1 citations · 1 across the 3 of their papers we have counts for
8 papers
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Yuan Zhang, Xiaoran Xu, Hanning Zhou +1
Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and fl…
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang +3
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an…
Neural Consciousness Flow
Xiaoran Xu, Wei Feng, Zhiqing Sun +1
The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have…
Modeling Attention Flow on Graphs
Xiaoran Xu, Songpeng Zu, Chengliang Gao +2
Real-world scenarios demand reasoning about process, more than final outcome prediction, to discover latent causal chains and better understand complex systems. It requires the lea…
ShareJIT: JIT Code Cache Sharing across Processes and Its Practical Implementation
Xiaoran Xu, Keith Cooper, Jacob Brock +2
Just-in-time (JIT) compilation coupled with code caching are widely used to improve performance in dynamic programming language implementations. These code caches, along with the a…
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
Xiaoran Xu, Songpeng Zu, Yuan Zhang +2
In real-world scenarios, it is appealing to learn a model carrying out stochastic operations internally, known as stochastic computation graphs (SCGs), rather than learning a deter…