@inproceedings{eslami2014just-in-time,
author = {Eslami, Ali and Tarlow, Daniel and Kohli, Pushmeet and Winn, John},
title = {Just-In-Time Learning for Fast and Flexible Inference},
booktitle = {NIPS'14 Proceedings of the 27th International Conference on Neural Information Processing Systems},
year = {2014},
month = {December},
abstract = {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.},
publisher = {MIT Press Cambridge},
url = {http://approjects.co.za/?big=en-us/research/publication/just-in-time-learning-for-fast-and-flexible-inference/},
pages = {154-162},
edition = {NIPS'14 Proceedings of the 27th International Conference on Neural Information Processing Systems},
}