no code implementations • 21 Mar 2024 • Zijie Wu, Mingtao Feng, Yaonan Wang, He Xie, Weisheng Dong, Bo Miao, Ajmal Mian
Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes.
no code implementations • ICCV 2023 • Zijie Wu, Yaonan Wang, Mingtao Feng, He Xie, Ajmal Mian
In this paper, we propose a sketch and text guided probabilistic diffusion model for colored point cloud generation that conditions the denoising process jointly with a hand drawn sketch of the object and its textual description.
no code implementations • 18 May 2023 • Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan
We formulate the model stability problem by studying how the predictions of a model change, even when it is retrained on the same data, as a consequence of stochasticity in the training process.
1 code implementation • Findings (EMNLP) 2021 • Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
no code implementations • NAACL 2021 • Luoxin Chen, Francisco Garcia, Varun Kumar, He Xie, Jianhua Lu
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
no code implementations • 9 Oct 2019 • Eunah Cho, He Xie, John P. Lalor, Varun Kumar, William M. Campbell
In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
no code implementations • WS 2019 • Eunah Cho, He Xie, William M. Campbell
Semi-supervised learning is an efficient way to improve performance for natural language processing systems.