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..
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
Authors: Jason Pacheco, Erik Sudderth
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach using optical flow benchmarks. We further demonstrate superior side chain prediction accuracy compared to baseline algorithms from the state-of-the-art Rosetta package. |
| Researcher Affiliation | Academia | Jason L. Pacheco EMAIL Erik B. Sudderth EMAIL Department of Computer Science, Brown University, Providence, RI 02912, USA |
| Pseudocode | Yes | LAZYGREEDY Particle Selection / Initialize: For each node t let M = M T s1t, . . . , M T sdt T be the message foundations of neighbors Γ(t) = {s1, . . . , sd}. Initialize the selection vector z and margins: a=1 M(a, b), z(b) = 0 b {1, αN}. (11) First Iteration: Ensure that the current MAP estimate x is never discarded by setting z(b ) = 1, where b is the index of x t in the augmented particle set Xaug t (see Sec. 3.3). Update the message approximation ˆm(a) = M(a, b ). Iterations 2 to N: Choose the largest margin to update, eb = arg max {b|z(b)=0} (b). If (eb) = 0 then terminate prematurely, the message can be perfectly reconstructed with a subset of particles. If (eb) has already been updated on the current iteration then set z(eb) = 1 and update the message approximation, ˆm(a) = max( ˆm(a), Mt(a,eb)). Otherwise, update the margin and repeat, h max( ˆm(a), M(a,eb)) ˆm(a) i . Selections are performed in parallel and updates at one node do not affect the selection at neighboring nodes. Figure 3 graphically demonstrates LAZYGREEDY selection on the small toy protein of Fig. 2. |
| Open Source Code | Yes | A MATLAB library, built on UGM (Schmidt, 2007), implementing these methods is available3. 3http://www.cs.brown.edu/ pachecoj |
| Open Datasets | Yes | We evaluate on the Middlebury optical flow benchmark (Baker et al., 2011) using 11 random initializations. The Middlebury training set contains 8 images with ground truth flow, and we report log-probability quantiles over this set (Fig. 7 (left)). |
| Dataset Splits | No | The Middlebury training set contains 8 images with ground truth flow, and we report log-probability quantiles over this set (Fig. 7 (left)). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | A MATLAB library, built on UGM (Schmidt, 2007), implementing these methods is available3. |
| Experiment Setup | Yes | We use the Charbonnier widths σ = 0.001 recommended for this model (Sun et al., 2014), but learn different scaling parameters (λs = 16, λd = 1) to compensate for our superpixel representation. We compute SLIC superpixels (Achanta et al., 2012) with region size 5 and regularizer 0.1; We run PMP with 50 particles for 50 iterations. D-PMP and T-PMP proposals are 50% random walks from Gaussians wrapped to account for angular discontinuities, and 50% samples from the rotamer marginals. |