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 [1].
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. |