Papers by Yang Young Lu
Interactive Training: Feedback-Driven Neural Network Optimization (2025.emnlp-demos)
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| Challenge: | In traditional neural network training, static optimization methods lack flexibility and responsiveness . authors demonstrate that Interactive Training provides superior training stability and reduced sensitivity to initial hyperparameters . |
| Approach: | They propose an open-source framework that enables real-time feedback-driven optimization of neural networks by human experts or automated AI agents. |
| Outcome: | The proposed framework achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs. |