Integrates dynamic codebook frequency statistics into a transformer attention module. Fuses semantic image features with latent representations of quantization ...
Explore how Quantization Aware Training (QAT) and Quantization Aware Distillation (QAD) optimize AI models for low-precision environments, enhancing accuracy and inference performance. As artificial ...
I am doing LoRA finetuning on a whisper model on multiple GPU's using Deepspeed Zero 3. I am able to finetune the model at float32 precision without any quantization. However, when I quantise the ...
ENOB describes an analog-to-digital converter’s performance with respect to total noise and distortion. In the earlier parts of this series on analog-to-digital converters (ADCs), we looked at the ...
ABSTRACT: Elderly individuals undergoing long-term neuroleptic therapy are increasingly vulnerable to cognitive decline, a condition that significantly impairs quality of life and increases healthcare ...
Specifications such as gain error, offset error, and differential nonlinearity help define an analog-to-digital converter’s performance. In part 1 of this series, we discussed an ideal ...
Abstract: Conventional quantization-based data hiding algorithms used uniform quantization. This scheme may be easily estimated by averaging on a set of embedded signals. Furthermore, by uniform ...
Abstract: Since most depth maps are quantized to 8-bit numbers in current 3D video systems, the induced cardboard effects can disturb human perception. Moreover ...
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