[HN Gopher] Cornell and NTT's Physical Neural Nets Enable Arbitr... ___________________________________________________________________ Cornell and NTT's Physical Neural Nets Enable Arbitrary Physical System Training Author : rch Score : 18 points Date : 2021-05-29 13:35 UTC (1 days ago) (HTM) web link (syncedreview.com) (TXT) w3m dump (syncedreview.com) | rich_sasha wrote: | Hard to deduce much from the article. Is it that it's a NN where | the individual components are physical transforms? | | > On the MNIST handwritten digit classification task, the | trainable SHG transformations boost the performance of digital | operations from roughly 90 percent accuracy to 97 percent. | | It is hard to take it seriously, when 97% on MNIST is achievable | with the kind of tutorials bundled at the end of PyTorch | installation guides - "see you can make a DNN model in 10 lines | of code!". | rch wrote: | The paper is linked at the bottom of the article: _Deep | physical neural networks enabled by a backpropagation algorithm | for arbitrary physical systems_ -- | https://arxiv.org/abs/2104.13386 | | > physics-aware training (PAT)... allows us to efficiently and | accurately execute the backpropagation algorithm on any | sequence of physical input-output transformations, directly _in | situ_. | Animats wrote: | What they're doing, I think, is compiling a trained neural net | into a different form. | | (1), training input data (e.g., an image) is input to the | physical system, along with parameters. | | (2), in the forward pass, the physical system applies its | transformation to produce an output. | | (3), the physical output is compared to the intended output | (e.g., for an image of an '8', a predicted label of 8) to compute | the error. | | (4), using a differentiable digital model to estimate the | gradients of the physical system(s), the gradient of the loss is | computed with respect to the controllable parameters. | | (5) the parameters are updated according to the inferred | gradient. | | What they mean by a "physical system" is a series of analog | elements with lots of tuning parameters. Like filters. This is a | system for setting the tuning parameters. You have to be able to | simulate the "physical system", and it has to be mostly | differentiable, so you can tune by hill-climbing. | | The control systems people ought to like this, because the output | is a control system that's made of components with predictable | and continuous properties. You want to know that if if does the | right thing for an input of 1.0 and 1.5, it doesn't do something | totally unexpected for 1.365. This may be a way to get there. | | This may be the mechanism behind "muscle memory". Tasks get | optimized down to a control system that executes fast, but | doesn't retrain easily. | | The problems they chose to work on seem strange, but that may | reflect their funding or interests. This might be worth trying | for, say, quadcopter control. You might be able to train a neural | net controller and then hammer it down into a quick little | algorithm that can fit in the onboard computer. | | (I subscribe to IEEE Control Systems Journal, and I understand | maybe 15% of it.) | [deleted] | teruakohatu wrote: | The hybrid approach of calculating loss with a physical system | and then calculating the gradient using a model narrows the | simulation-reality gap. The cost would surley be much, much | slower training. If the physical process required, for example, | heating and cooling an oven, training would take a very long | time. | [deleted] ___________________________________________________________________ (page generated 2021-05-30 23:01 UTC)