Hinton et al. first propose the concept of distillation, where a lightweight student model

However, one rarely explored issue is how well existing methods can handle the unique property ofDNAsequences:heterogeneity.

CLIP model to map the training data to a compact feature representation, which essentially extends the sparse semantics of training data to dense ones, thus alleviating the semantic gap of the instances nearby known concepts (i.e., inside the

Overall,twelvevarious monotonically increasing functions have achieved better results than the baseline method. The training cost mainly depends on the original networks and iterative times. Then the computational cost of the backpropagation can be approximately equal ton. Step2 training process is trained with4 105 minibatch updates and the variance branch doesn't require backpropagation. Figure 1 and Figure 1 shows the uncertaintyθ captured by proposedLUDL on three different SISR networks.

Useful biases often exploit symmetries intheprediction problem, suchasconvolutional networks relying on translation equivariance.