Search Results for author: Ziyan Wang

Found 24 papers, 5 papers with code

Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation

no code implementations25 Mar 2024 Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, Jie Zhang

However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations.

Language Modelling Large Language Model +1

ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image

no code implementations15 Mar 2024 Marco Pesavento, Yuanlu Xu, Nikolaos Sarafianos, Robert Maier, Ziyan Wang, Chun-Han Yao, Marco Volino, Edmond Boyer, Adrian Hilton, Tony Tung

In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.

Large Language Model with Graph Convolution for Recommendation

no code implementations14 Feb 2024 Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, Youchen Sun

To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.

Hallucination Language Modelling +1

Natural Language Reinforcement Learning

no code implementations11 Feb 2024 Xidong Feng, Ziyu Wan, Mengyue Yang, Ziyan Wang, Girish A. Koushik, Yali Du, Ying Wen, Jun Wang

Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.

Decision Making reinforcement-learning +1

Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models

no code implementations15 Jan 2024 Xingzhou Lou, Junge Zhang, Ziyan Wang, Kaiqi Huang, Yali Du

Through the use of pre-trained LMs and the elimination of the need for a ground-truth cost, our method enhances safe policy learning under a diverse set of human-derived free-form natural language constraints.

Reinforcement Learning (RL) Safe Reinforcement Learning

A Local Appearance Model for Volumetric Capture of Diverse Hairstyle

no code implementations14 Dec 2023 Ziyan Wang, Giljoo Nam, Aljaz Bozic, Chen Cao, Jason Saragih, Michael Zollhoefer, Jessica Hodgins

In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.

MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment

no code implementations6 Dec 2023 Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang

Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky.

Multi-agent Reinforcement Learning reinforcement-learning

A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music

no code implementations26 Aug 2023 Zeyu Xiong, Weitao Wang, Jing Yu, Yue Lin, Ziyan Wang

In recent years, AI-generated music has made significant progress, with several models performing well in multimodal and complex musical genres and scenes.

ChessGPT: Bridging Policy Learning and Language Modeling

1 code implementation NeurIPS 2023 Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang

Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.

Decision Making Language Modelling

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach

no code implementations NeurIPS 2023 Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we propose to explicitly model the contributions of state and action from a causal perspective, resulting in an interpretable reward redistribution and preserving policy invariance.


Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images

no code implementations28 Jul 2022 Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang, Chenglei Wu, Sven Behnke, Giljoo Nam

Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands.

Neural Rendering

Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

1 code implementation14 Feb 2022 Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Mguni, Jun Wang, Haitham Bou-Ammar

Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.

reinforcement-learning Reinforcement Learning (RL) +1

HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

no code implementations CVPR 2022 Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhoefer, Jessica Hodgins, Christoph Lassner

Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars.

Neural Rendering Optical Flow Estimation

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention

no code implementations27 Oct 2021 David Mguni, Usman Islam, Yaqi Sun, Xiuling Zhang, Joel Jennings, Aivar Sootla, Changmin Yu, Ziyan Wang, Jun Wang, Yaodong Yang

In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.

OpenAI Gym reinforcement-learning +3

Multi-Agent Constrained Policy Optimisation

3 code implementations6 Oct 2021 Shangding Gu, Jakub Grudzien Kuba, Munning Wen, Ruiqing Chen, Ziyan Wang, Zheng Tian, Jun Wang, Alois Knoll, Yaodong Yang

To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.

Multi-agent Reinforcement Learning reinforcement-learning +1

Learning Compositional Radiance Fields of Dynamic Human Heads

1 code implementation CVPR 2021 Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhöfer

In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.

Neural Rendering Synthetic Data Generation

Geometry-Aware Recurrent Neural Networks for Active Visual Recognition

no code implementations NeurIPS 2018 Ricson Cheng, Ziyan Wang, Katerina Fragkiadaki

We present recurrent geometry-aware neural networks that integrate visual information across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations.

3D Reconstruction Object +3

Semantic Photometric Bundle Adjustment on Natural Sequences

no code implementations30 Nov 2017 Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey

More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames.

Object Object Reconstruction

Object-Centric Photometric Bundle Adjustment with Deep Shape Prior

no code implementations4 Nov 2017 Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey

Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision.


Virtual to Real Reinforcement Learning for Autonomous Driving

6 code implementations13 Apr 2017 Xinlei Pan, Yurong You, Ziyan Wang, Cewu Lu

To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.

Autonomous Driving Domain Adaptation +5

Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition

no code implementations6 Apr 2016 Ziyan Wang, Jiwen Lu, Ruogu Lin, Jianjiang Feng, Jie zhou

Specifically, we construct a pair of deep convolutional neural networks (CNNs) for the RGB and depth data, and concatenate them at the top layer of the network with a loss function which learns a new feature space where both correlated part and the individual part of the RGB-D information are well modelled.

Object Object Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.