<ul data-eligibleForWebStory="true"><li>The study discusses improving pooling methods in transformer models for reinforcement learning and vision tasks.</li><li>Pooling methods like AvgPool, MaxPool, and ClsToken are found to struggle with fluctuating signal-to-noise ratios.</li><li>An attention-based adaptive pooling technique is proposed to minimize signal loss in scenarios with varying SNRs.</li><li>The adaptive pooling method proves to be more robust and effective compared to traditional pooling approaches in various tasks.</li><li>The research emphasizes on vector quantization to optimize information retention in transformer model outputs.</li><li>Experiments on synthetic datasets and real-world tasks demonstrate the superiority of adaptive pooling in maintaining performance.</li><li>The study provides theoretical insights and practical validations for the effectiveness of the proposed adaptive pooling strategy.</li></ul>

Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs

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