1 code implementation • ACL (dialdoc) 2021 • Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang
Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
Ranked #9 on Task-Oriented Dialogue Systems on KVRET
no code implementations • 27 Mar 2024 • Qihang Fan, Quanzeng You, Xiaotian Han, Yongfei Liu, Yunzhe Tao, Huaibo Huang, Ran He, Hongxia Yang
Firstly, we propose a novel module for dynamic resolution adjustment, designed with a single Transformer block, specifically to achieve highly efficient incremental token integration.
no code implementations • 11 Mar 2024 • Yufeng Zhang, Liyu Chen, Boyi Liu, Yingxiang Yang, Qiwen Cui, Yunzhe Tao, Hongxia Yang
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale.
no code implementations • 3 Mar 2024 • Haogeng Liu, Quanzeng You, Xiaotian Han, Yiqi Wang, Bohan Zhai, Yongfei Liu, Yunzhe Tao, Huaibo Huang, Ran He, Hongxia Yang
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently.
Ranked #34 on Visual Question Answering on MM-Vet
no code implementations • 8 Oct 2023 • Tingkai Liu, Yunzhe Tao, Haogeng Liu, Qihang Fan, Ding Zhou, Huaibo Huang, Ran He, Hongxia Yang
We present a novel task and human annotated dataset for evaluating the ability for visual-language models to generate captions and summaries for real-world video clips, which we call Video-CSR (Captioning, Summarization and Retrieval).
no code implementations • 8 Oct 2023 • Haogeng Liu, Qihang Fan, Tingkai Liu, Linjie Yang, Yunzhe Tao, Huaibo Huang, Ran He, Hongxia Yang
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text generation task.
1 code implementation • 5 Oct 2023 • Yiren Jian, Tingkai Liu, Yunzhe Tao, Chunhui Zhang, Soroush Vosoughi, Hongxia Yang
Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance.
no code implementations • 4 Oct 2023 • Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, Hongxia Yang
Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods.
1 code implementation • 18 Nov 2022 • Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
1 code implementation • 10 Jun 2021 • Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang
Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability.
no code implementations • NeurIPS 2020 • Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar
In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.
no code implementations • 24 Nov 2020 • Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya
Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low.
no code implementations • 28 Sep 2020 • Yunzhe Tao, Sahika Genc, Tao Sun, Sunil Mallya
Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown.
no code implementations • 2 Jan 2020 • Sahika Genc, Sunil Mallya, Sravan Bodapati, Tao Sun, Yunzhe Tao
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.
no code implementations • 5 Nov 2019 • Bharathan Balaji, Sunil Mallya, Sahika Genc, Saurabh Gupta, Leo Dirac, Vineet Khare, Gourav Roy, Tao Sun, Yunzhe Tao, Brian Townsend, Eddie Calleja, Sunil Muralidhara, Dhanasekar Karuppasamy
DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems.
no code implementations • 25 Oct 2019 • Yunzhe Tao, Saurabh Gupta, Satyapriya Krishna, Xiong Zhou, Orchid Majumder, Vineet Khare
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge, which usually cannot be satisfied by the customers.
no code implementations • 1 Dec 2018 • Qi Sun, Yunzhe Tao, Qiang Du
During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models.
no code implementations • NeurIPS 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
no code implementations • 7 Nov 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
no code implementations • NeurIPS 2018 • Yunzhe Tao, Qi Sun, Qiang Du, Wei Liu
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies in the feature space.
2 code implementations • 2 Jun 2018 • Yunzhe Tao, Lin Ma, Weizhong Zhang, Jian Liu, Wei Liu, Qiang Du
Time series prediction has been studied in a variety of domains.
no code implementations • 9 May 2018 • Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, Qiang Du
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization.
Ranked #6 on Text Summarization on DUC 2004 Task 1