RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
When is unsupervised disentanglement possible?Advances in Neural Information Processing Systems, 34:5150–5161
2 Pith papers cite this work. Polarity classification is still indexing.
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EC-Net combines Poincare-ball hyperbolic embeddings, hypergraph fusion, and decoupled radial-angular contrastive learning to improve accuracy on multimodal emotion benchmarks especially under partial or noisy modalities.
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Disentanglement Beyond Generative Models with Riemannian ICA
RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
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Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
EC-Net combines Poincare-ball hyperbolic embeddings, hypergraph fusion, and decoupled radial-angular contrastive learning to improve accuracy on multimodal emotion benchmarks especially under partial or noisy modalities.