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MICRO
2010
IEEE
238views Hardware» more  MICRO 2010»
15 years 4 months ago
Sampling Dead Block Prediction for Last-Level Caches
Last-level caches (LLCs) are large structures with significant power requirements. They can be quite inefficient. On average, a cache block in a 2MB LRU-managed LLC is dead 86% of ...
Samira Manabi Khan, Yingying Tian, Daniel A. Jimen...
JMLR
2010
125views more  JMLR 2010»
15 years 1 months ago
Regret Bounds for Gaussian Process Bandit Problems
Bandit algorithms are concerned with trading exploration with exploitation where a number of options are available but we can only learn their quality by experimenting with them. ...
Steffen Grünewälder, Jean-Yves Audibert,...
ICNC
2005
Springer
15 years 12 months ago
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
Abstract. Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focusses on finding a neu...
Joseph P. Herbert, Jingtao Yao
ECCC
2006
87views more  ECCC 2006»
15 years 6 months ago
The Learnability of Quantum States
Traditional quantum state tomography requires a number of measurements that grows exponentially with the number of qubits n. But using ideas from computational learning theory, we...
Scott Aaronson
AAAI
2008
15 years 8 months ago
Instance-level Semisupervised Multiple Instance Learning
Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-...
Yangqing Jia, Changshui Zhang