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..
Coin Flipping Neural Networks
Authors: Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster
ICML 2022 | Venue PDF | 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 <EMAIL>, Nitzan Hodos <EMAIL>, Assaf Schuster <EMAIL>. |
| 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. |