Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components. Yet, understanding how expertise emerges through joint training of gating mechanisms and experts remains incomplete, especially in scenarios without clear task partitions. Motivated by inference costs and data heterogeneity, we study how joint training of gating functions and experts can dynamically allocate domain-specific expertise across multiple underlying data distributions. As an outcome of our framework, we develop an instance tailored specifically to decentralized training scenarios, introducing Dynamically Decentralized Orchestration of MoEs or DDOME. DDOME leverages heterogeneity emerging from distributional shifts across decentralized data sources to specialize experts dynamically. By integrating a pretrained common expert to inform a gating function, DDOMEachieves personalized expert subset selection on-the-fly, facilitating just-in-time personalization.

Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components. Yet, understanding how expertise emerges through joint training of gating mechanisms and experts remains incomplete, especially in scenarios without clear task partitions. Motivated by inference costs and data heterogeneity, we study how joint training of gating functions and experts can dynamically allocate domain-specific expertise across multiple underlying data distributions. As an outcome of our framework, we develop an instance tailored specifically to decentralized training scenarios, introducing or .

The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain data for joint training to learn across multiple problem scenarios. In point cloud based 3D object detection, however, such multi-domain joint training is highly challenging, because large domain gaps among point clouds from different datasets lead to the severe domain-interference problem. In this paper, we propose OneDet3D, a universal one-for-all model that addresses 3D detection across different domains, including diverse indoor and outdoor scenes, within the same framework and only one set of parameters. We propose the domain-aware partitioning in scatter and context, guided by a routing mechanism, to address the data interference issue, and further incorporate the text modality for a language-guided classification to unify the multi-dataset label spaces and mitigate the category interference issue. The fully sparse structure and anchor-free head further accommodate point clouds with significant scale disparities. Extensive experiments demonstrate the strong universal ability of OneDet3D to utilize only one trained model for addressing almost all 3D object detection tasks (Figure 1).

To address this issue, multi-domain joint training ( i.e., multi-dataset joint training) should be introduced into point cloud based 3D object detection, to allow 3D detectors to learn from point clouds of

Due to limited time, we evaluated SNM [Yin and Neubig, 2017] on Python dataset.5 SNM explicitly introduces the constraints of grammar rules when generating ASTs. The BLEU score for SNM is6 10.62 and similar to our Basic model, indicating that the CG task on this dataset is very challenging. In particular,7 all prediction of SNM is valid, whereas the percentage of valid code generated by the dual model is low (Table 1).8 Since CS and CG models are trained at the same time and the parameters of the36 two models are separate after the joint training, i.e., the two models solve their respective tasks separately after the37 joint training, the number of parameters of each dual model is the same as that of the basic model.

In this paper, we use masked autoencoders for this one-sample learning problem.

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures.

Tothis end, we systematically study the task configurations in different application scenarios and develop a comprehensive Continual Graph Learning Benchmark (CGLB) curated from different public datasets.