no code implementations • 6 Feb 2024 • Jesús Bobadilla, Abraham Gutierrez, Fernando Ortega, Bo Zhu
Both quality measures are based on the hypothesis that the more suitable a reliability measure is, the better accuracy results it will provide when applied.
1 code implementation • 1 Feb 2024 • Fernando Ortega, Bo Zhu, Jesus Bobadilla, Antonio Hernando
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem.
no code implementations • 22 Dec 2023 • Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu, Bo Zhu
We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena.
no code implementations • NeurIPS 2023 • Hong-Xing Yu, Yang Zheng, Yuan Gao, Yitong Deng, Bo Zhu, Jiajun Wu
Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density.
1 code implementation • 30 Oct 2023 • Tianwen Wei, Liang Zhao, Lichang Zhang, Bo Zhu, Lijie Wang, Haihua Yang, Biye Li, Cheng Cheng, Weiwei Lü, Rui Hu, Chenxia Li, Liu Yang, Xilin Luo, Xuejie Wu, Lunan Liu, Wenjun Cheng, Peng Cheng, Jianhao Zhang, XiaoYu Zhang, Lei Lin, Xiaokun Wang, Yutuan Ma, Chuanhai Dong, Yanqi Sun, Yifu Chen, Yongyi Peng, Xiaojuan Liang, Shuicheng Yan, Han Fang, Yahui Zhou
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
1 code implementation • 25 Oct 2023 • Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu
Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.
no code implementations • 12 May 2023 • Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei B. Li, Bruce R. Rosen, Matthew S. Rosen
We compare our proposed approach with baseline methods: Monte-Carlo dropout and deep ensembles, and further analysis included MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction using UNET architectures.
no code implementations • CVPR 2023 • Ziyu Wan, Christian Richardt, Aljaž Božič, Chao Li, Vijay Rengarajan, Seonghyeon Nam, Xiaoyu Xiang, Tuotuo Li, Bo Zhu, Rakesh Ranjan, Jing Liao
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality.
no code implementations • 10 Mar 2023 • Ziqian Wu, Xingzhe He, Yijun Li, Cheng Yang, Rui Liu, Shiying Xiong, Bo Zhu
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
1 code implementation • 4 Mar 2023 • Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
no code implementations • 27 Jan 2023 • Yitong Deng, Hong-Xing Yu, Jiajun Wu, Bo Zhu
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video.
1 code implementation • 7 Dec 2022 • Gyeongmin Choe, Beibei Du, Seonghyeon Nam, Xiaoyu Xiang, Bo Zhu, Rakesh Ranjan
To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks.
1 code implementation • 7 Dec 2022 • Zheng Zhang, Qingrui Zhang, Bo Zhu, Xiaohan Wang, Tianjiang Hu
In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies.
no code implementations • 4 Sep 2022 • Daniel M. DiPietro, Bo Zhu
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data.
no code implementations • 15 Mar 2022 • Xiaoyu Xiang, Yapeng Tian, Vijay Rengarajan, Lucas Young, Bo Zhu, Rakesh Ranjan
Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts.
no code implementations • 10 Feb 2022 • David E. J. Waddington, Nicholas Hindley, Neha Koonjoo, Christopher Chiu, Tess Reynolds, Paul Z. Y. Liu, Bo Zhu, Danyal Bhutto, Chiara Paganelli, Paul J. Keall, Matthew S. Rosen
The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation.
no code implementations • ACL 2021 • Jiefu Gong, Xiao Hu, Wei Song, Ruiji Fu, Zhichao Sheng, Bo Zhu, Shijin Wang, Ting Liu
IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment.
no code implementations • 4 Mar 2021 • Zhekun Shi, Di Tan, Quan Liu, Fandong Meng, Bo Zhu, Longjian Xue
Bioinspired structure adhesives have received increasing interest for many applications, such as climbing robots and medical devices.
Soft Condensed Matter
no code implementations • 1 Jan 2021 • Shiying Xiong, Xingzhe He, Yunjin Tong, Yitong Deng, Bo Zhu
Since the number of such vortices are much smaller than that of the Eulerian, grid discretization, this Lagrangian discretization in essence encodes the system dynamics on a compact physics-based latent space.
no code implementations • ICLR 2021 • Shiying Xiong, Yunjin Tong, Xingzhe He, Shuqi Yang, Cheng Yang, Bo Zhu
The enabling mechanics of our approach is an augmented symplectic time integrator to decouple the position and momentum energy terms and facilitate their evolution.
1 code implementation • NeurIPS 2020 • Shuqi Yang, Xingzhe He, Bo Zhu
A neural projection operator lies at the heart of our approach, composed of a lightweight network with an embedded recursive architecture that interactively enforces learned underpinning constraints and predicts the various governed behaviors of different physical systems.
1 code implementation • NeurIPS 2020 • Daniel M. DiPietro, Shiying Xiong, Bo Zhu
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data.
no code implementations • 7 Jun 2020 • Shiying Xiong, Xingzhe He, Yunjin Tong, Runze Liu, Bo Zhu
The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches.
no code implementations • 7 Jun 2020 • Shiying Xiong, Xingzhe He, Yunjin Tong, Yitong Deng, Bo Zhu
To tackle this challenge, we propose a novel learning-based framework, the Neural Vortex Method (NVM), which builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner.
1 code implementation • 11 May 2020 • Yunjin Tong, Shiying Xiong, Xingzhe He, Guanghan Pan, Bo Zhu
We propose an effective and lightweight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations.
2 code implementations • 2 Mar 2020 • Yue Li, Xuan Li, Minchen Li, Yixin Zhu, Bo Zhu, Chenfanfu Jiang
A quadrature-level connectivity graph-based method is adopted to avoid the artificial checkerboard issues commonly existing in multi-resolution topology optimization methods.
Computational Physics Computational Engineering, Finance, and Science Graphics
1 code implementation • ICLR 2020 • Xingzhe He, Helen Lu Cao, Bo Zhu
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics.
no code implementations • 15 Oct 2017 • Ouri Cohen, Bo Zhu, Matthew S. Rosen
The accuracy of the NN reconstruction of noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in a both simulated numerical brain phantom data and acquired data from the ISMRM/NIST phantom.
1 code implementation • 28 Apr 2017 • Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen
Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and radio astronomy.