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Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
The application of transformer-based models on time series forecasting (TSF) tasks has long been popular to study.
Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
Recent empirical studies have identified fixed point iteration phenomena in deep neural networks, where the hidden state tends to stabilize after several layers, showing minimal change in subsequent layers.
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