One-shot Learning with Memory-Augmented Neural Networks · HackerPapers discussions

One-shot Learning with Memory-Augmented Neural Networks — mentioned by goolulusaurs

Sun, 03 Jun 2018 06:01:57 GMT

The Neural Turing Machine has been expanded upon considerably since that paper, and inspired a number of other memory augmented neural network architectures, including: https://www.gwern.net/docs/rl/2016-graves.pdf , https://arxiv.org/abs/1605.06065 and https://arxiv.org/abs/1803.10760 . If you don't want to read a bunch of papers this video also has a good discussion of that last paper, describing how a memory system based on the NTM has been applied to reinforcement learning to achieve very impressive results that seem to me to be a very significant step towards human-like general purpose…

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by nl

Mon, 05 Dec 2016 11:16:14 GMT

One shot learning is such an active area of research there's a long Wikipedia page[1] about it. I think the SOTA is probably [2], which came out of DeepMind. There's still a way to go before it matches ResNet performance on ImageNet (or even human performance on any real task) though. [1] https://en.wikipedia.org/wiki/One-shot_learning [2] https://arxiv.org/abs/1605.06065

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by vonnik

Sat, 03 Dec 2016 19:37:54 GMT

[Disclosure: I work for a deep-learning company.] Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites. He is skeptical that deep learning and other forms of advanced AI 1) will be applicable to smaller and smaller datasets, and that 2) they will become easier to use. And yet some great research is being done that will prove him wrong on his first…

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by YeGoblynQueenne

Fri, 11 Nov 2016 14:55:11 GMT

That's a nice article in that it manages to get excitement across without forgetting to balance it somewhat. Kind of a rare thing these days. Btw, I'm interested to hear how well training with large one-hot encoded vectors scales. A paper someone pointed me to recently on HN suggested that it doesn't scale very well: One-shot Learning with Memory-Augmented Neural Networks [https://arxiv.org/abs/1605.06065]

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by ak4g

Mon, 07 Nov 2016 20:10:03 GMT

This is just an outdated view of the state-of-the-art. It's understandable, given that it's outdated by maybe six months, iff you're willing to go with preprints. https://arxiv.org/abs/1605.06065 A hobbyist looking for something plug-and-play will still generally want lots of data; the cutting edge is not exactly "curl|bash"-able. But the papers coming out this year have been dispatching what I thought would be entire areas of study in a dozen pages, one after another after another. Not only do I think it's a "when" and not an "if", I think the timelines people throw around date to "ancient"…

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by jimfleming

Sat, 11 Jun 2016 15:58:29 GMT

> Only at such a high level of abstraction as to be meaningless. I'm not sure what this means or how the abstractions are meaningless? From Gabor filters to concepts like "dog", the abstractions are quite meaningful (in that they function well), even if not to us. > They are not. Hundreds of man years worth of engineering time go into each of those systems, and none of those systems generalizes to anything other than the task it was created for. That's nothing like human intelligence. This isn't strictly true if we look at the ability to generalize as a sliding scale. The level of…

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by dave_sullivan

Thu, 02 Jun 2016 14:06:41 GMT

> If this is the state of the art in machine translation, how on Earth can you expect a machine that can converse on human level in three years? Here are 2 good papers--one from last year, one very recent--that might change your mind. https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1605.06065v1.pdf Over the past 10 years (deep learning got going in ~2006, really), the state of the art has improved at an exponential rate, and that's not slowing down. There are plenty of reasons to be bullish. Or at least, now seems like a bad time to leave the field.

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

One-shot Learning with Memory-Augmented Neural Networks — mentioned by teraflop

Sat, 21 May 2016 02:14:38 GMT

http://arxiv.org/abs/1605.06065

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

One-shot Learning with Memory-Augmented Neural Networks — HN submission

Fri, 20 May 2016 13:39:40 GMT

One-shot Learning with Memory-Augmented Neural Networks

103 points; 19 discussion replies on HN. Paper and source history. A submission is not an endorsement.