text: 236,164,1270093422691364864,54599008,Chase,"New paper drop! Anomaly Detection with TensorNetworks. <LINK> The basic idea is really simple. We map our input into a vector space. If the length of our vector is close to 0, we flag it as an anomaly. <LINK> We achieve this by taking the inner product of our input/MPO model with itself. Again, very simple. <LINK> One of the awesome advantages of using a tensor network is that our model is entirely linear. This allows us to include a GLOBAL penalty that tries to bring all possible inputs close to 0. This penalty is just the partition function of our MPO. <LINK> These two terms bring us to a really simple and beautiful loss function. The first term tries to bring all normal inputs to the edge of a unit sphere, the second tries to bring everything to 0. <LINK> And our results kick ass. I want to point out the wine dataset specifically. This one is hard because its so small and so sparse. The second best method got 60% AUC, our method got 97%. <LINK> This was a really awesome project, and Jensen knocked it out of the park with how quickly we were able to publish this. Special thanks to @sleichen and @Theteamatx for making this work possible! Congrats again Jensen on the phenomenal paper! Defund the police. <LINK>",https://arxiv.org/abs/2006.02516,"Originating from condensed matter physics, tensor networks are compact representations of high-dimensional tensors. In this paper, the prowess of tensor networks is demonstrated on the particular task of one-class anomaly detection. We exploit the memory and computational efficiency of tensor networks to learn a linear transformation over a space with dimension exponential in the number of original features. The linearity of our model enables us to ensure a tight fit around training instances by penalizing the model's global tendency to a predict normality via its Frobenius norm---a task that is infeasible for most deep learning models. Our method outperforms deep and classical algorithms on tabular datasets and produces competitive results on image datasets, despite not exploiting the locality of images. ",Anomaly Detection with Tensor Networks,8,"['New paper drop!\n\nAnomaly Detection with TensorNetworks.\n<LINK>', 'The basic idea is really simple. We map our input into a vector space. If the length of our vector is close to 0, we flag it as an anomaly. https://t.co/Ksbk4psTLG', 'We achieve this by taking the inner product of our input/MPO model with itself. Again, very simple. https://t.co/WTHkeKGlTQ', 'One of the awesome advantages of using a tensor network is that our model is entirely linear. This allows us to include a GLOBAL penalty that tries to bring all possible inputs close to 0.\n\nThis penalty is just the partition function of our MPO. https://t.co/2akTwaG8zl', 'These two terms bring us to a really simple and beautiful loss function. The first term tries to bring all normal inputs to the edge of a unit sphere, the second tries to bring everything to 0. https://t.co/CQPYMaGVTO', 'And our results kick ass. I want to point out the wine dataset specifically. This one is hard because its so small and so sparse. The second best method got 60% AUC, our method got 97%. https://t.co/qluZ0Wv2Hr', 'This was a really awesome project, and Jensen knocked it out of the park with how quickly we were able to publish this.\n\nSpecial thanks to @sleichen and @Theteamatx for making this work possible!\n\nCongrats again Jensen on the phenomenal paper!', 'Defund the police.\n\nhttps://t.co/UEVk2XPhzk']",20,06,1229