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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
Collections
Discover the best community collections!
Collections including paper arxiv:2508.00367
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MambaVision: A Hybrid Mamba-Transformer Vision Backbone
Paper • 2407.08083 • Published • 34 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 63 -
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
Paper • 2408.15237 • Published • 42 -
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Paper • 2409.11355 • Published • 30
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Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
Paper • 2501.16372 • Published • 12 -
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Paper • 2501.16937 • Published • 7 -
Matryoshka Quantization
Paper • 2502.06786 • Published • 32 -
Identifying Sensitive Weights via Post-quantization Integral
Paper • 2503.01901 • Published • 8
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Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
Paper • 2501.16372 • Published • 12 -
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Paper • 2501.16937 • Published • 7 -
Matryoshka Quantization
Paper • 2502.06786 • Published • 32 -
Identifying Sensitive Weights via Post-quantization Integral
Paper • 2503.01901 • Published • 8
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
-
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
Paper • 2501.16372 • Published • 12 -
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Paper • 2501.16937 • Published • 7 -
Matryoshka Quantization
Paper • 2502.06786 • Published • 32 -
Identifying Sensitive Weights via Post-quantization Integral
Paper • 2503.01901 • Published • 8
-
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
Paper • 2501.16372 • Published • 12 -
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Paper • 2501.16937 • Published • 7 -
Matryoshka Quantization
Paper • 2502.06786 • Published • 32 -
Identifying Sensitive Weights via Post-quantization Integral
Paper • 2503.01901 • Published • 8
-
MambaVision: A Hybrid Mamba-Transformer Vision Backbone
Paper • 2407.08083 • Published • 34 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 63 -
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
Paper • 2408.15237 • Published • 42 -
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Paper • 2409.11355 • Published • 30