Optimal Neural Codes for Control and Estimation
Authors: Alex K. Susemihl, Ron Meir, Manfred Opper
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evolution of the average posterior variance is given by the average of equation (6b), which involves nonlinear averages over the covariances. These are intractable, but a simple mean-field approach yields the approximate equation for the evolution of the average Σs = E [Σs|Σ0] ds = A Σs + Σs A + D ˆλ Σs P Σs I + P Σs 1 . The alternative is to simulate the stochastic dynamics of Σt for a large number of samples and compute numerical averages. These results can be directly employed to evaluate the optimal costto-go in the control problem f(Σ, t). |
| Researcher Affiliation | Collaboration | Alex Susemihl1, Manfred Opper Methods of Artificial Intelligence Technische Universit at Berlin 1 Current affiliation: Google Ron Meir Department of Electrical Engineering Technion Haifa |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper conducts theoretical analysis and simulations of stochastic systems (e.g., Ornstein Uhlenbeck process, stochastic damped harmonic oscillator) rather than using a publicly available dataset for training. |
| Dataset Splits | No | The paper focuses on theoretical models and simulations of stochastic systems, and as such, it does not specify training, validation, or test dataset splits. The study does not involve empirical evaluation on standard datasets with such splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations or conduct the research (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper discusses theoretical frameworks and algorithms like Kalman filtering, LQG control, and point-process filtering, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, specific libraries, or simulation software). |
| Experiment Setup | Yes | Parameters for figure (a) were: T = 2, γ = 1.0, η = 0.6, b = 0.2, φ = 0.1, θ = 0.05, Q = 0.1, QT = 0.001, R = 0.1. Parameters for figure (b) were T = 5, γ = 0.4, ω = 0.8, η = 0.4, r = 0.4, q = 0.4, QT = 0, φ = 0.5, θ = 0.1. |