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09/21/2023
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems
The past decade has seen rapid growth of distributed stream data process...
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08/17/2023
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Graph Active Learning (GAL), which aims to find the most informative nod...
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08/04/2023
VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs
Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
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07/28/2023
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
Graph anomaly detection (GAD) has gained increasing attention in recent ...
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07/05/2023
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
Transformer models have emerged as the leading approach for achieving st...
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06/28/2023
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
Out-of-distribution (OOD) graph generalization are critical for many rea...
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05/27/2023
FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
Due to the recent success of diffusion models, text-to-image generation ...
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04/08/2023
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement
With the increasing data volume, there is a trend of using large-scale p...
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03/06/2023
Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent
Recent years have witnessed the unprecedented achievements of large-scal...
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11/25/2022
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
Transformer models have achieved state-of-the-art performance on various...
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11/01/2022
Efficient Graph Neural Network Inference at Large Scale
Graph neural networks (GNNs) have demonstrated excellent performance in ...
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11/01/2022
Distributed Graph Neural Network Training: A Survey
Graph neural networks (GNNs) are a type of deep learning models that lea...
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09/28/2022
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Contrastive Language-Image Pre-training (CLIP) has been shown to learn v...
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09/27/2022
OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning
Large-scale deep learning models contribute to significant performance i...
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09/02/2022
Diffusion Models: A Comprehensive Survey of Methods and Applications
Diffusion models are a class of deep generative models that have shown i...
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07/29/2022
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates
Vertical federated learning (VFL) is an emerging paradigm that allows di...
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06/16/2022
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data
Due to the rising concerns on privacy protection, how to build machine l...
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06/09/2022
Model Degradation Hinders Deep Graph Neural Networks
Graph Neural Networks (GNNs) have achieved great success in various grap...
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06/04/2022
Instance-wise Prompt Tuning for Pretrained Language Models
Prompt Learning has recently gained great popularity in bridging the gap...
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03/30/2022
An I/O-Efficient Disk-based Graph System for Scalable Second-Order Random Walk of Large Graphs
Random walk is widely used in many graph analysis tasks, especially the ...
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03/28/2022
HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
As giant dense models advance quality but require large-scale expensive ...
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03/25/2022
AutoML for Deep Recommender Systems: A Survey
Recommender systems play a significant role in information filtering and...
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03/02/2022
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Graph Neural Networks (GNNs) have achieved great success in various task...
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12/29/2021
Dense-to-Sparse Gate for Mixture-of-Experts
Mixture-of-experts (MoE) is becoming popular due to its success in impro...
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12/26/2021
K-Core Decomposition on Super Large Graphs with Limited Resources
K-core decomposition is a commonly used metric to analyze graph structur...
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12/26/2021
Efficient Diversity-Driven Ensemble for Deep Neural Networks
The ensemble of deep neural networks has been shown, both theoretically ...
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12/14/2021
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Embedding models have been an effective learning paradigm for high-dimen...
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12/04/2021
PointCLIP: Point Cloud Understanding by CLIP
Recently, zero-shot and few-shot learning via Contrastive Vision-Languag...
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10/28/2021
RIM: Reliable Influence-based Active Learning on Graphs
Message passing is the core of most graph models such as Graph Convoluti...
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08/23/2021
Graph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have recently achieved state-of-the-art per...
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08/02/2021
Evaluating Deep Graph Neural Networks
Graph Neural Networks (GNNs) have already been widely applied in various...
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05/28/2021
CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
To improve user experience and profits of corporations, modern industria...
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02/28/2021
Explore User Neighborhood for Real-time E-commerce Recommendation
Recommender systems play a vital role in modern online services, such as...
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11/04/2020
Graph Neural Networks in Recommender Systems: A Survey
With the explosive growth of online information, recommender systems pla...
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10/27/2020
Contrastive Pre-training for Sequential Recommendation
Sequential recommendation methods play a crucial role in modern recommen...
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10/10/2020
UniNet: Scalable Network Representation Learning with Metropolis-Hastings Sampling
Network representation learning (NRL) technique has been successfully ad...
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07/03/2019
An Experimental Evaluation of Large Scale GBDT Systems
Gradient boosting decision tree (GBDT) is a widely-used machine learning...
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11/06/2018