Zobrazeno 1 - 10
of 310
pro vyhledávání: '"Lu, Xicheng"'
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large la
Externí odkaz:
http://arxiv.org/abs/2403.17532
Asynchronous pipeline model parallelism with a "1F1B" (one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, the "1F1B" schedule inevitably leads to weight inconsistency and weight
Externí odkaz:
http://arxiv.org/abs/2312.00839
Publikováno v:
Shipin Kexue, Vol 44, Iss 24, Pp 245-252 (2023)
The differences in metabolites and related metabolism pathways in colostrum and mature milk from Saanen goats at different lactation stages were explored by untargeted metabolomics based on ultra-high performance liquid chromatography-quadrupole elec
Externí odkaz:
https://doaj.org/article/e3e667313baa4e5c9c62ba275317222d
Gradient-based optimization methods implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the high communication overhead for exch
Externí odkaz:
http://arxiv.org/abs/2112.04088
We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-GPU DNN training. XPipe is designed to use multiple GPUs to concurrently and continuously train different parts of a DNN model. To improve GPU utilization and a
Externí odkaz:
http://arxiv.org/abs/1911.04610
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection. However, it is quite challenging to solve the nonconvex penali
Externí odkaz:
http://arxiv.org/abs/1809.03655
Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble
Externí odkaz:
http://arxiv.org/abs/1804.04606
In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function. It has been recognized
Externí odkaz:
http://arxiv.org/abs/1801.06934
Publikováno v:
In Neurocomputing 7 May 2021 435:264-272
Akademický článek
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