Real-time variational method for learning neural trajectory and its dynamics
Authors: Matthew Dowling, Yuan Zhao, Il Memming Park
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 matthew.dowling@stonybrook.edu Yuan Zhao National Institute of Mental Health, USA yuan.zhao@nih.gov Il Memming Park Champalimaud Research, Champalimaud Foundation, Portugal memming.park@research.fchampalimaud.org |
| 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. |