Search papers, labs, and topics across Lattice.
Affiliation:
4
0
6
TESTNAV achieves up to 2.15x faster exploration of compositional perturbation spaces while ensuring realistic and severe input failures are prioritized.
TestifAI can predict the robustness of deep learning models against complex perturbations with remarkable accuracy while slashing testing costs by up to 80%.
Bridging the gap between coarse atmospheric models and local PM$_{2.5}$ variations, this framework achieves a 40x super-resolution without relying on temporal data.
Rectified flows can generate synthetic skin lesion images that boost classification accuracy by up to 9% compared to diffusion models, offering a promising solution to data scarcity in dermatology.
