text: 395,138,1369042014084296705,1276310243123720192,Daniel Filan research-tweets,"New paper is up about about clusterability in neural networks, authored by myself, Shlomi Hod, @StephenLCasper, @decodyng, Andrew Critch, and Stuart Russell! Link to paper: <LINK> (1/9) <LINK> An old version with a somewhat more clickbaity title has been online for a while, but this is a more final version that I feel comfortable publicly promoting. So what's in the paper? (2/9) We're interested in dividing the neurons of neural nets into groups such that there's a lot of connection within the groups, but not much between the groups. We do this with graph clustering algorithms, and call the groups 'clusters'. (3/9) (The hope is that if you can do this well, then one day you'll be able to analyze neural net structure group-wise to learn facts about the network that don't depend on knowledge of the deployment distribution. But that's pretty far away...) (4/9) We find that in many conditions, if you train neural networks with pruning and dropout, basically every net you train will be significantly more clusterable than a random neural network with the same distribution of weights. (5/9) This is true for small MLPs and VGG-scale CNNs trained on various image classification tasks (but not small CNNs that we train on MNIST and Fashion-MNIST 😳). (6/9) We also find that when we cluster big CNNs other people have trained for ImageNet classification (some ResNets, some VGGs, and Inception-v3), they also are more clusterable than if their weights were arranged randomly. (7/9) Another thing we do is figure out ways of changing the training process to produce nets that are more neatly divisible. We find that regularization manages to succeed at doing this with little cost in accuracy for MLPs! (8/9) The paper has a bunch of stuff to dig into. If you want to look at code, it's available here: <LINK> Thanks to my co-authors, and the support of folks at @CHAI_Berkeley, for helping make this paper real! (9/9) @robpmcadam @StephenLCasper @decodyng Nope. But I think some co-authors are interested in that.",https://arxiv.org/abs/2103.03386,"The learned weights of a neural network have often been considered devoid of scrutable internal structure. In this paper, however, we look for structure in the form of clusterability: how well a network can be divided into groups of neurons with strong internal connectivity but weak external connectivity. We find that a trained neural network is typically more clusterable than randomly initialized networks, and often clusterable relative to random networks with the same distribution of weights. We also exhibit novel methods to promote clusterability in neural network training, and find that in multi-layer perceptrons they lead to more clusterable networks with little reduction in accuracy. Understanding and controlling the clusterability of neural networks will hopefully render their inner workings more interpretable to engineers by facilitating partitioning into meaningful clusters. ",Clusterability in Neural Networks,10,"['New paper is up about about clusterability in neural networks, authored by myself, Shlomi Hod, @StephenLCasper, @decodyng, Andrew Critch, and Stuart Russell! Link to paper: <LINK> (1/9) <LINK>', ""An old version with a somewhat more clickbaity title has been online for a while, but this is a more final version that I feel comfortable publicly promoting. So what's in the paper? (2/9)"", ""We're interested in dividing the neurons of neural nets into groups such that there's a lot of connection within the groups, but not much between the groups. We do this with graph clustering algorithms, and call the groups 'clusters'. (3/9)"", ""(The hope is that if you can do this well, then one day you'll be able to analyze neural net structure group-wise to learn  facts about the network that don't depend on knowledge of the deployment distribution. But that's pretty far away...) (4/9)"", 'We find that in many conditions, if you train neural networks with pruning and dropout, basically every net you train will be significantly more clusterable than a random neural network with the same distribution of weights. (5/9)', 'This is true for small MLPs and VGG-scale CNNs trained on various image classification tasks (but not small CNNs that we train on MNIST and Fashion-MNIST 😳). (6/9)', 'We also find that when we cluster big CNNs other people have trained for ImageNet classification (some ResNets, some VGGs, and Inception-v3), they also are more clusterable than if their weights were arranged randomly. (7/9)', 'Another thing we do is figure out ways of changing the training process to produce nets that are more neatly divisible. We find that regularization manages to succeed at doing this with little cost in accuracy for MLPs! (8/9)', ""The paper has a bunch of stuff to dig into. If you want to look at code, it's available here: https://t.co/SBOG603OHy\n\nThanks to my co-authors, and the support of folks at @CHAI_Berkeley, for helping make this paper real! (9/9)"", '@robpmcadam @StephenLCasper @decodyng Nope. But I think some co-authors are interested in that.']",21,03,2021