Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Zhong, Tianle"'
Deep learning datasets are expanding at an unprecedented pace, creating new challenges for data processing in model training pipelines. A crucial aspect of these pipelines is dataset shuffling, which significantly improves unbiased learning and conve
Externí odkaz:
http://arxiv.org/abs/2312.02368
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models. Addressing this challenge, this study delves deep into the Rotated Tensor Parallelism (
Externí odkaz:
http://arxiv.org/abs/2311.01635
Autor:
Perera, Niranda, Shan, Kaiying, Kamburugamuwe, Supun, Kanewela, Thejaka Amila, Widanage, Chathura, Sarker, Arup, Staylor, Mills, Zhong, Tianle, Abeykoon, Vibhatha, Fox, Geoffrey
The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to process ter
Externí odkaz:
http://arxiv.org/abs/2301.07896
Autor:
Shan, Kaiying, Perera, Niranda, Lenadora, Damitha, Zhong, Tianle, Sarker, Arup, Kamburugamuve, Supun, Kanewela, Thejaka Amila, Widanage, Chathura, Fox, Geoffrey
Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed dataframe system, is developed to facilitate data processing both as
Externí odkaz:
http://arxiv.org/abs/2212.13732