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..
Model-Based Control with Sparse Neural Dynamics
Authors: Ziang Liu, Genggeng Zhou, Jeff He, Tobia Marcucci, Fei-Fei Li, Jiajun Wu, Yunzhu Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical and hardware experiments show that, despite the aggressive sparsification, our framework can deliver better closed-loop performance than existing state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Cornell University 2Stanford University 3Massachusetts Institute of Technology 4University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper describes procedures in text and equations, but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions 'Please see our website at robopil.github.io/Sparse-Dynamics/ for additional visualizations.' but does not explicitly state that the source code for their method is available at this link or elsewhere. |
| Open Datasets | Yes | For closed-loop control evaluation, we additionally present the performance of our framework on two standardized benchmark environments from Open AI Gym [7], Cart Pole-v1 and Reacher-v4. |
| Dataset Splits | No | The paper describes data collection for training (e.g., '1,600 transition pairs') and mentions evaluating 'long-horizon predictive capability' but does not explicitly provide details about specific train/validation/test dataset splits (e.g., percentages or sample counts) for their collected data or how they set up validation for the OpenAI Gym environments. |
| Hardware Specification | No | The paper mentions 'Numerical and hardware experiments' but does not provide any specific details about the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | Yes | We generated 50,000 transition pairs using the Pymunk simulator [6]. The formulation in Equation 11 is the simplest mixed-integer encoding of a Re LU network, and a variety of strategies are available in the literature to accelerate the solution of our MIPs. ... and solve the problem using a commercial optimization solver, Gurobi [18]. Post-pruning, model speedup is performed using Neural Network Intelligence Library [51]. |
| Experiment Setup | Yes | Instead of limiting the number of regular Re LUs from the very beginning of the training process, we start with a randomly initialized neural network and use gradient descent to optimize ω and π by minimizing the following objective function until convergence: E[L(θ)] + λR(π), where the regularization term R(π) aims to explicitly reduce the use of the regular Re LU function. ...We then take an iterative approach by starting with a relatively large ε1 and gradually decrease its value for K iterations with ε1 > ε2 > > εK = ε. ...This yields a spectrum of models with varying degrees of sparsification. |