Designing a Kubernetes Operator for Machine Learning Applications
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Multi-Tenant Machine Learning Platform Based on Kubernetes
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Hyper-Parameter Optimization: A Review of Algorithms and Applications
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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
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Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing
Ming Li, Ziqian Bi, Tianyang Wang*, Et al.
A System for Massively Parallel Hyperparameter Tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Et al.
Hippo: Sharing Computations in Hyper-Parameter Optimization
Ahnjae Shin, Joo Seong Jeong, Do Yoon Kim, Et al.
Delta-DNN: Efficiently Compressing Deep Neural Networks via Exploiting Floats Similarity
Zhenbo Hu, Xiangyu Zou, Wen Xia, Et al.
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Kevin Jamieson, Ameet Talwalkar
Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
Shuhei Watanabe
A Scalable and Cloud-Native Hyperparameter Tuning System
Johnu George, Ce Gao, Richard Liu, Et al.
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Et al.
Attention Is All You Need
George Orwell
TAPAS: Weakly Supervised Table Parsing via Pre-training
Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
The Era of 1-bit LLMs
Shuming Ma, Hongyu Wang, Lingxiao Ma, Et al.
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