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BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details.
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why?
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
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.
Learning Elastic Costs to Shape Monge Displacements
Given a source and a target probability measure, the Monge problem studies efficient ways to map the former onto the latter. This efficiency is quantified by defining a cost function between source and target data.
The Sea Surface Height Edition J. Emmanuel Johnson
The ocean is a crucial component of the Earth's system.
