42 citations · 54 across the 5 of their papers we have counts for
6 papers
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
Jincheng Zhou, Yucheng Zhang, Jianfei Gao +2
The task of fully inductive link prediction in knowledge graphs has gained significant attention, with various graph neural networks being proposed to address it. This task present…
Veritas: Answering Causal Queries from Video Streaming Traces
Chandan Bothra, Jianfei Gao, Sanjay Rao +1
In this paper, we seek to answer what-if questions - i.e., given recorded data of an existing deployed networked system, what would be the performance impact if we changed the desi…
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Weihan Cao, Yifan Zhang, Jianfei Gao +3
Knowledge distillation(KD) is a widely-used technique to train compact models in object detection. However, there is still a lack of study on how to distill between heterogeneous d…
On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions
Jianfei Gao, Bruno Ribeiro
This work formalizes the associational task of predicting node attribute evolution in temporal graphs from the perspective of learning equivariant representations. We show that nod…
Channel-wise Knowledge Distillation for Dense Prediction
Changyong Shu, Yifan Liu, Jianfei Gao +2
Knowledge distillation (KD) has been proven to be a simple and effective tool for training compact models. Almost all KD variants for dense prediction tasks align the student and t…
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations
Jianfei Gao, Mohamed A. Zahran, Amit Sheoran +2
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggrega…