Data sampling methods have been investigated for decades in the context of machine learning and statistical algorithms, with significant progress made in the past few years driven by strong interest in big data and distributed computing. Most recently, progress has been made in methods that can be...
大体上看,ps是一个数据并行的系统框架,数据和梯度计算都放在worker节点上,而server节点负责保存和维护全局共享的参数,其中,参数以稀疏/稠密的向量/矩阵表示。该框架通过异步的方式进行节点之间的数据通信,此外也支持灵活的一致性模型、弹性的可扩展性和持续的容灾。 2 痛点和解决思路 ps支持海量参数( 109 ~ 1012 )...
Scaling Distributed Machine Learning with In-Network Aggregation 摘要 此片论文主要设计了一种交换机SwitchML,通过将网络上多个worker的模型更新在交换机中aggregate来降低数据传递来的巨大的开销。 三个挑战 有限的计算能力:当今的可编程交换机只能处理整数,并且无法进行除法。 有限的存储能力:每次迭代可能都有数百MB...
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset size...
Brown, R. D. Non-linear mapping for improved identification of 1300+ languages. InProc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)(eds Moschitti, A. et al.) 627–632 (ACL, 2014). Caswell, I., Breiner, T., van Esch, D. & Bapna, A. Language ID in...
you can set up scaling plans for different sets of resources, per application. The AWS Auto Scaling console provides recommendations for scaling strategies customized to each resource. After you create your scaling plan, it combines dynamic scaling and predictive scaling methods together to support you...
For a while, we thought we could just do speech recognition with more traditional machine learning models; it turns out, neural networks are particularly good at that. For things like recommender systems, most companies are still using more traditional methods like factorization and even more basic...
Additionally, our previous work only examined the QoS-prioritized neural-network model while in this work, we also examine the cost-prioritized neural-network model and compare the performance of both methods. The output from the best performing MLP model i.e., ’the number of UPF instances’ ...
Methods In this section, we report details of the models considered in the paper and settings for the experiments performed in this paper. We define neural scaling and the model architectures considered here, which are chosen specifically for their likelihood to exhibit interesting scaling behaviour....
Provided are systems, methods and techniques for machine-learning classification. In one representative embodiment, an item having values for a plurality of different features in a feature set is obtained, together with scores for the different features. The score for a given feature is a measure ...