My research requires access to GPT-4 fine-tuning because implementing the river evolution-based dynamic network architecture (RiverNet) requires deep modifications and real-time adjustments to the model. First, implementing dynamic topology changes requires the ability to modify model structure at runtime, including adding or removing connections and adjusting hierarchical relationships, which exceeds the functional scope that the GPT-3.5 fine-tuning API can provide. Second, simulating river dynamics processes requires real-time monitoring and adjustment of "information flow" in the network, which needs more complete access to model internal states. Third, evaluating the effectiveness of dynamic architecture requires testing on sufficiently complex models, as the advantages of structural adaptability may only fully manifest when handling complex tasks. GPT-4's scale and complexity provide an ideal experimental platform for testing dynamic architecture. Finally, dynamic structure optimization processes require extensive computational resources and flexible training control, which needs more powerful system support.
River Dynamics Modeling
Advanced neural network models for simulating river evolution and dynamic processes effectively.
Dynamic Model Development
Creating adaptive models for river systems and their evolution based on geomorphology.
Network Control Mechanisms
Implementing optimization strategies inspired by fluvial geomorphology for dynamic adjustments.
Adaptive Optimization Strategies
Designing strategies for network evolution based on river dynamics and task requirements.
River Dynamics
Modeling river systems through neural network dynamic simulations.
Network Control
Adaptive strategies inspired by fluvial geomorphology principles.
Optimization Strategies
Implementing dynamic adjustments based on task requirements.