DeepMind researchers propose Hierarchical Perceiver (HiP), a model that retains the original Perceiver’s ability to process arbitrary modalities but is faster, can scale up to even more inputs/outputs, reduces the need for input engineering, and improves both efficiency and accuracy on classical computer vision benchmarks.
A research team from Microsoft and NVIDIA leverages the NVIDIA Megatron-LM and Microsoft’s DeepSpeed to create an efficient and scalable 3D parallel system that combines data, pipeline, and tensor-slicing based parallelism, achieving superior zero-, one-, and few-shot learning accuracies and new state-of-the-art results on NLP benchmarks.
A research team from Google Research, University of Cambridge and Alan Turing Institute proposes PolyViT, a single transformer model capable of processing multiple modalities and datasets. PolyViT is parameter-efficient and learns representations that generalize across multiple domains.
On August 5, WeChat AI and Beijing Jiaotong University system developers released the paper WeChat Neural Machine Translation Systems for WMT21, revealing the architecture of their novel neural machine translation (NMT) system and the strategies they adopted to achieve impressive performance in the WMT21 competition.
A Google Research team draws inspiration from two numerical analysis methods — Hierarchical Matrix (H-Matrix) and Multigrid — to address the quadratic complexity problem of attention mechanisms in transformer architectures, proposing a hierarchical attention scheme that has linear complexity in run time and memory.
A research team from Google and the University of California, Berkeley calculates the energy use and carbon footprint of large-scale models T5, Meena, GShard, Switch Transformer and GPT-3, and identifies methods and publication guidelines that could help reduce their CO2e footprint.