no code implementations • ICLR 2020 • Connie Kou, Hwee Kuan Lee, Ee-Chien Chang, Teck Khim Ng
Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images.
no code implementations • 1 Jun 2019 • Connie Kou, Hwee Kuan Lee, Ee-Chien Chang, Teck Khim Ng
Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images.
no code implementations • 5 Nov 2018 • Connie Kou, Hwee Kuan Lee, Jorge Sanz, Teck Khim Ng
However, in Kou et al. (2018) and some other works on distribution regression, there is a lack of comprehensive comparative study on both theoretical basis and generalization abilities of the methods.
no code implementations • 13 Apr 2018 • Connie Kou, Hwee Kuan Lee, Teck Khim Ng
Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces.
no code implementations • ICLR 2018 • Connie Kou, Hwee Kuan Lee, Teck Khim Ng
We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions.