Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
arxiv.orgยท20h
๐Ÿค–Machine Learning
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Automated Figure-Text Alignment & Knowledge Extraction for Scientific Literature
dev.toยท2hยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Beyond the Buzzwords: Making Sense of Vectors, Embeddings, and Similarity Search
pub.towardsai.netยท1d
๐Ÿค–Machine Learning
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UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning
paperium.netยท2dยท
Discuss: DEV
๐Ÿค–Machine Learning
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Novel idea in vector search: Wormhole vectors
dmitry-kan.medium.comยท1d
โ™Ÿ๏ธAlpha-Beta Pruning
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Topographical sparse mapping: A training framework for deep learning models
sciencedirect.comยท4hยท
Discuss: Hacker News
๐Ÿค–Machine Learning
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Semantic search with embeddings in PHP: a hands-on guide using Neuron AI and Ollama
ollama.comยท3dยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Semantic search with embeddings in JavaScript: a hands-on example using LangChain and Ollama
dev.toยท2dยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Detailed Technical Documentation on AI Implementation Logic (Taking Large Language Models as an Example )
nbtab.comยท16hยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Vision = Language: I Decoded VLM Tokens to See What AI 'Sees' ๐Ÿ”ฌ
reddit.comยท2dยท
Discuss: r/LocalLLaMA
๐Ÿ’ฌNatural Language Processing
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Dense vs Sparse vs Multi-Vector Embeddings Explained: What Every AI Engineer Should Know
pub.towardsai.netยท21h
๐Ÿค–Machine Learning
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NeuraSnip A Local Semantic Image Search Engine
github.comยท2dยท
Discuss: r/opensource
๐Ÿ’ฌNatural Language Processing
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Ranking LLMs based on 180k French votes (French government's AI arena)
comparia.beta.gouv.frยท12hยท
Discuss: Hacker News
๐Ÿ’ฌNatural Language Processing
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Spatial Sense: Unleashing Language Models on Location Data by Arvind Sundararajan
dev.toยท1dยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Mitigating Semantic Collapse in Partially Relevant Video Retrieval
arxiv.orgยท1d
๐Ÿ’ฌNatural Language Processing
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From searching to solving: how Vector Databases transform product discovery
dev.toยท1dยท
Discuss: DEV
๐Ÿ’ฌNatural Language Processing
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Efficient Test-Time Retrieval Augmented Generation
arxiv.orgยท20h
๐Ÿ’ฌNatural Language Processing
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Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
arxiv.orgยท20h
๐Ÿค–Machine Learning
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Embedding Atlas: Low-Friction, Interactive Embedding Visualization
machinelearning.apple.comยท2d
๐ŸŒฒB-Trees
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Social and UGC: The trust engines powering search everywhere
searchengineland.comยท11h
๐ŸŒฒB-Trees
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