Privacy preserving distributed training of neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04880-0
Abstract: L earnae is a system aiming to achieve a fully distributed way of neural network training. It follows a “Vires in Numeris” approach, combining the resources of commodity personal computers. It has a full peer-to-peer… read more here.
Keywords: network; privacy preserving; training data; distributed training ... See more keywords
Large-Scale Distributed Training of Transformers for Chemical Fingerprinting
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Chemical Information and Modeling"
DOI: 10.1021/acs.jcim.2c00715
Abstract: Transformer models have become a popular choice for various machine learning tasks due to their often outstanding performance. Recently, transformers have been used in chemistry for classifying reactions, reaction prediction, physiochemical property prediction, and more.… read more here.
Keywords: distributed training; molecular fingerprints; large scale; chemistry ... See more keywords
FPGA-Based AI Smart NICs for Scalable Distributed AI Training Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Computer Architecture Letters"
DOI: 10.48550/arxiv.2204.10943
Abstract: Training state-of-the-art artificial intelligence (AI) models requires scaling to many compute nodes and relies heavily on collective communication operations, such as all-reduce, to exchange the weight gradients between nodes. The overhead of these operations can… read more here.
Keywords: nics scalable; fpga based; distributed training; smart nics ... See more keywords