An initial approach that first aligns heterogeneous table-cell representations into a shared space using Hirschfeld–Gebelein–Rényi maximal correlation (HGR) is proposed, and it is found that it generalizes competitively compared to models specifically designed for individual tasks.
A systematic evaluation of table-level embeddings is introduced that captures several complementary properties required for downstream effectiveness, demonstrating that table-level embedding quality cannot be reduced to retrieval alone.
This work introduces Giga-Embeddings, a family of text embedding models designed to combine strong retrieval quality with efficient serving, and trains the compact model using a dimension-agnostic objective that aligns teacher and student similarity distributions.
Egor Kolodin, Egor Krasnoperov, Evgeniy Kosarev et al.· 0 citations
WeMM-Embedding is presented, a family of universal multimodal embedding models supporting text, images, videos, visual documents, and arbitrarily interleaved multimodal inputs with flexible output dimensions and achieves leading performance on multiple public benchmarks.
LSR retains a general encoder as a transferable semantic substrate while explicit modules reorganize, augment, or index its representations for a defined operational objective.
Pedro Emílio Amador Salomão· Nexus Science Review· 0 citations
This work proposes H2Table (Hierarchical Hypergraph-Enhanced Table Reasoning), a novel framework that represents complex tables as hierarchical nested hypergraphs, and designs a tailored hypergraph encoder to facilitate message passing between hyperedges and nodes within complex tables.
Jia Ling, Yang-Fan Wang, Chen Tang et al.· 0 citations
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