Dynamic Distribution

Dynamic distribution research focuses on understanding and manipulating the changing probability distributions of data, parameters, or features within various systems. Current efforts concentrate on developing methods to align or adapt these distributions across different domains (e.g., simulation to reality, different subjects or sessions), often employing techniques like manifold transformations, entropy maximization, or adversarial training within models ranging from non-deep learning approaches to deep learning and reinforcement learning frameworks. This work is crucial for improving the robustness and generalizability of machine learning models across diverse and evolving data, with applications spanning areas like emotion recognition, natural language processing, and robotics. The ultimate goal is to create more reliable and adaptable systems capable of handling real-world complexities.