no code implementations • EMNLP 2020 • Guoyin Wang, Chunyuan Li, Jianqiao Li, Hao Fu, Yuh-Chen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
1 code implementation • 16 Jun 2020 • Yulai Cong, Miaoyun Zhao, Jianqiao Li, Junya Chen, Lawrence Carin
An unbiased low-variance gradient estimator, termed GO gradient, was proposed recently for expectation-based objectives $\mathbb{E}_{q_{\boldsymbol{\gamma}}(\boldsymbol{y})} [f(\boldsymbol{y})]$, where the random variable (RV) $\boldsymbol{y}$ may be drawn from a stochastic computation graph with continuous (non-reparameterizable) internal nodes and continuous/discrete leaves.
1 code implementation • NeurIPS 2020 • Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin
As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfectly, no forgetting should be expected.
3 code implementations • 3 Jan 2019 • Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples.