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Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals Tingting Dan
Since the PDE in Eq. 5 in the main manuscript is equivalent to the E-L equation of the quadratic
Generalizing CNNs to Graphs with Learnable Neighborhood Quantization
Many important real-world scenarios involve data structured as graphs.
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
Through extensive experiments on prevalent image datasets, we demonstrate the superiority of HASSOD over existing methods, thereby advancing the state of the art in self-supervised object detection. Notably, we improve Mask AR from 20.2 to 22.5 on L VIS, and from 17.0 to 26.0 on SA-1B.
Unveiling
Earlier research highlighted DMs' vulnerability todatapoisoning attacks, butthese studies placed stricter requirements than conventional methods like'BadNets' inimage classification.