We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the ...
We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the ...
A recent lifting technique led to a computationally efficient Model Predictive Control (MPC) strategy in which the online optimization is performed using a univariate Newton-Raphs...
In this paper, we propose the first metal-density driven placement algorithm to reduce CMP variation and achieve higher routability. Based on an analytical placement framework, we...
Tung-Chieh Chen, Minsik Cho, David Z. Pan, Yao-Wen...
Abstract. This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN c...