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..
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
Authors: Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation of the approach on MNIST shows that CBCs are viable classifiers. Additionally, we demonstrate that the inherent interpretability offers a profound understanding of the classification behavior such that we can explain the success of an adversarial attack. The method s scalability is successfully tested using the IMAGENET dataset. |
| Researcher Affiliation | Collaboration | 1Dr. Ing. h.c. F. Porsche AG, Weissach, Germany, EMAIL 2University of Applied Sciences Mittweida, Mittweida, Germany, EMAIL |
| Pseudocode | No | The paper describes the network architecture and training process conceptually, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at www.github.com/saralajew/cbc_networks. |
| Open Datasets | Yes | We evaluate CBCs on MNIST [40] and IMAGENET [41]. |
| Dataset Splits | Yes | The final test accuracies of both models are quasi equivalent and on average over three runs (97.33 0.19) %. The top-5 validation accuracy of 82.4% is on par with earlier CNN generations such as Alex Net with 82.8% [48]. |
| Hardware Specification | Yes | The CBC was evaluated using one NVIDIA Tesla V100 32 GB GPU. |
| Software Dependencies | No | The paper mentions using specific optimizers (Adam) and activation functions (Swish, ReLU) and architectures (ResNet-50) by name and citation, but it does not specify software versions for any libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | The CBCs use the cosine similarity with Re LU activation as detection probability function. They are trained with the margin loss defined as Eq. (2) with φ (x) = Re LU(x + β), where β is a margin parameter, using the Adam optimizer [42]. The CNN feature extractors are implemented without the use of batch normalization [43], with Swish activation [44], and the convolutional filters constraint to a Euclidean norm of one. We trained the components and reasoning probabilities from scratch using random initialization. Moreover, the margin parameter β was set to 0.3. |