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..
Controlling Neural Level Sets
Authors: Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have tested our method on three different learning tasks: improving generalization to unseen data, training networks robust to adversarial attacks, and curve and surface reconstruction from point clouds. |
| Researcher Affiliation | Academia | Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman Weizmann Institute of Science Rehovot, Israel |
| Pseudocode | No | The paper describes methods using mathematical equations and textual explanations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement about the release of its own source code for the methodology described. |
| Open Datasets | Yes | Experiments were done on three datasets: MNIST [18], Fashion-MNIST [31] and CIFAR10 [16]. For surface reconstruction, we trained on 10 human raw scans from the FAUST dataset [5] |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not explicitly specify a validation dataset split or percentages for such a split. It states 'We randomly sampled a fraction of the original training examples and evaluated on the original test set.' |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper refers to PyTorch in its bibliography [25], but does not explicitly list software dependencies with specific version numbers (e.g., 'PyTorch 1.x' or 'Python 3.x'). |
| Experiment Setup | Yes | We performed 10-20 iterations of Equation 4 for each pi, i [n]. We used our method with the loss in Equation 12 to train robust models on MNIST [18] and CIFAR10 [16] datasets. The parameter εj fixed as εtrain in Table 1, λj to be 1, 11 for MNIST and CIFAR10 (resp.), and d = ρ as explained in Section 3.2. |