Search Results for author: Yunzhe Tao

Found 22 papers, 5 papers with code

A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems

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.

Task-Oriented Dialogue Systems

ViTAR: Vision Transformer with Any Resolution

no code implementations27 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.

Self-Supervised Learning Semantic Segmentation

Video-CSR: Complex Video Digest Creation for Visual-Language Models

no code implementations8 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).

Retrieval Sentence +1

Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling

no code implementations8 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.

Text Generation Video Summarization

Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction

1 code implementation5 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.

Representation Learning Text Generation

$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis

no code implementations4 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.

Code Generation Program Synthesis +2

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

1 code implementation18 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.

Recommendation Systems

A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems

1 code implementation10 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.

Task-Oriented Dialogue Systems

Robust Multi-Agent Reinforcement Learning with Model Uncertainty

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.

Multi-agent Reinforcement Learning Q-Learning +2

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

no code implementations24 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.

reinforcement-learning Reinforcement Learning (RL) +1

REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning

no code implementations28 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.

reinforcement-learning Reinforcement Learning (RL) +1

Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks

no code implementations2 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.

Autonomous Driving Deep Attention +4

FineText: Text Classification via Attention-based Language Model Fine-tuning

no code implementations25 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.

Benchmarking General Classification +4

Stochastic Training of Residual Networks: a Differential Equation Viewpoint

no code implementations1 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.

Image Classification

Explaining Deep Learning Models -- A Bayesian Non-parametric Approach

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.

Explaining Deep Learning Models - A Bayesian Non-parametric Approach

no code implementations7 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.

Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling

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.

A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

no code implementations9 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.

Abstractive Text Summarization Informativeness

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