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NVIDIA Developer describes how to use the NVIDIA Transformer Engine to accelerate dropless Mixture‑of‑Experts (MoE) training workloads in JAX, outlining implementation details and considerations for integrating the engine with MoE models. The article situates this work amid recent MoE models such as DeepSeek, Qwen, and Mixtral and discusses practical steps to improve training efficiency.
Google Research (post by Urs Köster) presents AutoBNN, an open-source JAX package available within TensorFlow Probability that replaces Gaussian processes with compositional Bayesian neural networks to automate discovery of interpretable time-series forecasting models, produce uncertainty estimates, and scale more efficiently to large datasets. AutoBNN maps compositional GP kernels to BNN architectures and supports operators analogous to GP addition and multiplication while enabling GPU/TPU acceleration and possible hybrid architectures with deep BNN components.
Croissant is a new ML-oriented metadata format (v1.0) built on schema.org that standardizes description and organization of datasets without changing underlying file formats. The release includes a spec, example datasets, an open-source Python validator/consumer/generator, a visual editor, and a Responsible AI (RAI) vocabulary extension, and is being adopted by Kaggle, Hugging Face, OpenML, indexed by Dataset Search, and made loadable in major frameworks via TensorFlow Datasets.
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