DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
Poincar\'e GloVe: Hyperbolic Word Embeddings
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy.
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CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.
HyFL-CLIP distills Euclidean CLIP alignment into hyperbolic space using cross-manifold similarity and Einstein midpoint aggregation to capture hierarchical part-whole relations, achieving up to 19.5% gains in long-text retrieval under perturbations.
A fully hyperbolic attention model using Busemann energy in Poincaré discs produces emotion predictions from text that generalize well even at low embedding dimensions.
citing papers explorer
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DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
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Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding
CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
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Group-Equivariant Poincar\'e Convolutional Networks
Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.
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HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding
HyFL-CLIP distills Euclidean CLIP alignment into hyperbolic space using cross-manifold similarity and Einstein midpoint aggregation to capture hierarchical part-whole relations, achieving up to 19.5% gains in long-text retrieval under perturbations.
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Busemann energy-based attention for emotion analysis in Poincar\'e discs
A fully hyperbolic attention model using Busemann energy in Poincaré discs produces emotion predictions from text that generalize well even at low embedding dimensions.