Previous work has probed the content of these signals by directly reconstructing images from retinal activity using linear regression or nonlinear regression with neural networks.

Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2019), which eschews deep learning, is competitive with

Choose a random initial value for p as well as h, and construct H = diag( h) and P = diag( p) .

In Appendix B, we provide additional experiments supporting our claims. A.1 Proof of Lemma 3.1 We begin with Lemma 3.3 of [2], which gives 1 2 nullx We begin with equation eq. B.1 Condition Number of A is 1 Figure 6: Speed ups with best possible stepsizes vs. batch size (noisy experiments with σ = 0 . Figure 7: Speed up vs. stepsizes for l Figure 8: Speed up vs. stepsizes for logistic regression. Figure 13: Speed ups with best possible stepsizes vs. batch size (noisy experiments with σ = 0 .

We first consider process samples by logistic regression with cluster centers as categorical variables . Intuitively, non-orthonormal centers correlate with each other, which means there is an overlap among categorical variables and makes it hard to identify the decision boundary that leads to a failed classification.

In Section A.1 and A.2 we derive closed-form expressions of the standard and robust risks from We first prove Equation (13). We now prove the second part of the statement. In this section we provide additional details on our experiments. B.1 Neural networks on sanitized binary MNIST If not mentioned otherwise, we use noiseless i.i.d. C.1 we give an intuitive explantion for the robust overfitting phenomenon described in C.2 we discuss how inconsistent adversarial training prevents We now shed light on the phenomena revealed by Theorem 3.1 and Figure 2. In particular, we In this section we further discuss robust logistic regression studied in Section 4. As observed in Section 4.4, label noise can prevent interpolation and hence improve the robust risk Hence, inconsistent training perturbations can induce spurious regularization effects.

This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan.

This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan.