413 citations · 1.2k across the 20 of their papers we have counts for
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An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets
Po-Yi Lu, Yi-Jie Cheng, Chun-Liang Li +1
Active Learning (AL) addresses the crucial challenge of enabling machines to efficiently gather labeled examples through strategic queries. Among the many AL strategies, Uncertaint…
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Generative Adversarial Networks (GANs) is a powerful family of models that learn an underlying distribution to generate synthetic data. Many existing studies of GANs focus on impro…
Interpretable Sequence Learning for COVID-19 Forecasting
Sercan O. Arik, Chun-Liang Li, Jinsung Yoon +12
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19. Our model is explainable by design as it ex…
Unsupervised Program Synthesis for Images By Sampling Without Replacement
Chenghui Zhou, Chun-Liang Li, Barnabas Poczos
Program synthesis has emerged as a successful approach to the image parsing task. Most prior works rely on a two-step scheme involving supervised pretraining of a Seq2Seq model wit…
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li +2
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial me…
Data-driven Random Fourier Features using Stein Effect
Wei-Cheng Chang, Chun-Liang Li, Yiming Yang +1
Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht,…
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng +2
Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample…