Reconstructing past events requires reasoning across long time horizons.

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments.

It is unclear whether distributed errors alone are sufficient for synapses to make coordinated updates to learn complex, nonlinear reward-based learning tasks.

To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details.

These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why?

That which we call a rose by any other name would smell as sweet."

Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.

The OSSE NADIR and SWOT are pseudo-observations generated from the NEMO simulation.