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.

In this paper, we propose a fully efficient and robust deep learning algorithm for unsupervised text classification. The learning is done using a CNN-based model, which uses information to model the semantic relations between text words and classify them. In order to learn the semantic relation between text words and classify them, the CNN-based model must first learn the semantic relationship between word-level and word-level features, which may not be available in both the word embedding and model. As a result, we have to rely on a few different word embedding features, which we call the word-level feature, and a more discriminative one to classify the text word with low-level information. We validate our model on unsupervised Chinese text classification datasets and on publicly available Chinese word graph. The model achieves comparable or comparable accuracy to state-of-the-art baselines for both unsupervised and supervised classification, especially when it is coupled with fast inference.

Efficient Regularization of Gradient Estimation Problems

An Evaluation of Different Techniques for 3D Human Pose Estimation

TBD: Typed Models

Boosting with Variational Asymmetric Priors

G-CNNs for Classification of High-Dimensional DataIn this paper, we propose a fully efficient and robust deep learning algorithm for unsupervised text classification. The learning is done using a CNN-based model, which uses information to model the semantic relations between text words and classify them. In order to learn the semantic relation between text words and classify them, the CNN-based model must first learn the semantic relationship between word-level and word-level features, which may not be available in both the word embedding and model. As a result, we have to rely on a few different word embedding features, which we call the word-level feature, and a more discriminative one to classify the text word with low-level information. We validate our model on unsupervised Chinese text classification datasets and on publicly available Chinese word graph. The model achieves comparable or comparable accuracy to state-of-the-art baselines for both unsupervised and supervised classification, especially when it is coupled with fast inference.