ICML2017 Accepted Paper List DeepNLP AI Robotic and STEM Top Conference & Journal Papers
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This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit coding scheme that achieves the optimal tradeoff based on recursive polynomial constructions, coding both across data subsets and vector components. As a result, the proposed scheme allows to minimize the running time for gradient computations. Implementations are made on Amazon EC2 clusters using Python with mpi4py package. Results show that the proposed scheme maintains the same generalization error while reducing the running time by $32%$ compared to uncoded schemes and $23%$ compared to prior coded schemes focusing only on stragglers (Tandon et al., ICML 2017).
Introduction
Conference ICML2017 accepted paper complete List. Top ranking conferences for AI and Robotics communities. Total Accepted Paper Count 1