8 citations · 25 across the 7 of their papers we have counts for
9 papers
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction
Congcong Liu, Yuejiang Li, Jian Zhu +4
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from…
Dynamic Parameterized Network for CTR Prediction
Jian Zhu, Congcong Liu, Pei Wang +6
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems. Most existing CTR prediction…
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
Xiaodong Mei, Yuxiang Sun, Yuying Chen +2
In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we pro…
AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
Congcong Liu, Yuying Chen, Ming Liu +1
Pedestrian trajectory prediction is a critical yet challenging task, especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of di…
CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
Yuying Chen, Congcong Liu, Bertram Shi +1
Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving. Modeling social interactions is of great importance for accurate group-w…
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
Yuying Chen, Congcong Liu, Ming Liu +1
Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficie…