Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
Many 0/1 datasets have a very large number of variables; however, they are sparse and the dependency structure of the variables is simpler than the number of variables would sugge...
Clock selection has a significant impact on the performance and quality of designs in high-level synthesis. In most synthesis systems, a convenient value of the clock is chosen or...
J. Ramanujam, Sandeep Deshpande, Jinpyo Hong, Mahm...
Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and ...