text: 46,92,1425366685435973636,1339508444764786694,Gregor Kasieczka,"New paper today: ""Symmetries, Safety, and Self-Supervision"" (arXiv: <LINK>). We - driven by excellent Heidelberg people including @LorenzVogel - look at how known physical symmetries can be used to learn better representations. 1/3 We use contrastive learning a la #SimCLR and include translation, rotations, and soft+collinear emissions. This figure shows how well rotations are learned. Left is without including rotations, right is with. s(z,z')=1 &lt;-&gt; identical representations 2/3 <LINK> The goal is to have a better input for #unsupervised learning (coming next..) but we can already test how well the learned representation does as input to a linear classifier. Spoiler: Pretty well (curve is for top tagging w/ a linear network) 3/3 <LINK>",https://arxiv.org/abs/2108.04253,"Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning. As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties. We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well. ","Symmetries, Safety, and Self-Supervision",3,"['New paper today: ""Symmetries, Safety, and Self-Supervision"" (arXiv: <LINK>). We - driven by excellent Heidelberg people including @LorenzVogel - look at how known physical symmetries can be used to learn better representations. 1/3', ""We use contrastive learning a la #SimCLR and include translation, rotations, and soft+collinear emissions. This figure shows how well rotations are learned. Left is without including rotations, right is with. s(z,z')=1 &lt;-&gt; identical representations 2/3 https://t.co/zFaYjFEiEk"", 'The goal is to have a better input for #unsupervised learning (coming next..) but we can already test how well the learned representation does as input to a linear classifier. Spoiler: Pretty well (curve is for top tagging w/ a linear network) 3/3 https://t.co/YyzlA9srTG']",21,08,752