Learning to Rank from Unlabeled Data with Conditional Rank Inference

Learning to Rank from Unlabeled Data with Conditional Rank Inference – We describe a method to learn a posterior function of a model conditioned on unseen data under the assumption that the data is annotated. We show that this method is a proper approximation of the posterior, which is not a direct prior of any model but a prior of the entire training set. We illustrate by showing an example on supervised learning.

We develop a novel deep learning framework to automatically segment the human brain into regions of high predictive utility. Our model has been trained on the MNIST dataset and can outperform state-of-the-art deep models on both the image classification task and the recognition task. To better understand the structure of the brain, we also propose to map the entire brain into a network of neurons. We show that using a CNN-based model is a significant improvement over existing techniques and outperforms the existing approaches on the recognition task using MNIST data from the Stanford ImageNet 2017 task.

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Fast Partition Learning for Partially Observed Graphs

Learning to Rank from Unlabeled Data with Conditional Rank Inference

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  • A Comparison of Performance Measures of Logical and Probabilistic Models

    Improving Human Parsing by Exploiting Minimal Metric Accuracy in Deep Neural NetworksWe develop a novel deep learning framework to automatically segment the human brain into regions of high predictive utility. Our model has been trained on the MNIST dataset and can outperform state-of-the-art deep models on both the image classification task and the recognition task. To better understand the structure of the brain, we also propose to map the entire brain into a network of neurons. We show that using a CNN-based model is a significant improvement over existing techniques and outperforms the existing approaches on the recognition task using MNIST data from the Stanford ImageNet 2017 task.


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