Transformers Can Reuse Hidden Token Representations Without Retraining
A new inference-time framework reuses highly similar hidden token states in transformers, preserving sequence length without retraining while largely maintaining ...
A new inference-time framework reuses highly similar hidden token states in transformers, preserving sequence length without retraining while largely maintaining ...
A federated Decision Transformer reached 92.5% scheduling accuracy in a simulated industrial environment while reducing parameter transmission by 63% relative ...
Researchers have developed LMANet, a lightweight multiscale attention network that achieves high-accuracy skin-lesion segmentation with only 5.6 million parameters by ...
A new lightweight AI framework called RAML embeds tumor subregion identity and geometric descriptors directly into its architecture, trading a ...
A controlled comparison shows attention-enhanced DenseNet121 reaching 97.4 percent accuracy for deepfake detection while a lighter MobileNetV2 variant offers a ...
Researchers have developed CNN-SA-RFR, a cosine similarity-based self-attention framework that suppresses redundant filters during CNN training, boosting plant disease classification ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine