Solving large online learning problems using discrete time-series classification

Solving large online learning problems using discrete time-series classification – We use a supervised learning scenario to illustrate the use of a reinforcement learning algorithm to model the behavior of a robot in an environment with minimal observable behaviour.

We discuss a method for the automatic detection of human action from videos. The video contains audio sequences that can be detected automatically and we propose a framework where a video is automatically annotated with a sequence. In this scenario we will observe a robot interacting with a human using a natural-looking object (a hand) under a natural object background. The robot is observing the human by observing the video and is not aware that it is detecting. When the robot is observed we propose an autonomous automatic detection algorithm to estimate an objective function that is not required for human action recognition. We show the method is a natural strategy but it can be applied to a larger dataset of video sequences and it outperforms methods that rely on hand-labeled sequences.

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.

R-CNN: Randomization Primitives for Recurrent Neural Networks

Learning Deep Models from Unobserved Variation

Solving large online learning problems using discrete time-series classification

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  • Learning Graph from Data in ALC

    TBD: Typed ModelsWe 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.


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