102 citations · 547 across the 60 of their papers we have counts for
9 papers · 1 filter
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Qiaoyu Tan, Xin Zhang, Ninghao Liu +5
Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply G…
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan +6
We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the comput…
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Aaron Ferber, Taoan Huang, Daochen Zha +4
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to the…
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
Zirui Liu, Shengyuan Chen, Kaixiong Zhou +3
The training of graph neural networks (GNNs) is extremely time consuming because sparse graph-based operations are hard to be accelerated by hardware. Prior art explores trading of…
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan +3
Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to…
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Daochen Zha, Louis Feng, Bhargav Bhushanam +7
Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely lar…
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Yicheng Wang, Xiaotian Han, Chia-Yuan Chang +3
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation. In…
BED: A Real-Time Object Detection System for Edge Devices
Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang +8
Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume…
Towards Similarity-Aware Time-Series Classification
Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou +1
We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that…