Latent Matters: Learning Deep State-Space Models
Authors: Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation includes experiments on the image data of a moving pendulum [13] and on the reacher environment [26], where we use angle as well as high-dimensional RGB image data as observations. |
| Researcher Affiliation | Collaboration | 1Technical University of Munich 2Machine Learning Research Lab, Volkswagen Group, Munich 3Eötvös Loránd University 4AWS AI Labs |
| Pseudocode | Yes | Algorithm 1 REWO for deep state-space models |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | The pendulum dataset was originally introduced in [13] and consists of 500 sequences with 15 images each... The reacher dataset consists of 2000 sequences with 30 time steps each. We use two versions in our experiments: (i) partially observed system states, i.e. the angles of the first and second joint; and (ii) RGB images of 64 64 pixels in size. |
| Dataset Splits | No | The paper describes the datasets and some aspects of evaluation (e.g., 'MSE of 500 predicted sequences') and refers to 'test ELBO' in tables, but it does not specify explicit train/validation/test split percentages or sample counts for the datasets used. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions general software concepts like 'RNNs' and 'LSTMs' but does not list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We provide a heuristic for the simple determination of D0 in App. A.1... Furthermore, Alg. 1 allows us to efficiently learn the parameters of the VHP (ψ0, φ0) and the transition model (ψ) by dividing the CO process into two phases: an initial and a main phase... For this purpose, we apply a special update scheme for λ, introduced and explained in depth in [16]: λ(i) = λ(i 1) exp h ν fλ λ(i 1), D(i)(θ, φ) D0; τ1, τ2 D(i)(θ, φ) D0 i . In this context, i denotes the iteration step of the optimisation process. The function fλ is defined as fλ(λ, δ; τ1, τ2) = 1 H(δ) tanh (τ1 (1/λ 1)) τ2 H (δ), where H is the Heaviside function, and τ1 as well as τ2 are slope parameters. |