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Differentiable Plasticity: A New Method Learning to Learn
- dpflan 9y agoInteresting. Some highlighted links from the writeup: 1. Differentiable plasticity: training plastic neural networks with backpropagation (https://arxiv.org/abs/1804.02464 https://arxiv.org/abs/1804.02464) 2. Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (https://arxiv.org/abs/1703.10371 https://arxiv.org/abs/1703.10371) 3. Github for the project: https://github.com/uber-common/differentiable-plasticity https://github.com/uber-common/differentiable-plasticity 4. Learning to Learn (http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/) 5. Meta-Learning: http://metalearning.ml/ http://metalearning.ml/
- signa11 9y agovery cool stuff ! it might be possible to use this stuff for pruning edges which are not that plastic as well.
- trextrex 9y agoVery cool. It's interesting how powerful the recurrent network becomes with the addition of the learned hebbian term. For context, even without the Hebbian term, recurrent networks can learn to learn to do quite interesting things (Hochreiter et al. 2001). Shameless plug -- our lab recently ported LSTMs to spiking networks without a significant loss in performance, and showed that learning to learn works quite well even with spiking networks (Bellec et al. 2018). So it seems like this method of learning to learn could provide a extremely biologically realistic and fundamental paradigm for fast learning. The addition of the Hebbian term neatly fits in with this paradigm too. Hochreiter et al. 2001: http://link.springer.com/chapter/10.1007/3-540-44668-0_13 http://link.springer.com/chapter/10.1007/3-540-44668-0_13 Bellec et al. 2018: https://arxiv.org/abs/1803.09574 https://arxiv.org/abs/1803.09574
- no_identd 9y ago"What if the plasticity of the connections was under the control of the network itself, as it seems to be in biological brains through the influence of neuromodulators?" Anyone who wishes to explore this idea would do well to go back to the basics of neural nets and read Warren McCulloch's seminal papers on neural nets, from the 40s: http://www.cse.chalmers.se/~coquand/AUTOMATA/mcp.pdf http://www.cse.chalmers.se/~coquand/AUTOMATA/mcp.pdf A Logical Calculus of the ideas immanent in nervous activity http://vordenker.de/ggphilosophy/mcculloch_heterarchy.pdf http://vordenker.de/ggphilosophy/mcculloch_heterarchy.pdf A heterarchy of values determined by the topology of neural nets (After having read those two papers, one can then try to make sense of Heinz von Förster's masterpiece, http://www.univie.ac.at/constructivism/archive/fulltexts/1270.pdf http://www.univie.ac.at/constructivism/archive/fulltexts/127..., Objects: Tokens for (Eigen-)Behaviors, which also bears some relevance to this matter. However, most people find it incomprehensible.)
- nairboon 9y agoThank you for those papers! Do you have some further suggestions that build upon Försters idea?
- no_identd 9y agoTry anything citing von Förster's paper that was written by: • Louis H. Kauffman if you're mostly interested in the pure math aspect of it. He wrote numerous papers on the topic of von Förster's paper, you can find one them here: https://arxiv.org/abs/1109.1892 https://arxiv.org/abs/1109.1892 • JM Stern and CAB Pereira if you're mostly interested in the application to statistics and fundamental questions of the epistemology of statistics. I wrote a thread on their works on my Twitter account at some point: https://twitter.com/no_identd/status/877883663014400000 https://twitter.com/no_identd/status/877883663014400000 Try this AMAZING paper by AF Zimpel: http://www.emeraldinsight.com/doi/pdf/10.1108/03684920510581701 http://www.emeraldinsight.com/doi/pdf/10.1108/03684920510581... Also, this paper by Heinz von Förster (first order author) and Karl H. Miiller (second order editor), where Miller basically took a lot of old papers by von Förster and rearranged them to give a more coherent view for certain types of readers: http://www.univie.ac.at/constructivism/archive/fulltexts/3098.pdf http://www.univie.ac.at/constructivism/archive/fulltexts/309...
- trisimix 9y agoOh god my Im going to bomb finals if I read this
- letitgo12345 9y agoIs the plasticity update guaranteed to reach equilibrium assuming the network is run on iid data (as in do the H_{ij} values reach a fixed point)? Edit: Seems like it should be reached eventually as the equilibrium point is H_ij = y_i * y_j and they keep doing a weighted average of the former with the latter (this is not a proof ofc as y_i * y_j keeps changing with each sample).
- whatever1 9y agoGood luck with getting even more suboptimal solutions with this extra non linearity. No wonder why when your autonomous cars are plowing into people or walls you have no clue of what is going on.
- atomical 9y agoSeems like a LIDAR problem.
- whatever1 9y agoTeslas do not have lidars. They still drive happily towards stationary objects. Because somehow we accepted that very bad probabilistic solutions to a very tough problem are good enough.
- petters 9y agoHolmes: You have a Lidar problem. Tesla: But we don't use Lidars! Holmes: That's the problem.
- ben_w 9y agoHumans don’t have lidar. If machine vision isn’t good enough to drive a car at human levels without lidar, then it isn’t good enough yet. (IIUC, it is superhuman overall but with some really dumb edgecases where humans go “well that was obviously wrong, why didn’t it see the thing?”)
- Firadeoclus 9y agoBut that's only because our edge cases and the machine vision edge cases don't match, since the underlying concepts are different. And frankly, we probably don't want them to match. The main goal should be to make the cars drive better than humans on average, even if they're not perfect. And we know that there will always be edge cases.
- dchichkov 9y agoIt’d be interesting to compare this approach against a simpler baseline: setting a different (10 – 100 times higher?) learning rate for a fraction (10% ?) of neurons in an LSTM.
- fizx 9y agoInteresting... I mean knockout already does this (sort of), by setting the learning rate of 10% artificially low. I'm not sure if inverting the common thing is useful, but it might be fun to try.
- adrianratnapala 9y agoSo the "plastic component" of a connection strength is a thing which decays away exponentially, but is replenished whenever the two endpoints do the same thing. I have heard that neuroscientists have an adage "fire together wire together". Is that all that ML people mean by "plasticity".