Tag
Proposes LieBN, a framework for batch normalization over Lie groups, applicable to SPD, rotation, and correlation manifolds, with theoretical guarantees and extensive experiments.
This paper reveals that PPO-Clipping's use of Euclidean metric causes exploration collapse in LLM RL, and proposes Riemannian Isometric Policy Optimization (RIPO) to ensure geometrically consistent policy updates, achieving up to 60% improvement over GRPO on AIME24.
The paper introduces Riemannian Mean Pooling (RMP), a method that aggregates token embeddings from pre-trained language models using Riemannian geometry via pullback metrics, showing improved performance over Euclidean pooling on sentence classification tasks.
I²RiMA is a novel intra-inter Riemannian manifold attention network for EEG-based mental stress detection. It constructs frequency-specific spatial covariance and uses temporal attention to improve cross-subject stress classification, achieving up to 82.78% balanced accuracy.
SPDM introduces a geometry-aware state-space model that uses manifold constraints on the symmetric positive definite manifold for time series forecasting, achieving state-of-the-art performance on 11 benchmarks.
This paper introduces a Riemannian version of archetypal analysis using data-driven pullback geometry to combine interpretability with non-linear expressiveness, proposing the Riemannian Archetypal Mapping (RAM) and demonstrating its effectiveness on synthetic data and MNIST.