Weevaluate PI-SAC agents by comparing against uncompressed PI-SAC agents, other compressed and uncompressed agents, and SAC agents directly trained from pixels.

From the classification ablation results, summation is better than max-pooling and mean-pooling. After migrating them to point cloud analysis with Point-to-Pixel Prompting, we report the number of trainable parameters (Tr. Methodmax mean sum Accuracy 92.2 92.3 92.7 (b)SegmentationAblations.

However,itisnon-trivialtopromote such a pretraining-tuning paradigm to the 3D vision, given the limited training data that are relatively inconvenient to collect.

Compared withconvolutional neural networks (CNNs), the training ofAdderNets ismuch more sophisticated including several techniques for adjusting gradient and batch normalization.

In Restless-UCB, we present a novel method to construct offline instances,whichonlyrequiresO(N)time-complexity(N isthenumberofarms) and is exponentially better than the complexity of existing learning policy.

In Restless-UCB, we present a novel method to construct offline instances,whichonlyrequiresO(N)time-complexity(N isthenumberofarms) and is exponentially better than the complexity of existing learning policy.