Zobrazeno 1 - 10
of 678
pro vyhledávání: '"liu, Peilin"'
Recently, self-supervised learning (SSL) has been extensively studied. Theoretically, mutual information maximization (MIM) is an optimal criterion for SSL, with a strong theoretical foundation in information theory. However, it is difficult to direc
Externí odkaz:
http://arxiv.org/abs/2409.04747
On-device computing, or edge computing, is becoming increasingly important for remote sensing, particularly in applications like deep network-based perception on on-orbit satellites and unmanned aerial vehicles (UAVs). In these scenarios, two brain-l
Externí odkaz:
http://arxiv.org/abs/2409.02146
In deep reinforcement learning applications, maximizing discounted reward is often employed instead of maximizing total reward to ensure the convergence and stability of algorithms, even though the performance metric for evaluating the policy remains
Externí odkaz:
http://arxiv.org/abs/2407.13279
Autor:
Ma, Yang, Wang, Dongang, Liu, Peilin, Masters, Lynette, Barnett, Michael, Cai, Weidong, Wang, Chenyu
The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreover, the limited availability of well-annotated datasets constrains th
Externí odkaz:
http://arxiv.org/abs/2407.08948
Semi-gradient Q-learning is applied in many fields, but due to the absence of an explicit loss function, studying its dynamics and implicit bias in the parameter space is challenging. This paper introduces the Fokker--Planck equation and employs part
Externí odkaz:
http://arxiv.org/abs/2406.08148
Autor:
Cao, Boxi, Lu, Keming, Lu, Xinyu, Chen, Jiawei, Ren, Mengjie, Xiang, Hao, Liu, Peilin, Lu, Yaojie, He, Ben, Han, Xianpei, Sun, Le, Lin, Hongyu, Yu, Bowen
Alignment is the most critical step in building large language models (LLMs) that meet human needs. With the rapid development of LLMs gradually surpassing human capabilities, traditional alignment methods based on human-annotation are increasingly u
Externí odkaz:
http://arxiv.org/abs/2406.01252
Autor:
Wang, Dongang, Liu, Peilin, Wang, Hengrui, Beadnall, Heidi, Kyle, Kain, Ly, Linda, Cabezas, Mariano, Zhan, Geng, Sullivan, Ryan, Cai, Weidong, Ouyang, Wanli, Calamante, Fernando, Barnett, Michael, Wang, Chenyu
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analysis tasks, where the cost associated with image acquisition and annota
Externí odkaz:
http://arxiv.org/abs/2404.03451
Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improv
Externí odkaz:
http://arxiv.org/abs/2307.09801
Autor:
Bian, Ning, Lin, Hongyu, Liu, Peilin, Lu, Yaojie, Zhang, Chunkang, He, Ben, Han, Xianpei, Sun, Le
Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs), which suggests their potential significance as agents in the society.
Externí odkaz:
http://arxiv.org/abs/2305.04812
Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layo
Externí odkaz:
http://arxiv.org/abs/2304.05146