However, in domains where precise and succinct expert state information is available, agents trained onsuchexpert state features usually outperform agents trained onrichobservations.

Inthiswork,weconsider thescenario where agent limitations may entirely preclude identifying an exactly value-equivalent model, immediately giving rise to a trade-off between identifying a model that is simple enough to learn while only incurring bounded sub-optimality.

Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines.

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using textdescriptions orreference images, while preserving irrelevant attributes(e.g.,identity,background,cloth).

Robotics,autonomousdriving,augmentedreality,andmanyembodiedcomputer vision applications must quickly react to user-defined events unfolding in real time.

This allows us to learn high-precision embeddings without using BigFloats, and enables us to store the resulting embeddings with fewer bits.

We study a dynamic version of the implicit trace estimation problem.

Neural implicit function based on signed distance field (SDF) has achieved impressiveprogress inreconstructing 3Dmodels withhighfidelity. However,such approaches canonlyrepresent closed surfaces.

To promote better performance-bandwidth trade-off for multi-agent perception, weproposeanovel distilledcollaborationgraph (DiscoGraph)tomodeltrainable, pose-aware, and adaptive collaboration among agents. Our key novelties lie in twoaspects.

Alternatively,theyrequire large-scale training data and longer training schedules to learn the IB implicitly.