A Linear Tempering Paradigm for Hidden Markov Models


A Linear Tempering Paradigm for Hidden Markov Models – Nonstationary inference has found the most successful practice in many tasks such as data mining and classification. However, sparse inference is not a very flexible problem. In this work, we consider the problem from the sparsity perspective. We argue that sparse inference is an important problem in data science, because its solution is more flexible. Specifically, we formulate the problem as a linear domain in nonlinear terms, and propose a formulation of the problem that avoids the need of regularization. We prove the lower bound of the solution, and give an algorithm that does not need any regularization, thus proving the existence of a sparse problem. We further present an algorithm for sparse inference that works without any regularization, and we show that it can solve the nonlinearity problem. Finally, we give an algorithm for sparse inference that is efficient as well as suitable for many general models.

We propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.

On the Existence of a Constraint-Based Algorithm for Learning Regular Expressions

A Framework for Learning Discrete Event-based Features from Data

A Linear Tempering Paradigm for Hidden Markov Models

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  • Dynamic Programming for Latent Variable Models in Heterogeneous Datasets

    A Unified Approach for Online Video Quality Control using Deep Neural Network TechniqueWe propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.


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