We apply one-dimensional convolutional neural networks to the Frobenius traces of elliptic curves over $\mathbb{Q}$ and evaluate and interpret their predictive capacity. In keeping with similar experiments by Kazalicki--Vlah, Bujanović--Kazalicki--Novak, and Pozdnyakov, we observe high accuracy predictions for the analytic rank across a range of conductors. We interpret the prediction using saliency curves and explore the interesting interplay between murmurations and Mestre--Nagao sums, the details of which vary with the conductor and the (predicted) rank.

Let 1, , M be K Kis PSD, i 0foralli.

The FAUST test set contains 200 scans of undressed people in challenging poses andthescans themselvesarenoisy. Nonetheless we report the results as per the protocol in Table 2. For competing approaches we take the numbers from the corresponding papers. It can be clearly seen that our model trained primarily with selfsupervision performs better than the competing approaches. Our formulation allows us to jointly differentiate through the correspondences and the instance specific human model parameters. This allows us to create a self-supervised loop for registration.

Model details can be found at https://huggingface.co/gpt2 Popular phrases such as "pair of" are examples of repetitive