instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown to improve computational efficiency at test time, but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates. Borrowing techniques from the literature on training deep generative models, we present the Wake-Sleep Recurrent Attention Model, a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients. We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation.
answer:
Learning Wake-Sleep Recurrent Attention Models

question:
Dawid, Kjrerulff & Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact lo cal computations in junction trees. Utiliz ing the strengths of both methods, such hy brid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist ex act solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of ex act inference and Gibbs sampling in discrete Bayesian networks. We argue that this com bination calls for an extension of the usual message passing scheme of ordinary junction trees.
answer:
HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

question:
In this paper, we suggest a novel data-driven approach to active learning: Learning Active Learning (LAL). The key idea behind LAL is to train a regressor that predicts the expected error reduction for a potential sample in a particular learning state. By treating the query selection procedure as a regression problem we are not restricted to dealing with existing AL heuristics; instead, we learn strategies based on experience from previous active learning experiments. We show that LAL can be learnt from a simple artificial 2D dataset and yields strategies that work well on real data from a wide range of domains. Moreover, if some domain-specific samples are available to bootstrap active learning, the LAL strategy can be tailored for a particular problem.
answer:
Learning Active Learning from Real and Synthetic Data