57 citations · 253 across the 32 of their papers we have counts for
4 papers · 1 filter
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Xupeng Miao, Hailin Zhang, Yining Shi +4
Embedding models have been an effective learning paradigm for high-dimensional data. However, one open issue of embedding models is that their representations (latent factors) ofte…
PointCLIP: Point Cloud Understanding by CLIP
Renrui Zhang, Ziyu Guo, Wei Zhang +6
Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match i…
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Xiaonan Nie, Xupeng Miao, Shijie Cao +7
Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i…
ROD: Reception-aware Online Distillation for Sparse Graphs
Wentao Zhang, Yuezihan Jiang, Yang Li +6
Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance…