Physics-inspired Energy Transition Neural Network for Sequence Learning (2505.03281v1)

Abstract: Recently, the superior performance of Transformers has made them a more robust and scalable solution for sequence modeling than traditional recurrent neural networks (RNNs). However, the effectiveness of Transformer in capturing long-term dependencies is primarily attributed to their comprehensive pair-modeling process rather than inherent inductive biases toward sequence semantics. In this study, we explore the capabilities of pure RNNs and reassess their long-term learning mechanisms. Inspired by the physics energy transition models that track energy changes over time, we propose a effective recurrent structure called the``Physics-inspired Energy Transition Neural Network" (PETNN). We demonstrate that PETNN's memory mechanism effectively stores information over long-term dependencies. Experimental results indicate that PETNN outperforms transformer-based methods across various sequence tasks. Furthermore, owing to its recurrent nature, PETNN exhibits significantly lower complexity. Our study presents an optimal foundational recurrent architecture and highlights the potential for developing effective recurrent neural networks in fields currently dominated by Transformer.

Physics-inspired Energy Transition Neural Network for Sequence Learning

In the domain of sequence modeling, the dominance of Transformer models over Recurrent Neural Networks (RNNs) has been undisputed. However, the paper "Physics-inspired Energy Transition Neural Network for Sequence Learning" introduces the Physics-inspired Energy Transition Neural Network (PETNN), suggesting a potential paradigm shift. The authors propose PETNN as a recurrent structure capable of competing with Transformer-based models and offer an effective technique for managing long-term dependencies in sequences.

Reassessment of RNN Capabilities

Traditionally, RNNs have faced challenges with gradient vanishing, particularly when dealing with long sequences. While LSTM and GRU variants mitigate these issues using gating mechanisms, they still fall short compared to Transformers, which utilize self-attention to efficiently capture pairwise sequence interactions. PETNN seeks to address the limitations inherent in the RNN architecture by introducing a memory mechanism inspired by physics-based energy transition models.

Energy Transition Mechanism and Model Components

The PETNN framework draws from the energy transition model in quantum physics, where information akin to energy levels in atoms is dynamically absorbed, stored, and released. Key components of PETNN include:

• Remaining Time (Tt): Represents the duration a memory cell retains its state before updating, analogous to an atom's time in an excited state.
• Cell State (Ct): Denotes the energy level of the neuron, undergoing transitions akin to quantum state changes.
• Hidden State (St): Encapsulates memory across time steps, updated based on both current inputs and past states.

PETNN neurons autonomously determine information updates using a self-selective information mixing method, allowing dynamic control over information storage duration and update proportion. This flexibility is a departure from conventional methods and enhances PETNN's adaptability in sequence learning tasks.

Empirical Evidence and Comparative Performance

Experimental results demonstrate PETNN's superior performance over Transformers across various sequence tasks, notably achieving lower MSE and MAE metrics in time series forecasting scenarios. PETNN consistently ranks among the top performers when compared to state-of-the-art methods, showcasing lower computational complexity due to its recurrent nature.

Implications and Future Directions

PETNN offers a novel perspective on addressing long-term dependencies in sequence modeling, potentially challenging the Transformer model hegemony. Its physics-inspired architecture paves the way for further research into enhancing recurrent models in domains currently dominated by Transformers. Future work may focus on expanding its applicability beyond sequential tasks by embedding PETNN within broader neural network structures, aligning with its demonstrated efficacy in image classification tasks.

Conclusion

The introduction of PETNN signifies a promising advancement in sequence learning, particularly for tasks requiring efficient long-term dependency management. Its design reflects a judicious blend of physics principles with neural network architecture, highlighting a novel trend towards physics-inspired machine learning models. As the field evolves, PETNN's contributions may represent a pivotal step in reestablishing the significance of RNNs in cutting-edge applications.