Deep Learning via Semi|Supervised Embedding

Deep Learning via Semi-supervised Embedding - SpringerLink

We show how nonlinear semi-supervised embedding algorithms popular for use with “shallow” learning techniques such as kernel methods can be easily applied ...

Deep Learning via Semi-Supervised Embedding - Ronan Collobert

In this work we use the same embedding trick as those researchers, but apply it to (deep) neural networks. Deep architectures seem a natural choice in hard AI ...

DEEP LEARNING VIA SEMI-SUPERVISED EMBEDDING

We pose deep learning as multi-tasking at different layers with auxiliary tasks. Hinton, LeCun and Bengio approaches use encoder-decoder models.

Deep learning via semi-supervised embedding

We show how nonlinear embedding algo- rithms popular for use with shallow semi- supervised learning techniques such as ker- nel methods can be applied to deep ...

Deep learning via semi-supervised embedding - ACM Digital Library

Abstract. We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to ...

Deep Learning via Semi-Supervised Embedding - Google Research

Deep Learning via Semi-Supervised Embedding. Jason Weston. Frederic Ratle. Hossein Mobahi. Ronan Collobert. Neural Networks Tricks of the Trade, Reloaded, ...

Deep learning via semi-supervised embedding - Semantic Scholar

It is shown how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to ...

Deep Learning via Semi-Supervised Embedding - Semantic Scholar

Deep Learning via Semi-Supervised Embedding. Jason Weston, Frederic Ratle and Ronan Collobert. Presented by: Janani Kalyanam. Page 2. Review Deep Learning.

Semi-supervised deep learning by metric embedding - arXiv

Title:Semi-supervised deep learning by metric embedding ... Abstract:Deep networks are successfully used as classification models yielding state- ...

26 Deep Learning via Semi-supervised Embedding

Several authors have recently proposed methods for using unlabeled data in deep neural network-based architectures. These methods either perform a greedy layer- ...

SEMI-SUPERVISED DEEP LEARNING BY METRIC EM - OpenReview

Using metric embedding with neural network was also specifically shown to provide good results in the semi-supervised learning setting as seen in Weston et al.

A versatile semi-supervised training method for neural networks

We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. ... ing via semi-supervised embedding. In Neural ...

Semi-supervised elastic manifold embedding with deep learning ...

In [21], a general kernelization framework for learning algorithms is proposed via a two-stage procedure, i.e., transforming data by kernel principal component ...

Deep Learning via Semi-Supervised Embedding - Videolectures

We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be ...

Semi Supervised Learning with Deep Embedded Clustering for ...

To this end, by adding a clustering layer to a deep convolutional neural network (CNN), we present a new training algorithm for a semi-supervised method that ...

Semi-supervised Learning with Deep Generative Models

by training feed-forward classifiers with an additional penalty from an auto-encoder or other unsu- pervised embedding of the data (Ranzato and Szummer, 2008; ...

Semi-Supervised Deep Learning with Memory - Xiatian Zhu

where xi refers to the embedded deep feature representation of Ii extracted by the deep CNN, and Wj is the j-th class prediction function parameter. The.

Semi-Supervised Learning With Deep Embedded Clustering for ...

Our proposed semi-supervised learning algorithm based on deeply embedded clustering (SSLDEC) learns feature representations via iterations by ...

Semi-supervised deep learning by metric embedding - ResearchGate

Request PDF | Semi-supervised deep learning by metric embedding | Deep networks are successfully used as classification models yielding state-of-the-art ...

Deep semi-supervised learning via dynamic anchor graph ...

A novel deep semi-supervised algorithm for simultaneous graph embedding and node classification, utilizing dynamic graph learning in neural network hidden ...