Despite their promising performance, the learned knowledge remains implicit in these black-box neural structures, which hinders understanding the importance of input features and how they influencedecisions.

A plant's phenome is defined by its physical and biochemical characteristics, and is the result of the interaction of its genome and its environment.

For the latter, PT -based methods adaptively extract a matching width-based slimmed-down sub-model from the global model as a local model according to each client's budget, thus averting the requirements for public data. As with FedAvg, PT -based methods require the server to periodically communicate with the clients. Existing PT -based methods focus on how to extract width-based sub-models from the global model. DFKD methods are promising, which transfer knowledge from the teacher model to another student model without any real data. Existing DFKD methods can be broadly classified into non-adversarial and adversarial training methods. They take the quality and/or diversity of the synthetic data as important objectives.

Bayesian black-box function paradigmknowledg expected [33,25].

We address the issue of physical implausibility in multi-view neural reconstruction. While implicit representations have gained popularity in multi-view 3D reconstruction, previous work struggles to yield physically plausible results, limiting their utility in domains requiring rigorous physical accuracy.

Experimental results show that vanilla OSOA can save significant time versus training bespokemodels and space versus using one model for all targets.