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 robust learning framework for extracting feature-based semantic information from a large corpus, with the aim of improving the robustness of state-of-the-art semantic retrieval systems. We evaluate the proposed framework on both large-scale and small-scale datasets, and demonstrate improved performance on a variety of tasks.

Improving the performance of CNN-based image segmentation with weighted dictionary CNNs

Generating Semantic Representations using Greedy Methods

TBD: Typed Models

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  • Unsupervised Multi-modal Human Action Recognition with LSTM based Deep Learning Framework

    Guided Depth EstimationWe present a robust learning framework for extracting feature-based semantic information from a large corpus, with the aim of improving the robustness of state-of-the-art semantic retrieval systems. We evaluate the proposed framework on both large-scale and small-scale datasets, and demonstrate improved performance on a variety of tasks.


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