Zobrazeno 1 - 10
of 13 542
pro vyhledávání: '"Guo, Jian"'
The machine learning problem of extracting neural network parameters has been proposed for nearly three decades. Functionally equivalent extraction is a crucial goal for research on this problem. When the adversary has access to the raw output of neu
Externí odkaz:
http://arxiv.org/abs/2409.11646
The DArk Matter Particle Explorer (DAMPE) is a satellite-borne particle detector for measurements of high-energy cosmic rays and {\gamma}-rays. DAMPE has been operating smoothly in space for more than 8 years since launch on December 17, 2015. The tr
Externí odkaz:
http://arxiv.org/abs/2409.03352
Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. Howe
Externí odkaz:
http://arxiv.org/abs/2409.03277
Autor:
Chen, Zewen, Xu, Sunhan, Zeng, Yun, Guo, Haochen, Guo, Jian, Liu, Shuai, Wang, Juan, Li, Bing, Hu, Weiming, Liu, Dehua, Li, Hesong
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience. However, existing NR-IQA methods
Externí odkaz:
http://arxiv.org/abs/2409.01212
Autor:
Guo, Jian, Shum, Heung-Yeung
Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs. To overcome these challenges, we introduce the Large Investment Model (LIM), a novel research paradigm designed to enhance both pe
Externí odkaz:
http://arxiv.org/abs/2408.10255
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant drawback of MLLMs is their reliance on static training data, leading to ou
Externí odkaz:
http://arxiv.org/abs/2407.21439
Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated content. However, these systems often falter with comple
Externí odkaz:
http://arxiv.org/abs/2407.10805
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and li
Externí odkaz:
http://arxiv.org/abs/2407.08500
Autor:
Wrobel, Julia, Sauerbrei, Britton, Kirk, Erik A., Guo, Jian-Zhong, Hantman, Adam, Goldsmith, Jeff
We are motivated by a study that seeks to better understand the dynamic relationship between muscle activation and paw position during locomotion. For each gait cycle in this experiment, activation in the biceps and triceps is measured continuously a
Externí odkaz:
http://arxiv.org/abs/2406.19535
In our previous study, we introduced a machine-learning technique, namely CMBFSCNN, for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data
Externí odkaz:
http://arxiv.org/abs/2406.17685