3 accepted papers
The vision transformer has achieved state-of-the-art performance in various vision tasks; however, the memory consumption is larger than those of previous convolutional neural network based models because of $O(N^2)$ time and memory complexity of the general self-attention models. Many approaches ai…
Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against $\ell_2$-adversarial perturbations. Under the paradigm, the robustness of a classifier is aligned with the prediction confidence, i.e., the higher confidence from a s…
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search. Our approach samples candidate models from the multinomial distribution over the policy vectors. The proposed technique improves the efficiency of architecture search sign…