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..
Real-time variational method for learning neural trajectory and its dynamics
Authors: Matthew Dowling, Yuan Zhao, Il Memming Park
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance. 5 EXPERIMENTS We first evaluate and compare e VKF to other online variational methods as well as classic filtering methods using synthetic data. |
| Researcher Affiliation | Academia | Matthew Dowling Stony Brook University, New York, USA EMAIL Yuan Zhao National Institute of Mental Health, USA EMAIL Il Memming Park Champalimaud Research, Champalimaud Foundation, Portugal EMAIL |
| Pseudocode | Yes | Algorithm 1 eVKF |
| Open Source Code | No | The paper does not explicitly state that open-source code for the described methodology is provided or include a link to a code repository. |
| Open Datasets | Yes | To evaluate e VKF with real-world neural data, we considered electrophysiological recordings taken from monkey motor cortex during a reaching task (Churchland et al., 2012). |
| Dataset Splits | No | The paper mentions training data points ('We train each method for 3500 data points') and subsequent inference but does not explicitly specify train/validation/test dataset splits needed for reproduction of data partitioning. |
| Hardware Specification | Yes | For measuring the time per step as in Table 2 the experiments were run on a computer with an Intel Xeon E5-2690 CPU at 2.60 GHz. |
| Software Dependencies | No | The paper mentions using 'Adam' and 'Si LU nonlinearity' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To parameterize the dynamics, pθ(zt | zt 1), we use a single layer MLP with 32 hidden units and Si LU (Elfwing et al., 2018) nonlinearity. During training we use Adam (Kingma & Ba, 2014), and update the dynamics every 150 time steps. In total we use 3500 time points for training the dynamics model for all methods. |