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Found 2 papers, 1 papers with code
TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction
For the branch of cell lines genomics, we use the multi-headed attention mechanism to globally represent the genomics sequence.
Ranked #1 on
Drug Response Prediction
on GDSC
Object-Centric Representation Learning with Generative Spatial-Temporal Factorization
We train DyMON on multi-view-dynamic-scene data and show that DyMON learns -- without supervision -- to factorize the entangled effects of observer motions and scene object dynamics from a sequence of observations, and constructs scene object spatial representations suitable for rendering at arbitrary times (querying across time) and from arbitrary viewpoints (querying across space).
