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Quantization after training (PTQ) Focuses on reducing the size and improving the speed of large language models (LLMs) to make them more practical for use in the real world. Such models require large amounts of data, but highly crooked and very heterogeneous data distribution during quantization causes significant difficulties. This would inevitably expand the quantization … Read more

The field of artificial intelligence is developing rapidly with increasing efforts to develop more skilled and effective language models. However, scaling of these models comes with challenges, especially with regard to calculation resources and the complexity of training. The research community is still investigating best practices for scaling extremely large models, whether they use a … Read more

Researchers have highlighted concern about hallucinations in LLMs because of their generation of plausible but inaccurate or non -related content. However, these hallucinations have potential in creativity -driven fields such as discovery of drugs where innovation is important. LLMs have been widely used in scientific domains, such as material science, biology and chemistry, help with … Read more

Synthesis of new visions has witnessed significant advances recently, with Neural Radiance Fields (NeRF) pioneering 3D representation techniques through neural rendering. While NeRF introduced innovative methods to reconstruct scenes by accumulating RGB values ​​along sampling rays using multilayer perceptrons (MLPs), it encountered significant computational challenges. The extensive ray point sampling and large neural network volumes … Read more