Abstract. Designing and tuning parallel applications with MPI, particularly at large scale, requires understanding the performance implications of different choices of algorithms ...
Torsten Hoefler, William Gropp, Rajeev Thakur, Jes...
Abstract. In supervised learning, discretization of the continuous explanatory attributes enhances the accuracy of decision tree induction algorithms and naive Bayes classifier. M...
Abstract. Shared counters are the key to solving a variety of coordination problems on multiprocessor machines, such as barrier synchronization and index distribution. It is desire...
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...