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Found 5 papers, 4 papers with code
Effective Quantization for Diffusion Models on CPUs
Diffusion models have gained popularity for generating images from textual descriptions.
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computationalast layer demands of these modern architectures while maintaining the accuracy.
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
As the number of bits decreases, the quantization grid broadens, thus emphasizing the importance of up and down rounding.
Uncertainty-Aware Cross-Modal Transfer Network for Sketch-Based 3D Shape Retrieval
UACTN decouples the representation learning of sketches and 3D shapes into two separate tasks: classification-based sketch uncertainty learning and 3D shape feature transfer.
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
