61 citations · 130 across the 8 of their papers we have counts for
23 papers
Towards Adversarial Robustness via Transductive Learning
Jiefeng Chen, Yang Guo, Xi Wu +4
There has been emerging interest to use transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to traditional "test-time…
Deep Online Fused Video Stabilization
Zhenmei Shi, Fuhao Shi, Wei-Sheng Lai +2
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning. The network fuses…
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding
Zhao Jinman, Shawn Zhong, Xiaomin Zhang +1
We look into the task of \emph{generalizing} word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-…
Can Adversarial Weight Perturbations Inject Neural Backdoors?
Siddhant Garg, Adarsh Kumar, Vibhor Goel +1
Adversarial machine learning has exposed several security hazards of neural models and has become an important research topic in recent times. Thus far, the concept of an "adversar…
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Siddhant Garg, Yingyu Liang
Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks. While these approaches are widely used in…
Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model
Yingyu Liang, Hui Yuan
In the setting of entangled single-sample distributions, the goal is to estimate some common parameter shared by a family of distributions, given one single sample from each di…