Search Results for author: Ke Bai

Found 8 papers, 3 papers with code

Variational Inference with Holder Bounds

no code implementations4 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.

Variational Inference

Semantic Matching for Sequence-to-Sequence Learning

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.

Weakly supervised cross-domain alignment with optimal transport

no code implementations14 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.

Learning Implicit Text Generation via Feature Matching

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.

Conditional Text Generation Style Transfer +2

Regularizing Reasons for Outfit Evaluation with Gradient Penalty

no code implementations2 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.

GO Gradient for Expectation-Based Objectives

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 $\Ebb_{q_{\gammav} (\yv)} [f(\yv)]$.

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

1 code implementation3 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.

Q-Learning

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