Zobrazeno 1 - 10
of 8 696
pro vyhledávání: '"LIANG, Feng"'
Artifact detection algorithms are crucial to correcting the output generated by diffusion models. However, because of the variety of artifact forms, existing methods require substantial annotated data for training. This requirement limits their scala
Externí odkaz:
http://arxiv.org/abs/2412.17109
Emotion recognition based on body movements is vital in human-computer interaction. However, existing emotion recognition methods predominantly focus on enhancing classification accuracy, often neglecting the provision of textual explanations to just
Externí odkaz:
http://arxiv.org/abs/2412.12581
Improving hydrocarbon production with hydraulic fracturing from unconventional reservoirs requires investigating transport phenomena at the single fracture level. In this study, we simulated geomechanical deformation, fluid flow, and reactive transpo
Externí odkaz:
http://arxiv.org/abs/2411.07992
Model Compression has drawn much attention within the deep learning community recently. Compressing a dense neural network offers many advantages including lower computation cost, deployability to devices of limited storage and memories, and resistan
Externí odkaz:
http://arxiv.org/abs/2411.00273
This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smo
Externí odkaz:
http://arxiv.org/abs/2411.00256
Autor:
Guo, Zhangyuan, Ge, Min, Zhou, You-Qi, Bi, Jiachang, Zhang, Qinghua, Zhang, Jiahui, Ye, Jin-Tao, Zhai, Rongjing, Ge, Fangfang, Huang, Yuan, Zhang, Ruyi, Yao, Xiong, Huang, Liang-Feng, Cao, Yanwei
Publikováno v:
Materials Horizons 2024
Superconductors, an essential class of functional materials, hold a vital position in both fundamental science and practical applications. However, most superconductors, including MgB$_2$, Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, and FeSe, are highly sens
Externí odkaz:
http://arxiv.org/abs/2410.17588
We introduce a novel Bayesian approach for both covariate selection and sparse precision matrix estimation in the context of high-dimensional Gaussian graphical models involving multiple responses. Our approach provides a sparse estimation of the thr
Externí odkaz:
http://arxiv.org/abs/2409.16276
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks, yet they occasionally tend to yield content that factually inaccurate or discordant with the expected output, a phenomenon empiri
Externí odkaz:
http://arxiv.org/abs/2408.08769
Recently learned image compression (LIC) has achieved great progress and even outperformed the traditional approach using DCT or discrete wavelet transform (DWT). However, LIC mainly reduces spatial redundancy in the autoencoder networks and entropy
Externí odkaz:
http://arxiv.org/abs/2407.09983
Autor:
Liang, Feng, Zhang, Zhen, Lu, Haifeng, Li, Chengming, Leung, Victor C. M., Guo, Yanyi, Hu, Xiping
With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and workload scheduling have become the key to high-performance deep learning. T
Externí odkaz:
http://arxiv.org/abs/2406.08115