Intermediate layers in single-cell foundation models encode optimal representations for biological tasks, outperforming final layers in a task- and context-dependent manner.
Tahoe-x1: Scaling perturbation-trained single-cell foundation models to 3 billion parameters.bioRxiv, pp
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Sigmoid attention replaces softmax in single-cell foundation models to deliver better representations, faster training, and stability, backed by bounded derivatives, diagonal Jacobian, and a new efficient GPU kernel.
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Intermediate Layers Encode Optimal Biological Representations in Single-Cell Foundation Models
Intermediate layers in single-cell foundation models encode optimal representations for biological tasks, outperforming final layers in a task- and context-dependent manner.
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Better Models, Faster Training: Sigmoid Attention for single-cell Foundation Models
Sigmoid attention replaces softmax in single-cell foundation models to deliver better representations, faster training, and stability, backed by bounded derivatives, diagonal Jacobian, and a new efficient GPU kernel.