Statistical Learning
Semi-supervisedly Co-embedding Attributed Networks
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao
Figure 1: OurA), theattribX) and labels (Yl) asinput, and attributes (Zn and Za), aswellasYu).
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness Y ang Qian
Despite these advancements, QAOA's practical efficacy is challenged by the quantum coherence limits of modern quantum devices, as there is a ceiling on the allowable maximum circuit depth
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing " Spurious " Correlations
"label shift" assumption, where the labels now also include the nuisance factors