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06/13/2023
Reviving Shift Equivariance in Vision Transformers
Shift equivariance is a fundamental principle that governs how we percei...
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11/02/2022
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Data augmentation is a critical contributing factor to the success of de...
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08/01/2021
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Randomized Smoothing (RS), being one of few provable defenses, has been ...
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06/16/2021
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
Enforcing orthogonality in neural networks is an antidote for gradient v...
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02/21/2020
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Higher-order Recurrent Neural Networks (RNNs) are effective for long-ter...
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01/14/2020
Understanding Generalization in Deep Learning via Tensor Methods
Deep neural networks generalize well on unseen data though the number of...
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12/06/2019
Sampling-Free Learning of Bayesian Quantized Neural Networks
Bayesian learning of model parameters in neural networks is important in...
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05/25/2018