31 citations · 68 across the 10 of their papers we have counts for
7 papers · 1 filter
Perlin Noise Improve Adversarial Robustness
Chengjun Tang, Kun Zhang, Chunfang Xing +2
Adversarial examples are some special input that can perturb the output of a deep neural network, in order to make produce intentional errors in the learning algorithms in the prod…
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Ignavier Ng, Kun Zhang
Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered. However, in practice, data may be distributed across diffe…
Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi +4
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information requ…
Unaligned Image-to-Image Translation by Learning to Reweight
Shaoan Xie, Mingming Gong, Yanwu Xu +1
Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumpt…
Instance-dependent Label-noise Learning under a Structural Causal Model
Yu Yao, Tongliang Liu, Mingming Gong +3
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, r…
Outdoor Position Recovery from HeterogeneousTelco Cellular Data
Yige Zhang, Weixiong Rao, Kun Zhang +1
Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report t…
CausalAdv: Adversarial Robustness through the Lens of Causality
Yonggang Zhang, Mingming Gong, Tongliang Liu +5
The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. As causal reasoning has an instinct for modelling distribution change…