43 citations · 51 across the 4 of their papers we have counts for
7 papers
Benchmarking Domain Generalization on EEG-based Emotion Recognition
Yan Li, Hao Chen, Jake Zhao +2
Electroencephalography (EEG) based emotion recognition has demonstrated tremendous improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms have been e…
Joining datasets via data augmentation in the label space for neural networks
Jake Zhao, Mingfeng Ou, Linji Xue +3
Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic w…
A critical look at the current train/test split in machine learning
Jimin Tan, Jianan Yang, Sai Wu +2
The randomized or cross-validated split of training and testing sets has been adopted as the gold standard of machine learning for decades. The establishment of these split protoco…
Interactive Rainbow Score: A Visual-centered Multimodal Flute Tutoring System
Daniel Chin, Yian Zhang, Tianyu Zhang +2
Learning to play an instrument is intrinsically multimodal, and we have seen a trend of applying visual and haptic feedback in music games and computer-aided music tutoring systems…
Levenshtein Transformer
Jiatao Gu, Changhan Wang, Jake Zhao
Modern neural sequence generation models are built to either generate tokens step-by-step from scratch or (iteratively) modify a sequence of tokens bounded by a fixed length. In th…
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
Jake Zhao, Kyunghyun Cho
We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid archi…