Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (1605.06432v3)

Abstract: We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.

• The paper demonstrates that DVBF effectively enforces state space model assumptions to enable reliable long-term predictions.
• It integrates neural networks with variational inference to capture nonlinear spatiotemporal dependencies without domain-specific knowledge.
• Experiments on dynamic systems confirm DVBF's scalability and superior performance, with enhanced log-likelihood scores and reduced reconstruction error.

Overview of Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

In this work, the authors introduce Deep Variational Bayes Filters (DVBF), an innovative method designed to tackle the challenge of unsupervised learning of state space models from raw sequential data. This approach capitalizes on recent advancements in Stochastic Gradient Variational Bayes (SGVB) to effectively manage intractable inference issues through variational inference. A primary advantage of DVBF is its ability to process highly nonlinear input data with spatiotemporal dependencies, such as image sequences, without requiring domain-specific knowledge.

Core Contributions

The paper presents several pivotal contributions to the field of unsupervised learning in dynamic systems:

1. Enforced State Space Model Assumptions: DVBF maintains the assumptions of latent state-space models, thereby enabling reliable system identification and plausible long-term predictions of observable systems. This approach uniquely balances the competing tasks of system representation and computational inference by ensuring that latent states contain full information.
2. Efficient Inference Mechanism: The inference mechanism developed in this work provides rich dependencies within the model, supporting the learning of complex dynamics without the necessity for simplified assumptions that could compromise performance.
3. Neural Network Integration: The model inherits the capabilities of neural architectures to train on raw data, such as sensory inputs. This integration enables handling of non-Markovian transitions present in high-dimensional sensory data.
4. Scalability: Through the implementation of stochastic gradient descent optimization, the proposed method exhibits scalability to large datasets, making it suitable for real-world applications with extensive data requirements.

Experimental Validation

The authors validate the efficacy of the DVBF approach through experiments using dynamic environments where the complete ground truth of the latent dynamical system is known. By testing on complex systems like a dynamic pendulum and a bouncing ball, DVBF successfully captures the entire system's dynamics and establishes learned latent spaces with full information. These experiments demonstrate that DVBF outperforms competing models such as the Deep Kalman Filter (DKF) in recovering latent states and predicting long-term trajectories.

Numerical Assessments and Implications

A notable aspect of this paper is its robust numerical evaluation. The DVBF shows statistically significant improvements in log-likelihood scores for encoding ground truth dynamics and outperforming previous models in terms of both reconstruction error and KL divergence. The use of annealed lower bound optimization further contributes to the effective training of the model by smoothing the non-convex error landscape, thus stabilizing the learning process.

On a practical level, DVBF's ability to work without domain-specific insights makes it highly versatile across various applications such as control systems, robotics, and model-based reinforcement learning. Theoretically, the enforcement of state-space model assumptions contributes to a more coherent understanding of dynamic systems by ensuring that all available information is captured in the latent states, allowing for more robust future predictions.

Conclusion and Future Directions

The presentation of Deep Variational Bayes Filters represents a significant step in the unsupervised learning of complex dynamic systems. The model's successful integration of state space assumptions into a neural framework, coupled with its unsupervised learning capability, opens paths for further research. Future developments in AI could focus on refining inference mechanisms and exploring more complex model structures to handle increased variability and uncertainty in real-world applications. The potential to adapt DVBF to various domains further emphasizes the utility and flexibility of this approach, fostering a deeper exploration into systems where full state observability may not be immediately accessible.