🔢 Embeddings

Vector Representations, Semantic Search, Similarity, Text Encoding, Neural Networks

Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
arxiv.org·8h
⛰️Gradient Descent
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Beyond the Buzzwords: Making Sense of Vectors, Embeddings, and Similarity Search
pub.towardsai.net·1d
🗺️UMAP
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Semantic search with embeddings in PHP: a hands-on guide using Neuron AI and Ollama
dev.to·2d·
Discuss: DEV
🗺️UMAP
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Toward a Compressed Core of Human Knowledge: The High-Dimensional Vector Network for AI
reddit.com·1d·
Discuss: r/AfterClass
🗺️Manifold Learning
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UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning
paperium.net·1d·
Discuss: DEV
🗺️UMAP
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Detailed Technical Documentation on AI Implementation Logic (Taking Large Language Models as an Example )
nbtab.com·4h·
Discuss: DEV
⛰️Gradient Descent
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Novel idea in vector search: Wormhole vectors
dmitry-kan.medium.com·12h
🗺️UMAP
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Semantic search with embeddings in PHP: a hands-on guide using Neuron AI and Ollama
ollama.com·2d·
Discuss: DEV
🗺️UMAP
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Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks
journals.aps.org·13h
⛰️Gradient Descent
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Dense vs Sparse vs Multi-Vector Embeddings Explained: What Every AI Engineer Should Know
pub.towardsai.net·8h
🗺️UMAP
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Embedding Atlas: Low-Friction, Interactive Embedding Visualization
machinelearning.apple.com·1d
🗺️UMAP
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Vision = Language: I Decoded VLM Tokens to See What AI 'Sees' 🔬
reddit.com·1d·
Discuss: r/LocalLLaMA
🗺️UMAP
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Semantic search with embeddings in JavaScript: a hands-on example using LangChain and Ollama
dev.to·1d·
Discuss: DEV
🗺️UMAP
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Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
arxiv.org·8h
🗺️UMAP
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The Infrastructure of Modern Ran king Systems, Part 2: The Data Layer - Fueling the Models with Feature and Vector Stores
shaped.ai·1d
🔲Zarr
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Fast, Scalable LDA in C++ with Stochastic Variational Inference
github.com·22h·
Discuss: r/cpp
📦Pseudobulk
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From searching to solving: how Vector Databases transform product discovery
dev.to·1d·
Discuss: DEV
🗺️UMAP
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Synthesized Generative Modeling via Graph-Constrained Semantic Embedding
dev.to·1d·
Discuss: DEV
🗺️UMAP
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Spatial Sense: Unleashing Language Models on Location Data by Arvind Sundararajan
dev.to·16h·
Discuss: DEV
🗺️UMAP
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Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
arxiv.org·8h
⛰️Gradient Descent
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