19 citations · 22 across the 8 of their papers we have counts for
5 papers · 1 filter
KAN based Autoencoders for Factor Models
Tianqi Wang, Shubham Singh
Inspired by recent advances in Kolmogorov-Arnold Networks (KANs), we introduce a novel approach to latent factor conditional asset pricing models. While previous machine learning a…
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
Depeng Li, Tianqi Wang, Junwei Chen +2
Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. In this paper, we propose a new kind of co…
Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding
Depeng Li, Tianqi Wang, Junwei Chen +3
Deep neural networks are susceptible to catastrophic forgetting when trained on sequential tasks. Various continual learning (CL) methods often rely on exemplar buffers or/and netw…
IF2Net: Innately Forgetting-Free Networks for Continual Learning
Depeng Li, Tianqi Wang, Bingrong Xu +3
Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which inform…
Multi-View Class Incremental Learning
Depeng Li, Tianqi Wang, Junwei Chen +3
Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods mo…