uncertainty-aware strategy selection. A multi-head Variational Bayesian Last Layer (VBLL) model predicts the expected tracking performance of each expert strategy given the current belief state, pro...
well-balanced expert utilization. We attribute this gap to a pre-routing bottleneck: multi-head attention concatenates head-specific signals into a single post-attention router input, forcing routing to act o...
We leverage sub-embeddings from each head in the final multi-head attention layer to predict the next item separately, effectively capturing distinct item facets. A gating mechanism then integrates th...
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[19] YANG X N, XIAO Y L. Named entity recognition based on BERT-MBiGRU-CRF and multi-head self-attention mechanism[C] //2022 4th International Conference on Natural Language Processing(I...
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and multi-head attention (MHA) subblocks. Each node identifies small clusters of possible output labels, with additional noise represented as labels outside these clusters. These features are progress...
MoH consists of multiple attention heads and a router that activates the Top-K heads for each token. Moreover, we replace the standard summation in multi-head attention with a weighted summation. ...
multi-head mechanism enables the model to collectively attend to information from various representation spaces within different experts, while significantly enhances expert activation, thus deepens c...
