Tag
This paper proposes modeling the CLIP latent space using Mixtures of von Mises–Fisher distributions on the unit hypersphere, capturing its directional and multimodal structure better than Gaussian assumptions. The model improves long-tailed and out-of-distribution detection and provides a semantic decomposition of CLIP embeddings.
RGLD combines global random-feature density estimation with local neighbor analysis for efficient unsupervised tabular anomaly detection, achieving top AUROC performance on 47 datasets while being 50x-580x faster than deep detectors.
DiScoFormer is a single transformer model that estimates both the density and score of a distribution from a set of data points in one forward pass without retraining, overcoming the trade-off between generalizability and accuracy of previous methods.
This paper presents CALHippo, a framework for 3D mapping of neurons and glial cells in the human hippocampus using state-of-the-art segmentation and density estimation models.
Introduces GORMPO, a density-regularized offline RL algorithm that uses generative density modeling to restrict policy updates to high-density areas, achieving 17% improvement on a real-world medical dataset and outperforming state-of-the-art baselines.
This paper presents PCNet, a probabilistic circuit trained as a tractable density estimator on LLM residual streams to detect hallucinations as geometric anomalies. It also introduces PC-LDCD, a dynamic correction method that only intervenes on hallucinated tokens, achieving near-perfect detection and reduced corruption rates.
FFJORD introduces a scalable reversible generative model using continuous dynamics and Hutchinson's trace estimator to enable unbiased log-density estimation without architectural constraints. The method achieves state-of-the-art results on density estimation and image generation while maintaining efficient sampling.