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Introduces Eggroll, a low-rank evolution strategy for gradient-free training of spiking neural networks, reducing memory and time overhead while achieving competitive accuracy on N-MNIST.
NVIDIA and Oxford University introduced EGGROLL, a scalable evolution strategies algorithm that trains billion-parameter models without backpropagation, using only integers and parallel mutations.
This paper introduces GUARD-IT, a training-free method for machine unlearning that uses input-dependent activation steering at inference time to remove targeted knowledge from LLMs without modifying weights, matching or exceeding gradient-based baselines while preserving utility and robustness to quantization.