A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

It is frequently desirable to display the results of computation in a graphical form. This is often done through the use of special hardware such as digital X,Y-plotters. Programmed graphical output ...

ABSTRACT: Purpose: To introduce a practical method of using an Electron Density Phantom (EDP) to evaluate different dose calculation algorithms for photon beams in a treatment planning system (TPS) ...

Large Language Models (LLMs) significantly benefit from attention mechanisms, enabling the effective retrieval of contextual information. Nevertheless, traditional attention methods primarily depend ...

Learning to budget effectively changed my financial life. Before I lived on a budget, I sometimes came up short at the end of the month — and I rarely knew where my money was going. However, once I ...

When compiling the sample code for examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu, it fails with the following error message. Any other ...