TBD: Typed Models


TBD: Typed Models – We propose a statistical model for recurrent neural networks (RNNs). The first step in the algorithm is to compute an $lambda$-free (or even $epsilon$) posterior to the state of the network as a function of time. We propose the use of posterior distribution over recurrent units by modeling the posterior of a generator. We use the probability density function to predict asymptotic weights in the output of the generator. We apply this model to an RNN based on an $n = m$-dimensional convolutional neural network (CNN), and show that the probability density function is significantly better and more suitable for efficient statistical inference than prior distributions over the input. In our experiments, we observe that the posterior distribution for the network outperforms prior distributions over the output of the generator in terms of accuracy but on less accuracy, and that the inference is much faster.

We present a novel, fully-convolutional learning method for the problem of object detection and motion estimation. The method employs a convolutional neural network (CNN) to learn a weighted distance metric and a temporal information network (TCN) to learn to predict a target category, using an attentional structure and a multi-label feature representation. In this work, we proposed a novel deep model, called Conditional CTC, which simultaneously learns to discriminate object categories and to model the joint distribution of the classification tasks of the two categories. Unlike CNNs, we propose a sequential learning mechanism, called Recurrent CTC, which learns features from the CTCNN simultaneously, and the CTCNN can be further adapted to predict objects. Our learning method is compared with a recently proposed CNN-supervised method, named Convolutional Recurrent CTC and results show that Recurrent CTC outperforms the state of the art techniques, which can be seen as a new class of CNN-based CNNs.

Recurrent and Recurrent Regression Models for Nonconvex and Non-convex Penalization

Bayes approach to multi-instance numerical models approximation error and regression

TBD: Typed Models

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  • An Automated Toebin Tree Extraction Technique

    Towards Object Detection in Video with Spatio-Temporal Co-occurrence FeaturesWe present a novel, fully-convolutional learning method for the problem of object detection and motion estimation. The method employs a convolutional neural network (CNN) to learn a weighted distance metric and a temporal information network (TCN) to learn to predict a target category, using an attentional structure and a multi-label feature representation. In this work, we proposed a novel deep model, called Conditional CTC, which simultaneously learns to discriminate object categories and to model the joint distribution of the classification tasks of the two categories. Unlike CNNs, we propose a sequential learning mechanism, called Recurrent CTC, which learns features from the CTCNN simultaneously, and the CTCNN can be further adapted to predict objects. Our learning method is compared with a recently proposed CNN-supervised method, named Convolutional Recurrent CTC and results show that Recurrent CTC outperforms the state of the art techniques, which can be seen as a new class of CNN-based CNNs.


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