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..
Hold me tight! Influence of discriminative features on deep network boundaries
Authors: Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, Pascal Frossard
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically support our findings by extensive evaluations on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Guillermo Ortiz-Jiménez EPFL, Lausanne, Switzerland EMAIL Apostolos Modas EPFL, Lausanne, Switzerland EMAIL Seyed-Mohsen Moosavi-Dezfooli ETH Zürich, Zurich, Switzerland EMAIL Pascal Frossard EPFL, Lausanne, Switzerland EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to reproduce our experiments can be found at https://github.com/LTS4/hold-me-tight. |
| Open Datasets | Yes | In our study, we train multiple networks on MNIST [25] and CIFAR-10 [26], and for Image Net [27] we use several of the pretrained networks provided by Py Torch [28]5. |
| Dataset Splits | No | The paper mentions training and test sets, but does not explicitly provide details about validation set splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions PyTorch [28] but does not provide specific version numbers for software dependencies. |
| Experiment Setup | No | The paper mentions general training details like using SGD and cross-entropy loss, but it does not provide specific hyperparameter values or comprehensive training configurations in the main text needed for exact reproduction. |