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Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs
Externí odkaz:
http://arxiv.org/abs/2411.09403
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This techn
Externí odkaz:
http://arxiv.org/abs/2411.08552
Autor:
Thakkar, Megh, More, Yash, Fournier, Quentin, Riemer, Matthew, Chen, Pin-Yu, Zouaq, Amal, Das, Payel, Chandar, Sarath
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the
Externí odkaz:
http://arxiv.org/abs/2411.06824
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Autor:
Loh, Jia Quan, Luo, Xuewen, Ding, Fan, Tew, Hwa Hui, Loo, Junn Yong, Ding, Ze Yang, Susilawati, Susilawati, Tan, Chee Pin
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailor
Externí odkaz:
http://arxiv.org/abs/2411.06087
Autor:
Sun, Lingfeng, Wang, Yixiao, Hung, Pin-Yun, Wang, Changhao, Zhang, Xiang, Xu, Zhuo, Tomizuka, Masayoshi
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or
Externí odkaz:
http://arxiv.org/abs/2411.03669
We study the problem of estimating the body movements of a camera wearer from egocentric videos. Current methods for ego-body pose estimation rely on temporally dense sensor data, such as IMU measurements from spatially sparse body parts like the hea
Externí odkaz:
http://arxiv.org/abs/2411.03561
Large Language Models (LLMs) are increasingly used to control robotic systems such as drones, but their risks of causing physical threats and harm in real-world applications remain unexplored. Our study addresses the critical gap in evaluating LLM ph
Externí odkaz:
http://arxiv.org/abs/2411.02317
Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks, where malicious inputs manipulate the model into ignoring original instructions and executing designated action. In this paper, we inv
Externí odkaz:
http://arxiv.org/abs/2411.00348
State-of-the-art (SOTA) semi-supervised learning techniques, such as FixMatch and it's variants, have demonstrated impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.22124
Autor:
Duan, Ting-Ting, Yang, Pei-Pin, Zhang, Peng-Cheng, Lao, Hai-Ling, Liu, Fu-Hu, Olimov, Khusniddin K.
This study investigates the transverse momentum ($p_T$) spectra of identified light charged hadrons produced in gold--gold (Au+Au) collisions across various centrality classes at center-of-mass energies per nucleon pair, $\sqrt{s_{NN}}$, ranging from
Externí odkaz:
http://arxiv.org/abs/2410.19266