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Found 3 papers, 1 papers with code
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
While compared in terms of runtime, AFLoRA can yield up to $1. 86\times$ improvement as opposed to similar PEFT alternatives.
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity.
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS
In this paper, we present Fast Learnable Once-for-all Adversarial Training (FLOAT) which transforms the weight tensors without using extra layers, thereby incurring no significant increase in parameter count, training time, or network latency compared to a standard adversarial training.
