328 citations · 1.2k across the 57 of their papers we have counts for
14 papers · 1 filter
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Samuel Wiggins, Yuan Meng, Rajgopal Kannan +1
Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing adva…
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Hongkuan Zhou, Da Zheng, Xiang Song +2
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications.…
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Online interactions with the environment to collect data samples for training a Reinforcement Learning (RL) agent is not always feasible due to economic and safety concerns. The go…
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Reinforcement Learning (RL) has achieved significant success in application domains such as robotics, games and health care. However, training RL agents is very time consuming. Cur…
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings
Hongkuan Zhou, James Orme-Rogers, Rajgopal Kannan +1
Temporal Knowledge Graphs store events in the form of subjects, relations, objects, and timestamps which are often represented by dynamic heterogeneous graphs. Event forecasting is…
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng +2
Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inferen…
The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance
Ajitesh Srivastava, Tianjian Xu, Viktor K. Prasanna
During the COVID-19 pandemic, a significant effort has gone into developing ML-driven epidemic forecasting techniques. However, benchmarks do not exist to claim if a new AI/ML tech…
Deep Graph Neural Networks with Shallow Subgraph Samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to thre…
Accurate, Efficient and Scalable Training of Graph Neural Networks
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform tr…
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Model usage is the central challenge of model-based reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step predic…
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer…
Accurate, Efficient and Scalable Graph Embedding
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge…
Holistic Measures for Evaluating Prediction Models in Smart Grids
Saima Aman, Yogesh Simmhan, Viktor K. Prasanna
The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. Howe…