no code implementations • 30 Aug 2022 • Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala
While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features.
no code implementations • 21 Jun 2020 • Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, Quanshi Zhang
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
no code implementations • 18 Mar 2020 • Hao Zhang, Yi-Ting Chen, Liyao Xiang, Haotian Ma, Jie Shi, Quanshi Zhang
We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.
1 code implementation • 10 Jun 2019 • Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang
We define two types of entropy-based metrics, i. e. (1) the discarding of pixel-wise information used in the forward propagation, and (2) the uncertainty of the input reconstruction, to measure input information contained by a specific layer from two perspectives.
1 code implementation • ICLR 2020 • Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Jie Ren, Quanshi Zhang
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features.
no code implementations • 8 Jan 2019 • Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.
no code implementations • CVPR 2019 • Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu
We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level.