📊 Embeddings - mando · Scour

Embeddings for Preferences, Not Semantics 🔍Vector Databases

SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations 🔍Vector Databases

Bilinear autoencoders find interpretable manifolds 🔍Vector Databases

Topic Is Not Agenda: A Citation-Community Audit of Text Embeddings 🔍Vector Databases

jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers 🔍Vector Databases

Inductive Entity Representations from Text via Link Prediction 🔍Vector Databases

Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data 🔍Vector Databases

Very Efficient Listwise Multimodal Reranking for Long Documents 📚Information Retrieval

Task-Adaptive Embedding Refinement via Test-time LLM Guidance 🤖LLM

FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances 🔍Vector Search

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization 🤖LLM

Bridging Textual Profiles and Latent User Embeddings for Personalization 📚Personal Wikis

Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions 🕸️Knowledge Graphs

FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings 🤖LLM

The Proxy Presumption: From Semantic Embeddings to Valid Social Measures 🔍Vector Databases

Beyond Bag-of-Patches: Learning Global Layout via Textual Supervision for Late-Interaction Visual Document Retrieval 📝Obsidian

Semantic Smoothing for Language Models via Distribution Estimation and Embeddings 🤖LLM

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models 🕸️Graph Databases

Granite Embedding Multilingual R2 Models 🤖LLM

MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph 🤖LLM

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