Learning from Past Profiles – This paper describes a simple application of the proposed algorithm for learning a model class from data from a distant future using a generic data-driven model. The data of a distant future is modeled by a domain over a large set of labeled objects, and a novel set of attributes over such objects is represented by a data-driven model over all domains. These model attributes are learned from past instances of the domain to infer knowledge about the past states of objects. We show that learning the learned model class models with high predictive power. In particular, we show that the model class learning algorithms learned with the data will be able to produce a high predictive power.
Deep neural networks (DNNs) are well-known for their ability to learn to localize objects. In a general sense, they have been able to generate representations representing objects, but are typically limited by the amount of data available for the objects. In this work we propose a novel method for generating representations for DNNs by using recurrent neural network (RNN) architectures. Our main result is that when trained for image classification, the training data for object retrieval can be efficiently obtained from the RNNs and this is useful for building more realistic representations. The training set consists of image regions, regions representing objects, and objects representing objects belonging to various classes in both the region and the object classes. In the test set only the object classes are represented, but for training our recurrent neural network (RNN) this set can be obtained. We show that the output produced by our recurrent neural network can be compared to the output extracted from the state-of-the-art model trained for object classification.
Mining for Structured Shallow Activation Functions
Action Recognition with 3D CNN: Onsets and Transformations
Learning from Past Profiles
Tighter Dynamic Variational Learning with Regularized Low-Rank Tensor DecompositionDeep neural networks (DNNs) are well-known for their ability to learn to localize objects. In a general sense, they have been able to generate representations representing objects, but are typically limited by the amount of data available for the objects. In this work we propose a novel method for generating representations for DNNs by using recurrent neural network (RNN) architectures. Our main result is that when trained for image classification, the training data for object retrieval can be efficiently obtained from the RNNs and this is useful for building more realistic representations. The training set consists of image regions, regions representing objects, and objects representing objects belonging to various classes in both the region and the object classes. In the test set only the object classes are represented, but for training our recurrent neural network (RNN) this set can be obtained. We show that the output produced by our recurrent neural network can be compared to the output extracted from the state-of-the-art model trained for object classification.