C.1 More details on datasets and models being explained For text datasets, We employ three-layer convolutional neural networks. Due to the large number of parameters in the target model (over 1.6 million), we do not implement Surrogate derivative: We first transform Eqn. We then follow Y eh et al. T arget derivative: We directly adopt Eqn. ( t 1) ( t 1) For last layer embeddings and neural tangent kernels, we directly use Eqn. We train 5 models simultaneously on a single GPU for speed up.

We propose a general class of sample based explanations of machine learning models, which we term generalized representers . To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel.

Now, let us derive the differential equation that gives a solution to the variational problem. This condition yields the Euler-Lagrange equation, d dt @ L @ q = @ L @q . Here, we derive the Noether's learning dynamics by applying Noether's theorem to the A general form of the Noether's theorem relates the dynamics of Noether By evaluating the right hand side of Eq. 23, we get e Now, we harness the covariant property of the Lagrangian formulation, i.e., it preserves the form Plugging this expression obtained from the steady-state condition of Eq.27 Here, we ignore the inertia term in Eq. 16, assuming that the mass (learning rate) is finite but small All the experiments were run using the PyTorch code base. We used Tiny ImageNet dataset to generate all the empirical figures in this work. The key hyperparameters we used are listed with each figure.