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].
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Authors: Robert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay Pande
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement our algorithm on GPUs and apply the method to two large protein simulation datasets generated respectively on the NCSA Bluewaters supercomputer and the Folding@Home distributed computing network. |
| Researcher Affiliation | Academia | Robert T. Mc Gibbon EMAIL Department of Chemistry, Stanford University, Stanford CA 94305, USA Bharath Ramsundar EMAIL Department of Computer Science, Stanford University, Stanford CA 94305, USA Mohammad M. Sultan EMAIL Department of Chemistry, Stanford University, Stanford CA 94305, USA Gert Kiss EMAIL Department of Chemistry, Stanford University, Stanford CA 94305, USA Vijay S. Pande EMAIL Department of Chemistry, Stanford University, Stanford CA 94305, USA |
| Pseudocode | No | The paper describes the learning algorithm using text and equations but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public release of its source code. |
| Open Datasets | No | The paper describes using custom-generated datasets from specific supercomputing resources and distributed networks, but does not provide concrete access information (link, DOI, repository, or explicit public availability statement) for these datasets. |
| Dataset Splits | No | The paper describes using a model selection criterion based on convergence of relaxation timescales, but does not provide specific train/test/validation dataset splits. |
| Hardware Specification | Yes | The speedup using our GPU implementation is 15 compared to our optimized CPU implementation and 75 with respect to a standard numpy implementation using K = 16 states on a NVIDIA GTX TITAN GPU / Intel Core i7 4 core Sandy Bridge CPU platform. |
| Software Dependencies | No | The paper mentions software components like 'numpy', 'Open MP', 'SSE2 intrinsics', and 'CUDA kernels' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | HMMs were constructed with 2 to 6 states. We chose by monitoring the convergence of the relaxation timescales as discussed in Sec. 3.2, and set the L1 fusion penalty heuristically to a default value of λ = 0.01. |