no code implementations • 3 May 2024 • Zhiwei Bao, Liu Liao-Liao, Zhiyu Wu, Yifan Zhou, Dan Fan, Michal Aibin, Yvonne Coady, Andrew Brownsword
The exponential growth of artificial intelligence (AI) and machine learning (ML) applications has necessitated the development of efficient storage solutions for vector and tensor data.
no code implementations • 27 Mar 2024 • Hanxiao Zhang, Yifan Zhou, Guo-Hua Wang, Jianxin Wu
In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes.
2 code implementations • 19 Mar 2024 • Shuai Yang, Yifan Zhou, Ziwei Liu, Chen Change Loy
In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint.
no code implementations • 19 Mar 2024 • Kaile Du, Yifan Zhou, Fan Lyu, Yuyang Li, Chen Lu, Guangcan Liu
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable.
1 code implementation • 11 Mar 2024 • Zihao Tang, Zheqi Lv, Shengyu Zhang, Yifan Zhou, Xinyu Duan, Fei Wu, Kun Kuang
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
1 code implementation • 7 Mar 2024 • Cheng Peng, Yutao Tang, Yifan Zhou, Nengyu Wang, Xijun Liu, Deming Li, Rama Chellappa
Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry.
no code implementations • 18 Feb 2024 • Yew Kee Wonga, Yifan Zhou, Yan Shing Liang
By training and employing a machine learning model that identifies and corrects the noise in quantum processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency.
no code implementations • 6 Feb 2024 • Lin Guan, Yifan Zhou, Denis Liu, Yantian Zha, Heni Ben Amor, Subbarao Kambhampati
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
no code implementations • 22 Jan 2024 • Sihan Niu, Yifan Zhou, Zhikai Li, Shuyao Huang, Yujun Zhou
This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation.
no code implementations • 12 Dec 2023 • Kaiwen Zhang, Yifan Zhou, Xudong Xu, Xingang Pan, Bo Dai
Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation.
no code implementations • 27 Nov 2023 • Siyuan Huang, Yifan Zhou, Ram Prabhakar, Xijun Liu, Yuxiang Guo, Hongrui Yi, Cheng Peng, Rama Chellappa, Chun Pong Lau
To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models.
no code implementations • 15 Nov 2023 • Zimin Jiang, Peng Zhang, Yifan Zhou, Łukasz Kocewiak, Divya Kurthakoti Chandrashekhara, Marie-Lou Picherit, Zefan Tang, Kenneth B. Bowes, Guangya Yang
Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids.
no code implementations • 29 Sep 2023 • Qing Shen, Yifan Zhou, Peng Zhang
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
no code implementations • 29 Sep 2023 • Qing Shen, Yifan Zhou, Qiang Zhang, Slava Maslennikov, Xiaochuan Luo, Peng Zhang
The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models.
no code implementations • 29 Sep 2023 • Qing Shen, Yifan Zhou, Huanfeng Zhao, Peng Zhang, Qiang Zhang, Slava Maslenniko, Xiaochuan Luo
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components.
no code implementations • 14 Aug 2023 • Yifan Zhou, Yew Kee Wong, Yan Shing Liang, Haichuan Qiu, Yu Xi Wu, Bin He
This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations.
no code implementations • 13 Jun 2023 • Shuai Yang, Yifan Zhou, Ziwei Liu, Chen Change Loy
The framework includes two parts: key frame translation and full video translation.
no code implementations • 25 Aug 2022 • Fei Feng, Yifan Zhou, Peng Zhang
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems.
1 code implementation • 27 Feb 2022 • Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, Ze Wang, Yaozu Ye, Zhe Yin, Shi Meng, Peng Li, Kaiwei Wang
PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks.
1 code implementation • 2 Feb 2022 • Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, Kaiwei Wang
In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving--CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI).
no code implementations • 11 Nov 2021 • Xuechun Li, Xueyao Sun, Zewei Xu, Yifan Zhou
For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms.
no code implementations • 10 Apr 2021 • Yifan Zhou, Peng Zhang
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids.
no code implementations • 28 Mar 2021 • Yifan Zhou, Yifan Ge, Jianxin Wu
Learning from examples with noisy labels has attracted increasing attention recently.
no code implementations • 13 Jan 2021 • Yifan Zhou, Peng Zhang
A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties.
no code implementations • 9 Jun 2020 • Ludmila Carone, Paul Mollière, Yifan Zhou, Jeroen Bouwman, Fei Yan, Robin Baeyens, Dániel Apai, Nestor Espinoza, Benjamin V. Rackham, Andrés Jordán, Daniel Angerhausen, Leen Decin, Monika Lendl, Olivia Venot, Thomas Henning
Using a 1D atmosphere model with isothermal temperature, uniform cloud deck and equilibrium chemistry, the Bayesian evidence of a retrieval analysis of the transmission spectrum indicates a preference for a high atmospheric metallicity ${\rm [Fe/H]}=2. 58^{+0. 26}_{-0. 37}$ and clear skies.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
no code implementations • 6 Feb 2020 • Youcheng Sun, Yifan Zhou, Simon Maskell, James Sharp, Xiaowei Huang
However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability.
2 code implementations • 5 Nov 2019 • Yifan Zhou, Simon Maskell
This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated.