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Proposes a novel self-supervised image clustering framework that uses an evolution-strategy outer loop to maximize a 'surprise score' without needing a per-step loss, paired with a gradient-descent inner loop, achieving state-of-the-art results on standard benchmarks in the strict non-parametric setting.
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.
OpenAI presents evolution strategies (ES) as a scalable black-box optimization alternative to reinforcement learning for training neural network policies. ES simplifies the optimization problem by treating policy training as a stochastic parameter search that repeatedly samples and selects better parameter configurations based on reward feedback.