248 citations · 755 across the 30 of their papers we have counts for
11 papers · 1 filter
Incremental Learning via Rate Reduction
Ziyang Wu, Christina Baek, Chong You +1
Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
A Critique of Self-Expressive Deep Subspace Clustering
Benjamin D. Haeffele, Chong You, René Vidal
Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with di…
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Chong You, Zhihui Zhu, Qing Qu +1
Recent advances have shown that implicit bias of gradient descent on over-parameterized models enables the recovery of low-rank matrices from linear measurements, even with no prio…
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu, Kwan Ho Ryan Chan, Chong You +2
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{M…
Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces
Chong You, Chi Li, Daniel P. Robinson +1
Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classi…
Deep Isometric Learning for Visual Recognition
Haozhi Qi, Chong You, Xiaolong Wang +2
Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-th…
Recovery and Generalization in Over-Realized Dictionary Learning
Jeremias Sulam, Chong You, Zhihui Zhu
In over two decades of research, the field of dictionary learning has gathered a large collection of successful applications, and theoretical guarantees for model recovery are know…
Is an Affine Constraint Needed for Affine Subspace Clustering?
Chong You, Chun-Guang Li, Daniel P. Robinson +1
Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as mot…
Stochastic Sparse Subspace Clustering
Ying Chen, Chun-Guang Li, Chong You
State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such re…
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Zitong Yang, Yaodong Yu, Chong You +2
The classical bias-variance trade-off predicts that bias decreases and variance increase with model complexity, leading to a U-shaped risk curve. Recent work calls this into questi…