To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions.

This is done in two stages.

We also test the direct maximization of Kullback-Leibler (KL) divergence between feature distributions. As presented in Table A.1, the direct maximization of Direct maximize KL divergence between feature distributions. We further conduct ablation studies focusing on directly maximizing the Kullback-Leibler (KL) divergence between feature distributions of peer Bayesian neural networks (as in setting d in Table A.1). Table A.2, the results for both ResNet20 and ResNet32 BNN models demonstrate that using optimal "*" means Bayesian neural networks that are initialized with the mean value from the pre-trained The results are shown in Table A.3. Figure A.1: Comparison of optimal transport distance between the parameter distributions of peer A.1, it is clear that our proposed method, which promotes A.2, it is clear that our proposed method, which promotes diversity in the feature

We repeat this process until the size of the negative set is 5x that of the positive set. The expanded dataset is then provided to the respective ERGO model. Any unobserved pair is treated as negative. Performance is shown in Table S2. TCRs that have more than one positive prediction or have at least one wrong prediction.

Eq. (3) defines PCE, which quantifies the expected disparity between the predicted probabilities and

Let's follow the notations in Alg. 3 of Argmax Flow. We can unfold the determinant by the i-th row. This is illustrated in Figure A.1, where the adaptive Further details can be found in Tables A.2. Furthermore, we will make the code used to reproduce these results publicly available. In different environments, different state encoders were exploited. We used MLP encoder for discrete control tasks and CNN encoder for Pistonball task.