Improving the performance of batch selection algorithms trained to recognize handwritten digits – We present an end-to-end learning framework for learning to correctly predict the performance of human action recognition. We use an existing classifier, that is a hand-crafted object recognition approach. A simple, yet powerful algorithm based on a large dictionary of labeled objects is used for this task, and we apply this learning framework to improve our decision-making in the task of hand-crafted object recognition. Our experiments demonstrate that our proposed technique significantly improves the performance of the hand recognition task. Further, it can be applied to any hand-crafted object recognition task.

We consider the problem of unsupervised learning of deep networks in which the learned feature vectors are a linear combination of non-negative weights. Our main contribution is a new approach to unsupervised learning and two new works in this paper. First, we propose an efficient unsupervised learning strategy: we use a general set of sparse and unsupervised features and select features from this set, where the unknown feature vectors are a linear combination of the weights. Second, we formulate the decision problem as a convex relaxation of a weighted Gaussian process, which reduces the loss function, and the loss function is used to evaluate the learning performance. We also demonstrate a general model-based method based on our method. We also provide an experimental validation of our unsupervised learning strategy for the task of unsupervised learning in a real scenario. We achieve state-of-the-art performance on both synthetic and real datasets.

On the Role of Recurrent Neural Networks in Classification

Learning to Participate Stereo Motion with ConvNets

Improving the performance of batch selection algorithms trained to recognize handwritten digits

On the convergence of the dyadic adaptive CRFs in the presence of outliers

Boosting With Generalized FeaturesWe consider the problem of unsupervised learning of deep networks in which the learned feature vectors are a linear combination of non-negative weights. Our main contribution is a new approach to unsupervised learning and two new works in this paper. First, we propose an efficient unsupervised learning strategy: we use a general set of sparse and unsupervised features and select features from this set, where the unknown feature vectors are a linear combination of the weights. Second, we formulate the decision problem as a convex relaxation of a weighted Gaussian process, which reduces the loss function, and the loss function is used to evaluate the learning performance. We also demonstrate a general model-based method based on our method. We also provide an experimental validation of our unsupervised learning strategy for the task of unsupervised learning in a real scenario. We achieve state-of-the-art performance on both synthetic and real datasets.