Zobrazeno 1 - 10
of 814
pro vyhledávání: '"Wang, YaQing"'
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. To emulate this human-like rapid learning and enhance alignment and discrimination abilities, we propose ConML, a universal meta-learning framework that can be a
Externí odkaz:
http://arxiv.org/abs/2410.05975
In-context learning (ICL) enables large language models (LLMs) to generalize to new tasks by incorporating a few in-context examples (ICEs) directly in the input, without updating parameters. However, the effectiveness of ICL heavily relies on the se
Externí odkaz:
http://arxiv.org/abs/2410.02203
The local and global well-posedness for the one dimensional fourth-order nonlinear Schr\"odinger equation are established in the modulation space $M^{s}_{2,q}$ for $s\geq \frac12$ and $2\leq q <\infty$. The local result is based on the $U^p-V^p$ spac
Externí odkaz:
http://arxiv.org/abs/2409.11002
The request for fast response and safe operation after natural and man-made disasters in urban environments has spurred the development of robotic systems designed to assist in search and rescue operations within complex rubble sites. Traditional Unm
Externí odkaz:
http://arxiv.org/abs/2409.10000
In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global model training. To address this issue, the concept of model heterogen
Externí odkaz:
http://arxiv.org/abs/2407.19389
In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenu
Externí odkaz:
http://arxiv.org/abs/2407.10112
The scaling law, which involves the brute-force expansion of training datasets and learnable parameters, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sust
Externí odkaz:
http://arxiv.org/abs/2407.00478
Autor:
Zhong, Yuan, Wang, Xiaochen, Wang, Jiaqi, Zhang, Xiaokun, Wang, Yaqing, Huai, Mengdi, Xiao, Cao, Ma, Fenglong
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-th
Externí odkaz:
http://arxiv.org/abs/2406.13942
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of
Externí odkaz:
http://arxiv.org/abs/2402.15700
When legged robots physically interact with obstacles in applications such as search and rescue through rubble and planetary exploration across Martain rocks, even the most advanced ones struggle because they lack a fundamental framework to model the
Externí odkaz:
http://arxiv.org/abs/2401.13062