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[TWIM Notes] Aug 16 2020
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Send message Joined: 30 Jun 20 Posts: 461 Credit: 21,406,548 RAC: 2,346 ![]() ![]() ![]() ![]() ![]() |
This Week in MLC@Home Notes for Aug 16 2020 A weekly summary of news and notes for MLC@Home Apologies for the delay this week, We were hoping to have the new client first, but it needs another day of prep. News:
SingleDirectMachine 9680/10004 EightBitMachine 9634/10006 SingleInvertMachine 9684/10003 SimpleXORMachine 9647/10002 ParityMachine 358/10005 ParityModified 44/10005 EightBitModified 1549/10006 SimpleXORModified 6480/10005 SingleDirectModified 6460/10004 SingleInvertModified 6530/10002 Thanks again to all our volunteers! -- The MLC@Home Admins |
Send message Joined: 9 Jul 20 Posts: 142 Credit: 11,533,376 RAC: 0 ![]() ![]() ![]() ![]() |
Thrilled to read about the upcoming dataset 3 and thus the next round of models we are going to train. Any primer of what aspects you will focus on in this round? How will this subsequent training round differ from previous ones? |
Send message Joined: 30 Jun 20 Posts: 461 Credit: 21,406,548 RAC: 2,346 ![]() ![]() ![]() ![]() ![]() |
This round will focus on a wide net. Dataset 3 will have 100 difference random finite automata, and train multiple identical networks on each of these networks, and see if we can differentiate between each network. This is a much harder problem than differentiating between our current set of 5. The next client update (not the one rolled out tonight, but the next one) will introduce support for simple CNN based networks as well RNNs, so we can do things like train on CIFAR10 or MNIST-based image datasets. I'm very interested in using the generation tools from TrojAI to see if we can differentiate between networks trained with their compromised versus pure training data. I know you in particular were interested in hyperparameter search, but that's not what we're focused on at the moment. When school starts in the fall, I'm hoping to bring more researchers on board to maybe tackle that problem. |
Send message Joined: 9 Jul 20 Posts: 142 Credit: 11,533,376 RAC: 0 ![]() ![]() ![]() ![]() |
Thanks for getting back to me! Appreciate that very much! very interesting to know what's coming next to MLC@Home and increasing the complexity of the trained networks might yield more compelling evidence for your initial NN-differentiation hypothesis based on its training set. Will we also likely see an increase in runtime of the future WU-set? Exciting to see support for CNN and RNN, as they are definitely more prevalent NN structures nowadays. As I really don't know much about the programming and porting of the applications, I am really in awe how fast you keep progressing with the project here. TrojAI sounds like an awesome tool and very much tailored to your research purpose. Amazing what's already out there :) Hyperparameter search and optimization might also be more difficult to implement, thus I fully understand your decision not only from the perspective of your current research interest. Reading between the lines however, I see that it still might a valid research area down the road, so that's exciting! |
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