1 code implementation • 15 Sep 2022 • Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky
Most existing PLMs are not tailored to the noisy user-generated text on social media, and the pre-training does not factor in the valuable social engagement logs available in a social network.
no code implementations • EMNLP 2020 • Thanh Tran, Yifan Hu, Changwei Hu, Kevin Yen, Fei Tan, Kyumin Lee, Serim Park
HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness.
no code implementations • CVPR 2018 • Serim Park, Matthew Thorpe
With experiments using 4 different datasets, we show that the generative tangent plane model in the optimal transport (OT) manifold can be learned with small numbers of images and can be used to create infinitely many `unseen' images.
no code implementations • 27 Sep 2016 • Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepčev
Transport based distances, such as the Wasserstein distance and earth mover's distance, have been shown to be an effective tool in signal and image analysis.
no code implementations • 15 Sep 2016 • Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepčev, Gustavo K. Rohde
Transport-based techniques for signal and data analysis have received increased attention recently.