Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity.

Diffusion models have significantly advanced the field of generative modeling.

These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.

It also extends the traditional diffusion-based model, enabling the generation of samples conditioned on the block maxima, thereby enhancing the model's

Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community.

Our analysis involved a two-phase process.

Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformers have dramatically advanced the landscape of forecasting, their effectiveness remains debated.