Zobrazeno 1 - 10
of 2 534
pro vyhledávání: '"I.2"'
This chapter delves into the emerging field of neuro-symbolic query optimization for knowledge graphs (KGs), presenting a comprehensive exploration of how neural and symbolic techniques can be integrated to enhance query processing. Traditional query
Externí odkaz:
http://arxiv.org/abs/2411.14277
Falls among seniors due to difficulties with tasks such as picking up objects pose significant health and safety risks, impacting quality of life and independence. Reliable, accessible assessment tools are critical for early intervention but often re
Externí odkaz:
http://arxiv.org/abs/2411.12689
Autor:
Norelli, Antonio
Rooted in the explosion of deep learning over the past decade, this thesis spans from AlphaGo to ChatGPT to empirically examine the fundamental concepts needed to realize the vision of an artificial scientist: a machine with the capacity to autonomou
Externí odkaz:
http://arxiv.org/abs/2411.11672
Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with
Externí odkaz:
http://arxiv.org/abs/2411.12070
Autor:
Gupta, Arushi, Kocielnik, Rafal, Wang, Jiayun, Nasriddinov, Firdavs, Yang, Cherine, Wong, Elyssa, Anandkumar, Anima, Hung, Andrew
During surgical training, real-time feedback from trainers to trainees is important for preventing errors and enhancing long-term skill acquisition. Accurately predicting the effectiveness of this feedback, specifically whether it leads to a change i
Externí odkaz:
http://arxiv.org/abs/2411.10919
Accurate and rapid prediction of flow-fields is crucial for aerodynamic design. This work proposes a discontinuous Galerkin method (DGM) whose performance enhances with increasing data, for rapid simulation of transonic flow around airfoils under var
Externí odkaz:
http://arxiv.org/abs/2411.09351
Autor:
Gao, Yingqi, Liu, Yifu, Li, Xiaoxia, Shi, Xiaorong, Zhu, Yin, Wang, Yiming, Li, Shiqi, Li, Wei, Hong, Yuntao, Luo, Zhiling, Gao, Jinyang, Mou, Liyu, Li, Yu
To tackle the challenges of large language model performance in natural language to SQL tasks, we introduce XiYan-SQL, an innovative framework that employs a multi-generator ensemble strategy to improve candidate generation. We introduce M-Schema, a
Externí odkaz:
http://arxiv.org/abs/2411.08599
Back propagation (BP) is the default solution for gradient computation in neural network training. However, implementing BP-based training on various edge devices such as FPGA, microcontrollers (MCUs), and analog computing platforms face multiple maj
Externí odkaz:
http://arxiv.org/abs/2411.05873
Autor:
Haensch, W.
Neuromorphic computing with crossbar arrays has emerged as a promising alternative to improve computing efficiency for machine learning. Previous work has focused on implementing crossbar arrays to perform basic mathematical operations. However, in t
Externí odkaz:
http://arxiv.org/abs/2411.04814
Autor:
Hossain, Soaad, Rasalingam, James, Waheed, Arhum, Awil, Fatah, Kandiah, Rachel, Ahmed, Syed Ishtiaque
With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that ther
Externí odkaz:
http://arxiv.org/abs/2411.05856