scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
citing papers explorer
-
scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.
-
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.