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..
Multi-layer State Evolution Under Random Convolutional Design
Authors: Max Daniels, Cedric Gerbelot, Florent Krzakala, Lenka Zdeborová
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theory numerically and observe close agreement between convolutional AMP iterations and its state evolution predictions, as shown in Figure 1 and in Section 5. |
| Researcher Affiliation | Academia | Max Daniels Northeastern University EMAIL Cédric Gerbelot ENS Paris EMAIL Florent Krzakala Ide PHIcs Laboratory, EPFL EMAIL Lenka Zdeborová SPOC Laboratory, EPFL EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code can be used as a general purpose library to build compositional models and evaluate AMP and its state evolution. We make this code available at https://github. com/mdnls/conv-ml-amp.git. |
| Open Datasets | Yes | As an example, we show in Figure 3 the sizes of convolutional layers used by the DC-GAN architecture to generate LSUN images [Radford et al., 2015, Figure 1]. |
| Dataset Splits | No | The paper does not explicitly specify training/test/validation splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In both, the output channel l = 1 generates noisy, compressive linear measurements y = z(1) + for i N(0, σ2) and for dense couplings W (1) ij N(0, 1/n(1)). Layers 2 l 4 use MCC couplings W (l) MCC(Dl, Pl, q, k), where q Pl = nl and Dl = βPl = qnl 1. Channel functions {'(l)} vary across the two experiments. The input prior is PX(x) = N(x; 0, 1) and the problem parameters are q = 10 channels, filter size k = 3, noise level σ2 = 10 4, input dimension n(L) = 5000, and layerwise aspect ratios β(L) = 2 and β(l) = 1 for 2 l < L. Finally, the channel aspect ratio β(1) varies in each experiment. |