Hawkes processes, in terms of both computational efficiency and accuracy.

Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers.

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We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PFA) modules. The model is composed of a Poisso n distribution to model observed vectors of counts, as well as a deep hierarchy of hidden binary units. Rather than using logistic functions to characteriz e the probability that a latent binary unit is on, we employ a Bernoulli-Poisson link, which allows PFA modules to be used repeatedly in the deep architecture. We al so describe an approach to build discriminative topic models, by adapting PF A modules. We derive efficient inference via MCMC and stochastic variational met hods, that scale with the number of non-zeros in the data and binary units, yieldin g significant efficiency, relative to models based on logistic links. Experim ents on several corpora demonstrate the advantages of our model when compared to rel ated deep models.

This paper proposes a framework for learning features that are robust to data variation, which is particularly important when only a limited number of training samples are available. The framework makes it possible to tradeoff the discriminative value of learned features against the generalization error of the learning algorithm. Robustness is achieved by encouraging the transform that maps data to features to be a local isometry. This geometric property is shown to improve (K,null)-robustness, thereby providing theoretical justification for reductions in generalization error observed in experiments. The proposed optimization framework is used to train standard learning algorithms such as deep neural networks. Experimental results obtained on benchmark datasets, such as labeled faces in the wild, demonstrate the value of being able to balance discrimination and robustness.

All methods are run five times, with mean and standard deviation reported.Method Final average accuracy Forward transfer Backward transferEWC 71.

The ability to quickly adapt to changing environments is an important quality of intelligent systems. For such quick adaptation, it is important to be able to identify, memorise, and recall useful past experiences when acquiring new ones.

Neural Information Processing Systems http://nips.cc/