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.

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

In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user.

Image Signal Processors (ISPs) play a fundamental role in camera systems.

To overcome this challenge, diverging from conventional efforts of expanding the solver's search scope, we focus on enabling information to actively propagate to the solver through heat diffusion.

We find that it significantly improves the base systems'

Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost.

Multi-task learning is a promising paradigm for handling several tasks simultaneously using a unified architecture.

Through extensive empirical analysis using benchmarks in the Safety Gym suite, we show that our algorithm has similar or better performance than SoT A (non-episodic) algorithms adapted for the episodic setting.