Zobrazeno 1 - 10
of 14 819
pro vyhledávání: '"Ma Chao"'
Autor:
Liu, Chao, Ma, Chao, Chang, Tieyan, Wang, Xiaoli, Fan, Chuanyan, Han, Lu, Li, Feiyu, Wang, Shanpeng, Chen, Yu-Sheng, Zhang, Junjie
Publikováno v:
Crystal Growth & Design (2024)
Quasi-two-dimensional averievites with triangle-kagome-triangle trilayers are of interest due to their rich structural and magnetic transitions and strong spin frustration that are expected to host quantum spin liquid ground state with suitable subst
Externí odkaz:
http://arxiv.org/abs/2411.08596
In the industry, numerous tasks are deployed online. Traditional approaches often tackle each task separately by its own network, which leads to excessive costs for developing and scaling models, especially in the context of large language models. Al
Externí odkaz:
http://arxiv.org/abs/2411.03644
Autor:
Wu, Yu, Huang, Daiqiang, Zhang, Huanyu, Guarino, Anita, Fittipaldi, Rosalba, Ma, Chao, Hu, Wenjie, Chang, Niu, Wang, Zhen, Yu, Weichao, Yerin, Yuriy, Vecchione, Antonio, Liu, Yang, Cuoco, Mario, Guo, Hangwen, Shen, Jian
The relation between superconductivity and time-reversal symmetry (TRS) is one of the most fascinating problems in condensed matter physics. Although most superconductors inherently possess TRS, nonmagnetic disorder can induce states that demonstrate
Externí odkaz:
http://arxiv.org/abs/2411.01232
Autor:
Li, Xirui, Herrmann, Charles, Chan, Kelvin C. K., Li, Yinxiao, Sun, Deqing, Ma, Chao, Yang, Ming-Hsuan
Recent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional generation. Instead of proposing another specialized technique, we in
Externí odkaz:
http://arxiv.org/abs/2410.11439
While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI systems often struggle. Current methods for visual grounding of dynamics either use pure neural-network-based simulators (black box), which may violate physica
Externí odkaz:
http://arxiv.org/abs/2410.08257
Gradient Smoothing is an efficient approach to reducing noise in gradient-based model explanation method. SmoothGrad adds Gaussian noise to mitigate much of these noise. However, the crucial hyper-parameter in this method, the variance $\sigma$ of Ga
Externí odkaz:
http://arxiv.org/abs/2410.07711
Recent advancements in State Space Models, notably Mamba, have demonstrated superior performance over the dominant Transformer models, particularly in reducing the computational complexity from quadratic to linear. Yet, difficulties in adapting Mamba
Externí odkaz:
http://arxiv.org/abs/2410.06806
Spatiotemporal predictive learning methods generally fall into two categories: recurrent-based approaches, which face challenges in parallelization and performance, and recurrent-free methods, which employ convolutional neural networks (CNNs) as enco
Externí odkaz:
http://arxiv.org/abs/2410.04733
Large-scale generative models have achieved remarkable success in a number of domains. However, for sequential decision-making problems, such as robotics, action-labelled data is often scarce and therefore scaling-up foundation models for decision-ma
Externí odkaz:
http://arxiv.org/abs/2410.12822
Open-vocabulary image semantic segmentation (OVS) seeks to segment images into semantic regions across an open set of categories. Existing OVS methods commonly depend on foundational vision-language models and utilize similarity computation to tackle
Externí odkaz:
http://arxiv.org/abs/2409.07683