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Lightweight Convolutional Representations for On-Device Natural Language Processing
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<span title="2020-02-04">2020</span>
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Table 2. ...
Future work will explore the viability of this representation in more language tasks. 1 The University of Texas at Austin 2 Facebook Assistant. ...
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Efficient Memory Management for Deep Neural Net Inference
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2020 Conference, Austin, TX, USA, 2020. ...
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Optimizing JPEG Quantization for Classification Networks
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Fig. 1 1 shows an overview of the standard JPEG compression 1 Cornell University.Correspondence to: Zhijing Li <zl679@cornell.edu>.Resource-Constrained Machine Learning (ReCoML) Workshop of MLSys 2020 ...
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MNN: A Universal and Efficient Inference Engine
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Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
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Submitted to Proceedings of the 3 rd MLSys Conference, Austin, TX, USA, 2020. Copyright 2020 by the author(s). 2018), and distillation (Polino et al., 2018) have been developed and deployed. ...
Hence, a number of model compression techniques (Gupta & Agrawal, 2020) such as pruning, quantization (Choi et al., 1 University of Texas, Austin 2 Facebook Inc. ...
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On-device Federated Learning with Flower
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<span title="2021-04-07">2021</span>
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Correspondence to: Akhil Mathur <akhilmathurs@gmail.com>.Proceedings of the 3 rd MLSys Conference, Austin, TX, USA, 2020. Copyright 2020 by the author(s). ...
TFF (Google, 2020) , PySyft (Ryffel et al., 2018) , LEAF (Caldas et al., 2018) , FedML (He et al., 2020) are other open-source frameworks that support research and experimentation of FL workloads. ...
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AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
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Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
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MLPerf Training Benchmark
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Federated Optimization in Heterogeneous Networks
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At each iteration, FedAvg first locally performs E epochs of stochastic gra-Proceedings of the 3 rd MLSys Conference, Austin, TX, USA, 2020. ...
Copyright 2020 by the author(s). 1 Privacy is a third key challenge in the federated setting. ...
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Differentially Private Federated Learning on Heterogeneous Data
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In Proceedings of the 3rd MLSys Conference, Austin, TX, USA, 2020a. arXiv: 1812.06127. Yurii Nesterov et al. Lectures on convex optimization, volume 137. Springer, 2004. ...
., 2020; Karimireddy et al., 2020b) . ...
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Multiplying Matrices Without Multiplying
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