Explore >> Select a destination
|
You are here 
|
www.spiedigitallibrary.org | ||
| | | | |
blog.evjang.com
|
|
| | | | | This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ... | |
| | | | |
wonger.dev
|
|
| | | | | [AI summary] The provided text is a collection of various open-source projects, organizations, and initiatives across multiple domains such as software development, machine learning, education, and security. It includes information about technologies like Linux, Python, JavaScript, and C++, as well as projects like Django, Wagtail, and Catrobat. The text also mentions educational initiatives, research collaborations, and community-driven efforts to promote open-source development and computational thinking. | |
| | | | |
gmd.copernicus.org
|
|
| | | | | Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called multi-scale feature fusion (MFF) that can forecast precipitation with a lead time of up to 3?h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the... | |
| | | | |
www.anyscale.com
|
|
| | | Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from NVIDIA Triton Inference Server software and the NVIDIA TensorRT-LLM library. | ||
