13 accepted papers
Federated Learning (FL) enables collaborative model training without sharing raw data, but client data are often Non-Independent and Identically Distributed (Non-IID), which often slow convergence and degrade global performance. Meanwhile, privacy preservation is also a critical concern in FL. To ad
3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models for high‑fidelity reconstruction. However, their multi-step iterative sampling incurs significant computational overhead,
The KV cache in self-attention has emerged as a major bottleneck in long-context and large-batch inference for LLMs. Existing approaches often treat sparsity prediction and compression as separate modules—relying on auxiliary index structures to select relevant tokens, and on complex quantization sc
Ranking of vertices in a graph for different objectives is one of the most fundamental tasks in computer science. It is known that traditional ranking algorithms can generate unbalanced ranking when the graph has underlying communities, resulting in loss of information, polarised opinions, and reduc…
Entity alignment has emerged as a powerful technique for integrating knowledge graphs, facilitating the fusion of heterogeneous knowledge into a unified graph. The state-of-the-art methods combine both graph structures and side information for effective entity alignment. However, they neglect low-qu…
Vision-based semantic scene completion (SSC) is able to predict complex scene information from limited 2D images, which has attracted widespread attention. Currently, SSC methods typically construct unified voxel features containing both geometry and semantics, which lead to different depth position…
As the network data in real life become multi-modal and multi-relational, multi-view attributed graphs have garnered significant attention. Numerous methods have achieved excellent performance in multi-view attributed graph clustering; however, they cannot efficiently handle incomplete attribute sce
With the advancement of deep learning, network communication has become the most critical factor in model training. Especially, the all-reduce operation can comprise over 70% of the cumulative training duration as a pivotal component within data parallelism. However, existing all-reduce algorithms o…
Principal component analysis (PCA) is one of the most fundamental tools in machine learning with broad use as a dimensionality reduction and denoising tool. In the later setting, while PCA is known to be effective at subspace recovery and is proven to aid clustering algorithms in some specific setti…
In prior studies, domain adversarial neural networks (DANNs) are used to align image-level features regardless of foreground and background. However, the conventional discriminator in DANNs may leads the feature extractor to disregard cross-domain features rather than aligning them. This phenomenon…
The accurate segmentation of the right and left ventricles with limited labeled data is a challenging task in echocardiographic data analysis. To fully leverage the easily accessible unlabeled data, we propose a label-decoupling teacher-student framework (LDTSF) based on semi-supervised learning. Sp…
A popular heuristic method for improving clustering results is to apply dimensionality reduction before running clustering algorithms. It has been observed that spectral-based dimensionality reduction tools, such as PCA or SVD, improve the performance of clustering algorithms in many applications. T…
The stochastic block model (SBM) is a fundamental model for studying graph clustering or community detection in networks. It has received great attention in the last decade and the balanced case, i.e., assuming all clusters have large size, has been well studied. However, our understanding of SBM w…