Search Results for author:
Found 23 papers, 12 papers with code
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
We propose a new MDR method named EDDA with two key components, i. e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively.
DGI: Easy and Efficient Inference for GNNs
In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution.
Face2Exp: Combating Data Biases for Facial Expression Recognition
To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network.
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths.
ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting
When pre-trained on the big scale datasets and transferred to the medium and small scale datasets, ProSTformer achieves a significant enhancement and behaves best.
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation
Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN.
Vertex-Centric Visual Programming for Graph Neural Networks
Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering.
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database.
DGCL: an efficient communication library for distributed GNN training
Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics.
Elastic Deep Learning in Multi-Tenant GPU Clusters
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
Delay-Tolerant Local SGD for Efficient Distributed Training
The heavy communication for model synchronization is a major bottleneck for scaling up the distributed deep neural network training to many workers.
The item selection problem for user cold-start recommendation
When a new user just signs up on a website, we usually have no information about him/her, i. e. no interaction with items, no user profile and no social links with other users.
Demand Forecasting in Bike-sharing Systems Based on A Multiple Spatiotemporal Fusion Network
Spatial and temporal features are critical for demand forecasting in BSSs, but it is challenging to extract spatiotemporal dynamics.
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs).
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification.
Convolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment.
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error.
Understanding and Improving Proximity Graph based Maximum Inner Product Search
Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items.
PMD: An Optimal Transportation-based User Distance for Recommender Systems
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.
Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including $L_2$-ALSH, Sign-ALSH and Simple-LSH have been proposed.
Norm-Ranging LSH for Maximum Inner Product Search
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee.
Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations
Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead.
