Zero-Shot Learning Through Cross-Modal Transfer · HackerPapers discussions

Zero-Shot Learning Through Cross-Modal Transfer — mentioned by nl

Wed, 23 Jan 2019 06:25:00 GMT

To expand on this (complete correct) response, unsupervised training is often part of the training process for a zero-shot prediction task. For example it's pretty common to use unsupervised learning to build embeddings for each target language, align the embeddings somehow (noting that you don't have labels, so you are using the multi-dimensional shapes within the embeddings to try to match them) and then finally test against labelled data (the zero-shot thing). Zero shot, cross modal transfer is something humans do really well. You can read a description of a Platypus and then label it…

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Zero-Shot Learning Through Cross-Modal Transfer — mentioned by nl

Sun, 12 Jun 2016 06:16:53 GMT

Sure, that's what I was pointing out. However: Zero-Shot Learning Through Cross-Modal Transfer http://arxiv.org/abs/1301.3666

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Zero-Shot Learning Through Cross-Modal Transfer — mentioned by nl

Tue, 28 Jul 2015 12:08:55 GMT

Jason Weston (Facebook) etc's work on Memory Nets[1] Anything to do with transfer learning (the 2013 Zero-Shot Learning Through Cross-Modal Transfer[2] paper is a good place to start) The increasing amount of demos around using NNs to generate "things" that look kinda-almost "intelligent". I can't point at a paper, but Andrej Karpathy demo of generating Shakespere-like writing, "Wikipedia" pages and "C" code in The Unreasonable Effectiveness of Recurrent Neural Networks[3] is the kind of thing I'm talking about. The beginnings of work around goal-seeking. The (now Google) DeepMind Atari…

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Zero-Shot Learning Through Cross-Modal Transfer — mentioned by dave_sullivan

Mon, 14 Apr 2014 21:24:12 GMT

This book is really coming together. It's been a while since I've put together a (100% not comprehensive) list of good places to start if you're looking to learn more and/or use deep learning in your projects. Open source Pylearn2 (used to win kaggle galaxies competition): http://deeplearning.net/software/pylearn2/ Theano (symbolic math library used by Pylearn2): http://deeplearning.net/software/theano/ Deep learning tutorials with theano (build your own neural networks): http://www.deeplearning.net/tutorial/ Demos Convnet JS: http://cs.stanford.edu/people/karpathy/convnetjs/ Sentiment…

Paper and discussion history. A mention is not an endorsement.