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..
QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models
Authors: Xiangming Meng, Yoshiyuki Kabashima
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted, demonstrating the substantial superiority of QCS-SGM+ over QCS-SGM for general sensing matrices beyond mere row-orthogonality. |
| Researcher Affiliation | Academia | 1The Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, China 2Institute for Physics of Intelligence & Department of Physics, The University of Tokyo, Japan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: QCS-SGM+ Input: {βt}T t=1, ϵ, γ, Iter EP, K, y, A, σ2, quantization thresholds {[lq, uq)|q Q} Initialization: x0 1 U (0, 1) 1 for t = 1 to T do 2 αt ϵβ2 t /β2 T 3 for k = 1 to K do 4 Draw zk t N(0, I) Initialization: h F , τ F , h G, τ G 5 for it = 1 to Iter EP do 7 τ G = 1 χa τ F 9 τ F = 1 χb τ G 10 Compute xt log p(y | xt) as (23) 11 xk t = xk 1 t + αt h sθ(xk 1 t , βt) + γ xt log p(y | xt) i + 2αtzk t 12 x0 t+1 x K t Output: ˆx = x K T |
| Open Source Code | Yes | The source code is available at https://github.com/mengxiangming/QCS-SGM-plus. |
| Open Datasets | Yes | Datasets: We consider three popular datasets: MNIST (Le Cun and Cortes 2010) , CIFAR-10 (Krizhevsky and Hinton 2009), and Celeb A (Liu et al. 2015). |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly state the train/validation/test splits or their methodology for splitting. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using NCSNv2 but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | The paper specifies various experimental settings and hyperparameters, including the number of measurements (M), noise standard deviation (σ), condition number (κ), and algorithm parameters such as {βt}T t=1, ϵ, γ, Iter EP, K, and quantization thresholds {[lq, uq)|q Q} as shown in Algorithm 1 and figure captions (e.g., Figure 2: 'M = 400, σ = 0.05, κ = 103' for MNIST). |