Zobrazeno 1 - 10
of 733
pro vyhledávání: '"Cao, Xiaofeng"'
Compositional Zero-Shot Learning (CZSL) aims to recognize novel \textit{state-object} compositions by leveraging the shared knowledge of their primitive components. Despite considerable progress, effectively calibrating the bias between semantically
Externí odkaz:
http://arxiv.org/abs/2408.08703
Autor:
Lee, YuXiao, Cao, Xiaofeng
The remarkable achievements of Large Language Models (LLMs) have captivated the attention of both academia and industry, transcending their initial role in dialogue generation. The utilization of LLMs as intermediary agents in various tasks has yield
Externí odkaz:
http://arxiv.org/abs/2405.16766
Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into non-isomorphic substructures and compare them. However, overlooking implicit similarities and topological position information between those substructures limi
Externí odkaz:
http://arxiv.org/abs/2405.05545
Autor:
Rao, Zhijie, Guo, Jingcai, Lu, Xiaocheng, Liang, Jingming, Zhang, Jie, Wang, Haozhao, Wei, Kang, Cao, Xiaofeng
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes,
Externí odkaz:
http://arxiv.org/abs/2404.16348
Machine learning models are susceptible to membership inference attacks (MIAs), which aim to infer whether a sample is in the training set. Existing work utilizes gradient ascent to enlarge the loss variance of training data, alleviating the privacy
Externí odkaz:
http://arxiv.org/abs/2402.05453
In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in offline reinforcement learning. Reward inference is the key to learning
Externí odkaz:
http://arxiv.org/abs/2402.03661
Autor:
Shu, Yangyang, Cao, Xiaofeng, Chen, Qi, Zhang, Bowen, Zhou, Ziqin, Hengel, Anton van den, Liu, Lingqiao
Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. The primary difficulty in this task is that the model's predictio
Externí odkaz:
http://arxiv.org/abs/2402.01157
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO alg
Externí odkaz:
http://arxiv.org/abs/2401.09257
We study the problem of teaching multiple learners simultaneously in the nonparametric iterative teaching setting, where the teacher iteratively provides examples to the learner for accelerating the acquisition of a target concept. This problem is mo
Externí odkaz:
http://arxiv.org/abs/2311.10318
Federated Learning (FL) typically aggregates client model parameters using a weighting approach determined by sample proportions. However, this naive weighting method may lead to unfairness and degradation in model performance due to statistical hete
Externí odkaz:
http://arxiv.org/abs/2311.05936