111 citations · 149 across the 7 of their papers we have counts for
7 papers
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Zhengqi Gao, Fan-Keng Sun, Mingran Yang +7
Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-worl…
FreDo: Frequency Domain-based Long-Term Time Series Forecasting
Fan-Keng Sun, Duane S. Boning
The ability to forecast far into the future is highly beneficial to many applications, including but not limited to climatology, energy consumption, and logistics. However, due to…
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Fan-Keng Sun, Christopher I. Lang, Duane S. Boning
An increasing body of research focuses on using neural networks to model time series. A common assumption in training neural networks via maximum likelihood estimation on time seri…
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration
Fan-Keng Sun, Cheng-I Lai
Transformer-based language models have shown to be very powerful for natural language generation (NLG). However, text generation conditioned on some user inputs, such as topics or…
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun +2
We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly l…
LAMOL: LAnguage MOdeling for Lifelong Language Learning
Fan-Keng Sun, Cheng-Hao Ho, Hung-Yi Lee
Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on langu…
Temporal Pattern Attention for Multivariate Time Series Forecasting
Shun-Yao Shih, Fan-Keng Sun, Hung-yi Lee
Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. H…