CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction [0.03%]
CrunchLLM：用于结构化业务推理和结果预测的多任务LLM
Rabeya Tus Sadia,Qiang Cheng
Rabeya Tus Sadia
Predicting the success of startup companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., ...
Deep Learning for analyzing chaotic dynamics in biological time series: Insights from frog heart signals [0.03%]
深度学习在分析生物时间序列混沌动力学中的应用——来自蛙心信号的启示
Carmen Mayora-Cebollero,Flavio H Fenton,Molly Halprin et al.
Carmen Mayora-Cebollero et al.
The study of experimental data is a relevant task in several physical, chemical and biological applications. In particular, the analysis of chaotic dynamics in cardiac systems is crucial as it can be related to certain pathological arrhythm...
SymRefine: A symbolic regression approach for refining and compressing neural networks [0.03%]
SymRefine：一种用于精炼和压缩神经网络的符号回归方法
Wei Wei,Qiang Lu,Can Huang et al.
Wei Wei et al.
Traditional methods for compressing neural networks (NN), such as pruning and distillation, primarily focus on reducing the size of the NN, often at the expense of accuracy. To overcome this limitation, we propose a novel refining NN method...
Artificial intelligence without restriction surpassing human intelligence with probability one: Theoretical insight into secrets of the brain with AI twins of the brain [0.03%]
不受限制的人工智能必然超越人类智能吗：用人工智能探索大脑秘密及人工大脑理论研究进展
Guang-Bin Huang,M Brandon Westover,Eng-King Tan et al.
Guang-Bin Huang et al.
Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical ...
Blake B Gaines,Chunjiang Zhu,Jinbo Bi
Blake B Gaines
Graph Neural Networks (GNNs) provide state-of-the-art graph learning performance, but their lack of transparency hinders our ability to understand and trust them, ultimately limiting the areas where they can be applied. Many methods exist t...
ShaderNN: A Lightweight and Efficient Inference Engine for Real-time Applications on Mobile GPUs [0.03%]
ShaderNN：一种轻量级且高效的移动GPU实时应用推理引擎
Jing Xie,Yuzhong Yan,Abhishek Saxena et al.
Jing Xie et al.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited...
Comparing multi-class classifier performance by multi-class ROC analysis: A nonparametric approach [0.03%]
非参数多类ROC分析及其在多类分类器性能评估中的应用
Jingyan Xu
Jingyan Xu
The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. Real world applications often require classification into multiple (more than two) classes. For mu...