B, we present additional details on peripheral region classification presented in Sec 4.1. We conclude this paper with a short discussion on potential impacts of our work in Sec. Recall the definition of the peripheral position encoding introduced in Sec. The parameterization in Eq. 3 is applied for all the layers ( Our first step is to prove the parameterization (Eq. 3) provides local attention after the Note that the negation in Eq. 8 gives the inequality of PP (R; W Step 2. We now show that the respective size and strength of local attention in the peripheral position Figure S2: Peripheral vision of human eye (left). We found that Eq. 23 gives radius of 1.5 given para-central angle of 8, resulting in quite narrow interval: Assume the position-based function at each head is learned to perform'hard attention' on one of its surrounding positions, i.e., an extreme semi-dynamic attention .

Human vision possesses a special type of visual processing systems called peripheral vision .

Both approaches perform end-to-end nonlinear manifold learning and density estimation for data projected onto this manifold.

To account also for spatial dependencies, Cini et al.

Hypothesis (L TH): a randomly-initialized dense neural network contains an extremely sparse subnet-work (i.e., a winning lottery ticket) such that, when trained from scratch with weights being reset to its initialization, can achieve similar performance to the original dense network within similar

Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently.