3 citations · 7 across the 3 of their papers we have counts for
7 papers
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Huimin Zeng, Jiahao Su, Furong Huang
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against -norm adversarial perturba…
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang
Enforcing orthogonality in neural networks is an antidote for gradient vanishing/exploding problems, sensitivity by adversarial perturbation, and bounding generalization errors. Ho…
ARMA Nets: Expanding Receptive Field for Dense Prediction
Jiahao Su, Shiqi Wang, Furong Huang
Global information is essential for dense prediction problems, whose goal is to compute a discrete or continuous label for each pixel in the images. Traditional convolutional layer…
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Jiahao Su, Wonmin Byeon, Jean Kossaifi +3
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.However, existing methods s…
Understanding Generalization in Deep Learning via Tensor Methods
Jingling Li, Yanchao Sun, Jiahao Su +2
Deep neural networks generalize well on unseen data though the number of parameters often far exceeds the number of training examples. Recently proposed complexity measures have pr…
Sampling-Free Learning of Bayesian Quantized Neural Networks
Jiahao Su, Milan Cvitkovic, Furong Huang
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesia…