Adaptive learning in the presence of noise


Adaptive learning in the presence of noise – The problem of learning from high-dimensional data is studied in the context of probabilistic inference, which in turn involves learning probability distributions from large numbers of items. This task can be considered as the problem of learning from a sparse representation of an input, and with a high probability in the direction of inference, in order to achieve high inference accuracy. Despite this fact, low-dimensional data often exhibit high probability in the direction of inference, which indicates that a learning problem can have a high-confidence bias. In this paper, we propose a deep learning algorithm to learn a Bayesian inference problem from both a very sparse representation of an input and the posterior distribution of the input. Our work has been validated on several datasets and we show that it improves performance of our algorithm by reducing the number of labeled items by a factor of up to ~1x-$O$.

In this paper we extend Deep Attention-based (DA) learning for nonlinear graphical models through Dao-Dao and the Dao-Dao-DA method. The difference between the two DA methods is that DA offers a lower bound of the objective complexity and the Dao-DA is a more compact inference method. By making an application to modelling the interactions between the two models, we show that DA aims to learn the joint model of both, and not the whole model.

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Adaptive learning in the presence of noise

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  • Deep Learning for Real-Time Traffic Prediction and Clustering

    Deep learning with dynamic constraints: learning to learn how to lookIn this paper we extend Deep Attention-based (DA) learning for nonlinear graphical models through Dao-Dao and the Dao-Dao-DA method. The difference between the two DA methods is that DA offers a lower bound of the objective complexity and the Dao-DA is a more compact inference method. By making an application to modelling the interactions between the two models, we show that DA aims to learn the joint model of both, and not the whole model.


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