NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
Variational inference with normalizing flows,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
RadProPoser uses a variational encoder-decoder with spectral attention to predict 3D poses and aleatoric uncertainties from radar tensors, achieving 6.425 cm MPJPE on a new benchmark and 5.042 cm on HuPR with calibrated uncertainties.
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
Bayesian neural posterior estimation recovers marginal generation costs from market schedules with credible intervals but shows start-up costs are largely unidentifiable from schedules alone.
citing papers explorer
-
Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
-
RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits
RadProPoser uses a variational encoder-decoder with spectral attention to predict 3D poses and aleatoric uncertainties from radar tensors, achieving 6.425 cm MPJPE on a new benchmark and 5.042 cm on HuPR with calibrated uncertainties.
-
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
-
Bayesian Inference for Estimating Generation Costs in Electricity Markets
Bayesian neural posterior estimation recovers marginal generation costs from market schedules with credible intervals but shows start-up costs are largely unidentifiable from schedules alone.