Search papers, labs, and topics across Lattice.
Affiliation:
4
0
8
0
Pruning models can lead to smaller, more accurate prediction sets without sacrificing reliability, as shown by CPP's impressive performance on large-label tasks.
The latest AI City Challenge reveals that integrating foundation models with synthetic data design can significantly boost performance in complex urban scenarios.
Uniform bagging's performance degrades significantly with Markov-dependent data, but a novel spectral routing algorithm can restore minimax optimality without knowledge of the mixing time.
Forget accuracy: a spectral "fingerprint" derived from Random Matrix Theory can reveal hidden overfitting in your model, even when standard metrics look good.
