Search Results for author:
Found 7 papers, 4 papers with code
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Time-based Sequence Model for Personalization and Recommendation Systems
In this paper we develop a novel recommendation model that explicitly incorporates time information.
Post-Training 4-bit Quantization on Embedding Tables
Continuous representations have been widely adopted in recommender systems where a large number of entities are represented using embedding vectors.
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in the data center.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
