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05/30/2023
How Does Information Bottleneck Help Deep Learning?
Numerous deep learning algorithms have been inspired by and understood v...
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11/26/2022
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations
Recently the Transformer structure has shown good performances in graph ...
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06/27/2022
Robustness Implies Generalization via Data-Dependent Generalization Bounds
This paper proves that robustness implies generalization via data-depend...
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04/09/2022
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems
We consider Bayesian inference for large scale inverse problems, where c...
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10/19/2021
Long Random Matrices and Tensor Unfolding
In this paper, we consider the singular values and singular vectors of l...
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02/27/2021
Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution
The unscented Kalman inversion (UKI) method presented in [1] is a genera...
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02/21/2021
Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model
The unscented Kalman inversion (UKI) presented in [1] is a general deriv...
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12/26/2020
Power Iteration for Tensor PCA
In this paper, we study the power iteration algorithm for the spiked ten...
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10/20/2020
Towards Understanding the Dynamics of the First-Order Adversaries
An acknowledged weakness of neural networks is their vulnerability to ad...
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09/18/2019
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
The evolution of a deep neural network trained by the gradient descent c...
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08/05/2019
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes
In this paper, we theoretically prove that gradient descent can find a g...
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11/20/2018