no code implementations • 3 May 2023 • Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang
This paper aims to prove the emergence of symbolic concepts in well-trained AI models.
1 code implementation • 25 Feb 2023 • Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang
In this paper, we focus on mean-field variational Bayesian Neural Networks (BNNs) and explore the representation capacity of such BNNs by investigating which types of concepts are less likely to be encoded by the BNN.
1 code implementation • ICLR 2022 • Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang
This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded in DNNs.
no code implementations • NeurIPS 2021 • Wen Shen, Qihan Ren, Dongrui Liu, Quanshi Zhang
In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing.
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.