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ORCID1st EuroMLSys@EuroSys 2021: Virtual Event, UK
- Eiko Yoneki, Paul Patras:
EuroMLSys@EuroSys 2021, Proceedings of the 1st Workshop on Machine Learning and Systemsg Virtual Event, Edinburgh, Scotland, UK, 26 April, 2021. ACM 2021, ISBN 978-1-4503-8298-4 - Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal, Shlomo Dubnov
:
DPD-InfoGAN: Differentially Private Distributed InfoGAN. 1-6 - Gagan Somashekar, Anshul Gandhi:
Towards Optimal Configuration of Microservices. 7-14 - Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew W. Fitzgibbon, Tim Harris:
DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks. 15-23 - Thomas Schmied, Diego Didona, Andreas C. Döring, Thomas P. Parnell, Nikolas Ioannou:
Towards a General Framework for ML-based Self-tuning Databases. 24-30 - Thomas Wang, Simone Ferlin, Marco Chiesa:
Predicting CPU usage for proactive autoscaling. 31-38 - Iulia Paun, Yashar Moshfeghi, Nikos Ntarmos:
Are we there yet? Estimating Training Time for Recommendation Systems. 39-47 - Octavian Machidon, Davor Sluga, Veljko Pejovic:
Queen Jane Approximately: Enabling Efficient Neural Network Inference with Context-Adaptivity. 48-54 - Sina Sheikholeslami, Moritz Meister, Tianze Wang, Amir Hossein Payberah, Vladimir Vlassov, Jim Dowling:
AutoAblation: Automated Parallel Ablation Studies for Deep Learning. 55-61 - Daniel Goodman, Adam Craig Pocock, Jason Peck, Guy L. Steele Jr.:
Vate: Runtime Adaptable Probabilistic Programming for Java. 62-69 - Edgar Liberis, Lukasz Dudziak, Nicholas D. Lane:
μNAS: Constrained Neural Architecture Search for Microcontrollers. 70-79 - Daniel Mendoza, Francisco Romero, Qian Li, Neeraja J. Yadwadkar, Christos Kozyrakis:
Interference-Aware Scheduling for Inference Serving. 80-88 - Kai Zhu, Wenyi Zhao, Zhen Zheng, Tianyou Guo, Pengzhan Zhao, Junjie Bai, Jun Yang, Xiaoyong Liu, Lansong Diao, Wei Lin:
DISC: A Dynamic Shape Compiler for Machine Learning Workloads. 89-95 - Ahmed M. Abdelmoniem, Marco Canini:
Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. 96-103 - Rik Mulder, Valentin Radu, Christophe Dubach:
Fast Optimisation of Convolutional Neural Network Inference using System Performance Models. 104-110 - Sami Alabed, Eiko Yoneki:
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB. 111-119 - Hanan Hindy, Christos Tachtatzis, Robert C. Atkinson, Ethan Bayne, Xavier J. A. Bellekens:
Developing a Siamese Network for Intrusion Detection Systems. 120-126
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