TL;DR
A dynamic pruning method that uses reinforcement learning guided by a dynamic reward function to automatically explore the pruning rate of each layer, and uses the designed similarity-based connected graph (SCG) mechanism to perform channel removal operations during training.
Indicative in these results is that intelligent batch ordering can partially compensate for reduced architectural complexity, thus offering a computationally efficient alternative to deeper models.
Deep neural networks often contain substantial parameter redundancy, resulting in unnecessary computational cost and energy consumption. This work presents a dynamically adaptive gating mechanism for learning layer-wise sparsity through differentiable masking. In the proposed framework, each network parameter is associ...
The Cyclical Local Adaptive Learning Rate Strategy (CLARA) is proposed, an enhanced CLR that introduces scheduler-level adaptive learning rate adjustment and dynamically adjusts the lower learning rate bound in response to training behaviour to improve convergence consistency during training.
By periodically merging parallel network instances, this method effectively counteracts the instability triggered by aggressive structural changes, enabling the use of structured pruning directly compatible with general-purpose hardware.
This work proposes LaRes, a novel hybrid framework that achieves efficient policy learning through reward function search by leveraging large language models to generate the reward function population, guiding RL in policy learning.
This work introduces Elite-Weighted Supervised Fine-tuning (EW-SFT), which uses reward to guide elite selection of high-scoring molecules, and updates the model by its own pretraining loss on that set, and consistently outperforms the corresponding native optimizers.
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