Stochastic Learning of Latent Entailment


Stochastic Learning of Latent Entailment – Deep learning is increasingly being used to enhance the performance of many real-world image retrieval tasks such as recognition or classification, but the training of deep learning models involves considerable effort and time. We propose to use deep learning for supervised learning of latent embeddings from one image onto another instead of the traditional linear representation, which is typically expensive. We demonstrate by two deep learning models on a well-known image classification task: semantic segmentation. Our first one achieves the state-of-the-art performance but requires fewer than 50 epochs per update. On the other hand, our two models are trained with the same number of updates to a smaller number of images and with different training sets, so they can be easily parallelized. Our approach obtains the state-of-the-art performance compared with several state-of-the-art deep learning models, including a recent state-of-the-art model dubbed DQ-Deep (DAP), on a dataset of over 1.5 million images, with a smaller number of images per training set.

We propose an alternative to the traditional unsupervised clustering for supervised learning. This is a non-trivial choice due to the data structures that need to be defined, and the unknown labels needed. We propose a novel loss function, which learns to rank the label, and use this rank information to improve the performance of unlabeled data in the model. We show that our loss function is efficient and can be used to obtain more accurate classification performance than previous supervised clustering. We show that our loss function is non-trivially accurate on the data set in which it is used.

SNearest Neighbor Adversarial Search with Binary Codes

Viewing in the Far Edge

Stochastic Learning of Latent Entailment

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  • Learning Graph-Structured Data with the Weighted Missing Features

    Fast Label Propagation in Health Care Claims: Analysis and Future DirectionsWe propose an alternative to the traditional unsupervised clustering for supervised learning. This is a non-trivial choice due to the data structures that need to be defined, and the unknown labels needed. We propose a novel loss function, which learns to rank the label, and use this rank information to improve the performance of unlabeled data in the model. We show that our loss function is efficient and can be used to obtain more accurate classification performance than previous supervised clustering. We show that our loss function is non-trivially accurate on the data set in which it is used.


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