A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

Abstract: Federated learning has emerged as a key paradigm for distributed optimization under privacy and communication constraints, yet classical aggregation schemes such as Federated Averaging ...

The forward and inverse kinematics networks of the data-driven path share a residual MLP backbone: MLP architecture for kinematics approximations. Inputs are projected into a 128-dim latent space and ...

Efficiently steering generative models toward pharmacologically relevant regions of chemical space is a major challenge in drug discovery under low-data regimes. We present VECTOR+ ...