Just-In-Time Learning for Fast and Flexible Inference - Eslami, Ali; Tarlow, Daniel; Kohli, Pushmeet; Winn, John (NIPS'14 Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014)
Much of research in machine learning has centered around the search for inference algorithms that are both general-purpose and efficient. The problem is extremely challenging and general inference remains computationally expensive. We seek to address this problem by observing that in most specific applications of a model, we typically only need to perform a small subset of all possible inference computations. Motivated by this, we introduce just-in-time learning, a framework for fast and flexible inference that learns to speed up inference at run-time. Through a series of experiments, we show how this framework can allow us to combine the flexibility of sampling with the efficiency of deterministic message-passing.