Sequential Neural Models with Stochastic Layers (1605.07571v2)

Abstract: How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.

• The paper introduces SRNNs that integrate deterministic RNNs with nonlinear state-space models to better propagate uncertainty in sequential data.
• It leverages variational inference to approximate complex posteriors, achieving significant performance gains on benchmarks like Blizzard and TIMIT.
• Results demonstrate SRNNs' effectiveness in capturing intricate temporal dependencies, with promising implications for speech synthesis and music generation.

Sequential Neural Models with Stochastic Layers

The paper "Sequential Neural Models with Stochastic Layers" discusses the integration of recurrent neural networks (RNNs) with state space models (SSMs) into what is termed as Stochastic Recurrent Neural Networks (SRNNs). This hybrid model aims to address a prevalent challenge in the machine learning community: effectively propagating uncertainty in latent state representations when dealing with sequential data such as speech and music.

Model Architecture and Theoretical Framework

SRNNs are designed by stacking an RNN with a nonlinear SSM to leverage the capabilities of both models. This results in a generative model where sequences are represented through a combination of deterministic and stochastic states. The inclusion of stochastic layers in the network allows the SRNN to better capture the nuances of sequential data, outperforming traditional RNN architectures on several benchmark datasets as evidenced by the results presented for the Blizzard and TIMIT speech datasets.

In SRNNs, the deterministic RNN component retains its long-term dependency capturing capabilities, while the SSM component introduces stochasticity by considering the latent states (influenced by uncertainty) as non-deterministic variables. This construction allows the SRNN to model intricate temporal dependencies with greater expressivity, which is particularly beneficial for complex data sequences where the propagation of uncertainty in latent states can enhance performance.

A notable theoretical advancement in this paper is the use of variational inference to approximate the intractable posterior distributions in the model. The authors structure the variational approximation by taking cues from the independence properties of the true posterior distribution, thus improving the expressive power of the model while simplifying computational demands. This is achieved through the implementation of an inference network that draws from the principles of variational auto-encoders.

Key Results

The experimental results emphasize the efficacy of SRNNs in speech and music modeling tasks. SRNNs significantly improve the average log-likelihood on the Blizzard and TIMIT datasets compared to previous methods, particularly outperforming the Variational Recurrent Neural Networks (VRNN) on these tasks. The performance gain is attributed to SRNN's ability to incorporate future information for smoothing and by the implementation of a residual parameterization that enhances inference by refining the error between the predictive prior and the posterior.

In polyphonic music modeling, SRNNs exhibit comparable performance to established models such as RNN-NADE, demonstrating their versatility in handling various types of sequential data.

Implications and Future Directions

The implications of this research extend to both practical applications and theoretical advancements in the field of sequential data modeling. Practically, SRNNs offer a robust framework for applications requiring the modeling of complex temporal sequences with inherent uncertainties, such as speech synthesis, music generation, and potentially, financial time series analysis. Theoretically, the structured variational inference approach introduced offers insights into improving inference mechanisms for complex models.

The future trajectory for SRNNs may involve exploring more sophisticated ways to integrate stochasticity and improving the computational efficiency of inference for extremely long sequences. Additionally, fine-tuning the balance between deterministic and stochastic elements in the network could lead to further performance improvements, and more broadly, to innovations across fields that depend heavily on sequence modeling. Continued exploration of model interpretability and robustness under uncertainty will be crucial directions for future work.

Overall, this paper presents a significant step towards advancing neural generative models for sequential data by innovatively integrating RNNs with SSMs and refining inference through variational techniques, marking a promising avenue for future research and application in artificial intelligence.