Zobrazeno 1 - 10
of 18 381
pro vyhledávání: '"Junqi AN"'
Autor:
Zhaohui WANG, Mingzhen CHEN, Qiang LI, Wei WANG, Zengqiang LI, Desheng XU, Xiaochen ZHENG, Shaolong SUN, Chuanping WU, Xinyang GUO, Junqi AN
Publikováno v:
Meitan xuebao, Vol 49, Iss 4, Pp 1804-1818 (2024)
High stress is the main reason for high frequency of rock burst in deep coal mines. Backfilling mining serves as an effective method to control roof strata movement, alleviate the degree of stress concentration, and reduce the failure of surrounding
Externí odkaz:
https://doaj.org/article/ea14683555994b64ba896a55e3521bfe
Binary Neural Networks (BNNs) have garnered significant attention due to their immense potential for deployment on edge devices. However, the non-differentiability of the quantization function poses a challenge for the optimization of BNNs, as its de
Externí odkaz:
http://arxiv.org/abs/2412.11777
Vision-Language Models (VLMs) have shown promising capabilities in handling various multimodal tasks, yet they struggle in long-context scenarios, particularly in tasks involving videos, high-resolution images, or lengthy image-text documents. In our
Externí odkaz:
http://arxiv.org/abs/2412.09616
For end-to-end autonomous driving (E2E-AD), the evaluation system remains an open problem. Existing closed-loop evaluation protocols usually rely on simulators like CARLA being less realistic; while NAVSIM using real-world vision data, yet is limited
Externí odkaz:
http://arxiv.org/abs/2412.09647
Metaphor serves as an implicit approach to convey information, while enabling the generalized comprehension of complex subjects. However, metaphor can potentially be exploited to bypass the safety alignment mechanisms of Large Language Models (LLMs),
Externí odkaz:
http://arxiv.org/abs/2412.12145
Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. RMs approximate human preferences over possible LLM responses to the same prompt by predicting and comparing reward scores. Howev
Externí odkaz:
http://arxiv.org/abs/2411.16502
As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting their performance. Although current methods aim to enhance reasoning quali
Externí odkaz:
http://arxiv.org/abs/2412.07779
As an effective approach to equip models with multi-task capabilities without additional training, model merging has garnered significant attention. However, existing methods face challenges of redundant parameter conflicts and the excessive storage
Externí odkaz:
http://arxiv.org/abs/2412.00054
Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based J
Externí odkaz:
http://arxiv.org/abs/2411.09308
Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of labels for node classification,
Externí odkaz:
http://arxiv.org/abs/2411.06659