1 code implementation • 6 Sep 2023 • Pengyu Cheng, Jiawen Xie, Ke Bai, Yong Dai, Nan Du
Besides, from the perspective of data efficiency, we propose a three-stage customized RM learning scheme, then empirically verify its effectiveness on both general preference datasets and our DSP set.
no code implementations • 9 Mar 2023 • Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin
Open world classification is a task in natural language processing with key practical relevance and impact.
no code implementations • 20 Sep 2022 • Ke Bai, Aonan Zhang, Zhizhong Li, Ricardo Heano, Chong Wang, Lawrence Carin
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item.
no code implementations • 4 Nov 2021 • Junya Chen, Danni Lu, Zidi Xiu, Ke Bai, Lawrence Carin, Chenyang Tao
In this work, we present a careful analysis of the thermodynamic variational objective (TVO), bridging the gap between existing variational objectives and shedding new insights to advance the field.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai, Lawrence Carin
In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences.
no code implementations • 14 Aug 2020 • Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
no code implementations • ACL 2020 • Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.
no code implementations • 2 Feb 2020 • Xingxing Zou, Zhizhong Li, Ke Bai, Dahua Lin, Waikeung Wong
In this paper, we build an outfit evaluation system which provides feedbacks consisting of a judgment with a convincing explanation.
1 code implementation • NeurIPS 2019 • Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy Bobashev, Lawrence Carin
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
1 code implementation • ICLR 2019 • Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin
Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\gammav$ for expectation-based objectives $\Ebb_{q_{\gammav} (\yv)} [f(\yv)]$.
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.