Zobrazeno 1 - 10
of 49 725
pro vyhledávání: '"ONG, P"'
We introduce a version of probabilistic Kleene algebra with angelic nondeterminism and a corresponding class of automata. Our approach implements semantics via distributions over multisets in order to overcome theoretical barriers arising from the la
Externí odkaz:
http://arxiv.org/abs/2412.06754
Autor:
Xu, Qingshan, Cui, Jiequan, Yi, Xuanyu, Wang, Yuxuan, Zhou, Yuan, Ong, Yew-Soon, Zhang, Hanwang
3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce re
Externí odkaz:
http://arxiv.org/abs/2412.04826
This paper presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs). We begin by discussing the limitations of existing High-Order CBF approaches and how these c
Externí odkaz:
http://arxiv.org/abs/2412.03708
Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, leading to ineffective extraction of user preferences, insufficient inject
Externí odkaz:
http://arxiv.org/abs/2412.07796
Autor:
Jaghouar, Sami, Ong, Jack Min, Basra, Manveer, Obeid, Fares, Straube, Jannik, Keiblinger, Michael, Bakouch, Elie, Atkins, Lucas, Panahi, Maziyar, Goddard, Charles, Ryabinin, Max, Hagemann, Johannes
In this report, we introduce INTELLECT-1, the first 10 billion parameter language model collaboratively trained across the globe, demonstrating that large-scale model training is no longer confined to large corporations but can be achieved through a
Externí odkaz:
http://arxiv.org/abs/2412.01152
Autor:
Ong, Yen Chin
It has been almost 40 years since the proposal of the idea that Hawking radiation of black holes does not lead to a complete evaporation but rather a "remnant" state. Though traditionally viewed with great criticisms especially from the high energy p
Externí odkaz:
http://arxiv.org/abs/2412.00322
The increasing use of Generative Artificial Intelligence (GAI) tools in education highlights the need to understand their influence on individuals' thinking processes and agency. This research explored 20 university students' interaction with GAI dur
Externí odkaz:
http://arxiv.org/abs/2411.19490
The rapid development of AI models has led to a growing emphasis on enhancing their capabilities for complex input data such as videos. While large-scale video datasets have been introduced to support this growth, the unique challenges of reducing re
Externí odkaz:
http://arxiv.org/abs/2412.00111
Autor:
Yoon, Se-eun, Wei, Xiaokai, Jiang, Yexi, Pareek, Rachit, Ong, Frank, Gao, Kevin, McAuley, Julian, Gong, Michelle
In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a
Externí odkaz:
http://arxiv.org/abs/2411.19352
Autor:
Nguyen, Hiep, Tang, Haiyang, Alger, Matthew, Marchal, Antoine, Muller, Eric G. M., Ong, Cheng Soon, McClure-Griffiths, N. M.
Publikováno v:
MNRAS 2024
We introduce TPCNet, a neural network predictor that combines Convolutional and Transformer architectures with Positional encodings, for neutral atomic hydrogen (HI) spectral analysis. Trained on synthetic datasets, our models predict cold neutral ga
Externí odkaz:
http://arxiv.org/abs/2411.13325