Diffusion Transformers (DiTs) are driving advancements in high-quality image and video generation. With the escalating input context length in DiTs, the computational demand of the Attention mechanism ...
To speed up computation, deep neural networks (DNNs) usually rely on highly optimized tensor operators. Despite the effectiveness, tensor operators are often defined empirically with ad hoc semantics.
Abstract: The problem of estimating zero-sequence parameters of a parallel transmission line from fault data is considered. This paper analytically demonstrates that the zero-sequence impedances of a ...
Abstract: With the advance of smart manufacturing and information technologies, the volume of data to process is increasing accordingly. Current solutions for big data processing resort to distributed ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
Comprehensive Training Pipelines: Full support for Diffusion Language Models (DLMs) and Autoregressive LMs, from pre-training and SFT to RL, on both dense and MoE architectures. We strongly recommend ...
Learn how to use advanced techniques like short-circuiting, parallel execution, virtual threads, and stream gatherers to maximize Java stream performance. My recent Java Stream API tutorial introduced ...
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