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:
We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.
answer:
Collaborative Filtering with Recurrent Neural Networks


question:
The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle’s physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDARvideo inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.
answer:
Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks


question:
Unsupervised training of deep generative models containing latent variables and performing inference remains a challenging problem for complex, high dimensional distributions. One basic approach to this problem is the so called Helmholtz machine and it involves training an auxiliary model that helps to perform approximate inference jointly with the generative model which is to be fitted to the training data. The top-down generative model is typically realized as a directed model that starts from some prior at the top, down to the empirical distribution at the bottom. The approximate inference model runs in the opposite direction and is typically trained to efficiently infer high probability latent states given some observed data. Here we propose a new method, referred to as geometric mean matching (GMM), that is based on the idea that the generative model should be close to the class of distributions that can be modeled by our approximate inference distribution. We achieve this by interpreting both the top-down and the bottom-up directed models as approximate inference distributions and by defining the target distribution we fit to the training data to be the geometric mean of these two. We present an upper-bound for the log-likelihood of this model and we show that optimizing this bound will pressure the model to stay close to the approximate inference distributions. In the experimental section we demonstrate that we can use this approach to fit deep generative models with many layers of hidden binary stochastic variables to complex and high dimensional training distributions.
answer:

Training opposing directed models using geometric mean matching