Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
Visualizing data using t-
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.
High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.
citing papers explorer
-
Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
-
Practical validation of synthetic pre-crash scenarios
A binning-based Bayesian ROPE equivalence testing method is introduced to quantitatively assess practical equivalence between synthetic and real pre-crash scenario datasets for driving automation safety impact evaluation.
-
Stable Routing for Mixture-of-Experts in Class-Incremental Learning
StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.
-
Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.