Collections
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Collections trending this week
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What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Paper • 2407.03320 • Published • 93 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 134
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Associative Recurrent Memory Transformer
Paper • 2407.04841 • Published • 36 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 58 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 67 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 262
-
Associative Recurrent Memory Transformer
Paper • 2407.04841 • Published • 36 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 58 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 67 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 262
-
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Paper • 2407.03320 • Published • 93 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 134