no code implementations • 2 May 2024 • Yipeng Li, Xinchen Lyu
There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients; and sequential FL (SFL), where models are trained in a sequential manner across clients.
no code implementations • 28 Mar 2024 • Yue Gao, Jiaxuan Lu, Siqi Li, Yipeng Li, Shaoyi Du
By treating segments as vertices and constructing hyperedges using rule-based and KNN-based strategies, a multi-view hypergraph neural network that captures relationships across viewpoint and temporal features is established.
2 code implementations • NeurIPS 2023 • Yipeng Li, Xinchen Lyu
There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner.
no code implementations • 3 Feb 2023 • Yipeng Li, Xinchen Lyu
In this paper, we derive the convergence guarantees of Sequential SL (SSL, the vanilla case of SL that conducts the model training in sequence) for strongly/general/non-convex objectives on heterogeneous data.
no code implementations • 23 Jun 2021 • Hanlei Wang, Yipeng Li, Tiantian Jiang
This paper investigates bilateral control of teleoperators with closed architecture and subjected to arbitrary bounded time-varying delay.
no code implementations • ICCV 2021 • Siqi Li, Yutong Feng, Yipeng Li, Yu Jiang, Changqing Zou, Yue Gao
Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera.
no code implementations • 26 Aug 2020 • Tiankuang Zhou, Xing Lin, Jiamin Wu, Yitong Chen, Hao Xie, Yipeng Li, Jintao Fan, Huaqiang Wu, Lu Fang, Qionghai Dai
Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons.
no code implementations • 31 Mar 2020 • Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao, Yue Gao
This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images.
Ranked #13 on 3D Semantic Scene Completion on NYUv2