We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Abstract. Semi-supervised clustering models, that incorporate user provided constraints to yield meaningful clusters, have recently become a popular area of research. In this paper...
Most of the existing clock synchronization algorithms for wireless sensor networks can be viewed as proactive clock synchronization since they require nodes to periodically synchro...
—This paper presents the first hierarchical Byzantine fault-tolerant replication architecture suitable to systems that span multiple wide area sites. The architecture confines ...
Yair Amir, Claudiu Danilov, Danny Dolev, Jonathan ...