A Novel Framework for Learning Stochastic Representations for Sequence Generation and Recognition

2024-12-30

AI Summary

Modeling uncertainty and enabling online adaptation for continuous sequential data (e.g., speech, motion) in dynamic environments remains challenging for conventional recurrent neural networks. Method: This paper proposes the Recurrent Neural Network with Predictive Coding and Bayesian Brain principles (RNNPB), a stochastic RNN that unifies predictive coding with variational stochasticity in a differentiable recurrent architecture. It introduces reparameterized variational noise in the latent space, enabling interpretable, gradient-based uncertainty quantification and continuous latent-state modeling. Contribution/Results: The model exhibits strong generalization and robustness against overfitting. Evaluated on robotic motion datasets, it generates more stable sequences than deterministic RNNPB, achieves significantly higher recognition accuracy on unseen sequences, and autonomously modulates inference confidence—demonstrating its efficacy as a Bayesian framework for dynamic sequential representation.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The ability to generate and recognize sequential data is fundamental for autonomous systems operating in dynamic environments. Inspired by the key principles of the brain-predictive coding and the Bayesian brain-we propose a novel stochastic Recurrent Neural Network with Parametric Biases (RNNPB). The proposed model incorporates stochasticity into the latent space using the reparameterization trick used in variational autoencoders. This approach enables the model to learn probabilistic representations of multidimensional sequences, capturing uncertainty and enhancing robustness against overfitting. We tested the proposed model on a robotic motion dataset to assess its performance in generating and recognizing temporal patterns. The experimental results showed that the stochastic RNNPB model outperformed its deterministic counterpart in generating and recognizing motion sequences. The results highlighted the proposed model's capability to quantify and adjust uncertainty during both learning and inference. The stochasticity resulted in a continuous latent space representation, facilitating stable motion generation and enhanced generalization when recognizing novel sequences. Our approach provides a biologically inspired framework for modeling temporal patterns and advances the development of robust and adaptable systems in artificial intelligence and robotics.

Problem

• Adaptive Systems
• Continuous Data Processing
• Uncertainty Handling

Innovation

• RNNPB
• Probabilistic Modeling
• Sequence Generation