 4.1; Res Net networks with Dropout viewed as CFNNs; and an analysis of CFNN s accuracy as the number of amplification samples changes. In all experiments, empirical random accuracy was used as the target metric (see Definition 2.1). |
| Researcher Affiliation | Academia | Yuval Sieradzki 1 Nitzan Hodos 1 Gal Yehuda 1 Assaf Schuster 1 1Department of Computer Science, Technion Israel Institute of Technology, Haifa, Israel. Correspondence to: Yuval Sieradzki <syuvsier@campus.technion.ac.il>, Nitzan Hodos <hodosnitzan@campus.technion.ac.il>, Assaf Schuster <assaf@technion.ac.il>. |
| Pseudocode | No | No clearly labeled 'Pseudocode' or 'Algorithm' blocks were found in the paper. The methodology is described in narrative text and mathematical formulas. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | In this section we experiment with CFNNs as classifiers of CIFAR10 and CIFAR100 (Krizhevsky, 2009). |
| Dataset Splits | Yes | In this section we experiment with CFNNs as classifiers of CIFAR10 and CIFAR100 (Krizhevsky, 2009). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or detailed specifications of the machines used for experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., libraries, frameworks, or programming languages) used in the experiments. |
| Experiment Setup | Yes | Training Hyperparameters Our CFNN is optimized using SGD with Cross Entropy loss, momentum=0.9 and L2 weight decay of 5 10 3, performing majority as described in Section 5. We train our models for 200 epochs with batch size 128. We apply a cosine learning rate scheduler with an initial learning rate 0.1. For CIFAR10, we use n = 25 samples and for CIFAR100 we use n = 125 samples. |