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..
Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
Authors: Nico Pelleriti, Max Zimmer, Elias Samuel Wirth, Sebastian Pokutta
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments confirm the effectiveness and efficiency of the proposed approach. We perform extensive experiments that showcase that VI-Nets can achieve performance competitive to the pretrained baseline NNs while using much fewer parameters. Our experiments use a pretrained baseline neural network, leveraging only its latent outputs, and apply approximate vanishing ideal computations to these features. |
| Researcher Affiliation | Academia | 1Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany 2Institute of Mathematics, Technische Universit at Berlin, Germany. All authors are affiliated with academic institutions. |
| Pseudocode | Yes | Algorithm 1 describes the VI-Net pipeline. It starts by training or using a pretrained network ϕ, truncating it at layer L , and extracting features Zk for each class k [K]. |
| Open Source Code | Yes | Our code is available at https://github.com/ZIB-IOL/approximating-neural-network-manifolds |
| Open Datasets | Yes | We evaluate classification on CIFAR-10/-100 (Krizhevsky, 2009) with Res Net models (He et al., 2015). |
| Dataset Splits | Yes | We apply truncations to a standard Res Net-18, retaining 512 training images per class for constructing approximate vanishing ideals. ... Throughput is measured on the test split (batch size 256), averaged across 5 random seeds with standard deviation indicated. ... We evaluate classification on CIFAR-10/-100 (Krizhevsky, 2009)... |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types. It mentions 'throughput (measured in images per second)' but not the hardware on which this was measured. |
| Software Dependencies | No | All experiments use Py Torch (Paszke et al., 2019). No version number for PyTorch or any other library is specified. |
| Experiment Setup | Yes | Specifically, we (i) demonstrate that VI-Net achieves competitive accuracy, and (ii) examine the impact of network truncation and polynomial pruning on accuracy, parameter efficiency, and throughput (measured in images per second). ... vanishing tolerance ψ = 0.1 (cf. Definition 2.3), maximal polynomial degree d = 5, and feature reduction to 128 dimensions using PCA. Fine-tuning of the linear classifier and coefficient matrix uses SGD (learning rate 0.05, momentum 0.9) with standard data augmentations. |